A data-driven hierarchical control strategy is proposed to address the leader-following consensus problem for nonlinear networked multi-agent systems (MASs) with unknown dynamics under Byzantine attacks. The proposed hierarchical framework decouples the leader-following consensus control into leader state estimation and trajectory tracking, only using input/output data of systems. Specifically, a discrete-time distributed observer is first proposed to securely estimate the leader state utilizing the mean-subsequence-reduced-falgorithm, which effectively filters out malicious data from Byzantine agents in networked MASs. Then, a data-driven decentralized controller is developed to track the estimated leader state. In addition, we extend this strategy into a dynamic event-triggered data-driven hierarchical control algorithm, which not only securely achieves the leader-following consensus but also conserves network communication resources. Finally, simulation results demonstrate the effectiveness of the proposed methods.

This paper investigates the problem of a robust diffusion Kalman filter in a nonlinear system within multi-sensor networks. To address the challenge of model mismatch caused by unmodeled dynamics or external disturbances, a novel robust diffusion Kalman filter (RDKF) algorithm is proposed. Conventional DKF algorithms typically suffer from significant performance degradation when system models deviate from actual dynamics, due to their reliance on accurate model assumptions. To overcome this limitation, a model mismatch compensation mechanism is incorporated into the proposed RDKF, which actively corrects estimation failures induced by inaccurate models. Furthermore, to release the additional network communication burden introduced by the compensation mechanism, two selective neighbor communication strategies are developed, which are the Round-Robin-inspired (RR-inspired) periodic selection strategy and the optimal selection strategy preserving estimation quality. In addition, the theoretical analysis about the mean and mean-square stability is established, and the equivalence between local and global estimation stability is demonstrated, which guarantees the effectiveness of the designed global compensation mechanism for both global and local estimates. Finally, two simulation examples on target tracking are presented to demonstrate the performance comparison between the proposed RDKFs and the conventional DKF approaches in both linear and nonlinear systems.

In this paper, we focus on the control Lyapunov function (CLF) for a class of neural networks, both with and without disturbance input. First, we design an exponential controller using the quadratic program-based CLF (QP-CLF) method to address the drive-response synchronization of a class of neural networks. Second, we propose a robust controller based on the robust QP-CLF approach to ensure the input-to-state stability (ISS) of the closed-loop system, even in the presence of external disturbances or system uncertainties. Finally, we present two numerical examples to demonstrate the effectiveness of the proposed QP-CLF and robust QP-CLF methods, highlighting their capability to maintain stability and synchronization in both ideal and disturbed conditions. These examples provide valuable insights into the practical applicability of the proposed control strategies.

This work discusses the consensus tracking for stochastic multi-agent systems with a leader-follower structure. An observer-based distributed control approach is put forward, leveraging control Lyapunov functions and quadratic programming framework. This control method ensures that each follower can track with the leader's state in the sense of expectation, even under stochastic disturbances. Two observers are given for each follower to observe its own state and the leader's state information, respectively, enabling fully distributed control requirement. Theoretical analysis guarantees the convergence of this control approach, and simulation results validate its effectiveness in achieving consensus tracking.

This paper investigates the robust state estimation problem for non-cooperative target tracking to overcome the limitations of conventional tracking algorithms under model uncertainty and noise uncertainty. Although some existing algorithms employ the maximum correntropy criterion (MCC) to handle non-Gaussian noise, most of them generally assume precise state-space models and neglect the effect of uncertain noise parameters as well. To deal with this limitation, a robust fuzzy adaptive MCC-based unscented Kalman filter (UKF) algorithm is proposed, which enhances robustness through the simultaneous state and unknown input estimation (SSUIE), and adjusts the noise covariance via a fuzzy inference system (FIS) adaptively. First, an unknown input (UI) estimator is integrated into the MCC-based UKF to tackle the challenge of model uncertainty, thereby eliminating the reliance on a precise state-space model. Then, an FIS is employed to adjust noise covariance, which explicitly accounts for uncertain noise statistics, improving adaptability in challenging conditions. Notably, the FIS-based adjustment is a more efficient and flexible method compared to the interacting multiple model (IMM) and variational Bayesian (VB) methods, as it avoids the need for precise prior knowledge and high computational complexity. Finally, the simulation results demonstrate that our proposed algorithm can enable effective tracking of non-cooperative targets, even during orbital maneuvers and under uncertain noise conditions. This work provides an adaptive solution for enhancing estimation robustness against uncertain target behaviors and dynamic environments, with potential applications in autonomous systems and security defense systems.

This article focuses on the attack-tolerant and fault-tolerant cooperative fuzzy security control problem for nonlinear delayed Markov jump distributed parameter systems (DPSs) under the stochastic sampling that randomly switches between two sampling periods. First, a Takagi-Sugeno (T-S) fuzzy partial differential equation (PDE) model is presented to accurately describe the nonlinear delayed Markov jump DPSs. Subsequently, in consideration of possible random deception attacks and actuator faults, an attack-tolerant and fault-tolerant cooperative fuzzy security control approach with stochastic sampling is proposed under spatially point measurements (SPMs). Then, linear matrix inequality (LMI)-based sufficient conditions that guarantee the stochastically mean-square exponential stability of closed-loop nonlinear delayed Markov jump DPSs are obtained by employing a mode-dependent Lyapunov functional (LF). Lastly, two examples are given to illustrate the effectiveness of the presented control scheme.

In recent years, with the wide application of artificial neural networks (ANNs) in the field of artificial intelligence, the study of neural network bifurcation dynamics has received much attention and has achieved a large number of results, but the current research only focuses on the binary interactions between neurons and does not take into account the higher-order interactions (HOIs) that exist between neurons. Furthermore, most neural network models focus on ring, star and chain structures, but it is more practical to study multi-layer neural networks. For this reason, this paper develops a three-layer neural network with multiple time delays under HOIs. Firstly, the characteristic equation of network is obtained, and the time delay is selected as the bifurcation parameter. Subsequently, the stability of the neural network and the sufficient condition for the occurrence of Hopf bifurcation are established. Then, the correctness of the theoretical results is verified through numerical simulations. The simulation results demonstrate that the increase of the time delay leads to Hopf bifurcation, which consequently leads to the system oscillation and instability. In addition, the stability of the network is closely related to the higher-order coupling coefficient, self-feedback coefficient and unified connection weight. An increase in the higher-order coupling coefficient expands the stability domain of the network, an increase in the self-feedback coefficient enlarges the stability domain of the network, and an increase in the unified connection weight narrows the stability domain of the network. Finally, our model performs well in terms of function fitting, which also provides some insights into the subsequent modelling and analysis of higher-order neural networks.

This paper investigates the distributed set-membership filtering for a class of discrete systems over wireless sensor networks. A new type of coding-decoding method with mixed quantizer is proposed to balance the upper bound of decoding error and the communication burden. Then, a distributed filter is designed based on decoding measurements, and a recursive technique is utilized to ascertain sufficient conditions of the feasibility of the proposed filtering design. A fusion rule is further presented to achieve a more compact effective region by determining the intersection of the local estimation and prediction ellipsoids. A fusion estimate is then derived by addressing two optimization problems, which outperforms all local estimates. Finally, a simulation example is introduced to verify the effectiveness of the developed filtering algorithm.

This study investigates the mean-square reachable set (RS) consensus of nonlinear Markov-switched multiagent systems (MASs) with time-varying delays, in which a multirate sampled-data consensus (MRSDC) control scheme is designed for the first time under general uncertain semi-Markov transition (GUST) switched topologies. First, the nonlinear Markov-switched MAS is transformed into quasilinear subsystems by applying the Takagi-Sugeno (T-S) fuzzy modeling technique, where the GUST-based Markov model characterizes both the operation mode and abrupt variations in the communication network topologies among all agents. Second, an aperiodic MRSDC control strategy is developed to reduce the sampling frequency of certain sensors below the single-rate threshold by adaptively adjusting their sampling rates, thereby enhancing flexibility and improving consensus performance. Furthermore, a new free-weighting integral inequality is introduced to handle the integral quadratic term involving time-varying delay bounds. Subsequently, an appropriate looped-side Lyapunov functional is designed, leveraging aperiodic multirate sampling and time-varying delay characteristics. Next, by combining the constructed Lyapunov functional with the proposed integral inequality and an improved reciprocally convex combination inequality, sufficient conditions are derived in the form of linear matrix inequalities (LMIs). These conditions not only ensure the mean-square leaderless consensus of the resulting MASs but also guarantee that all reachable states remain confined within ellipsoidal attracting-like regions under the MRSDC scheme. Finally, numerical validations are conducted to demonstrate the effectiveness of the proposed MRSDC control strategies using interconnected single-link robot arm systems (SLRASs), while a comparative numerical example further illustrates the superiority of the proposed method.

The study of dynamics in complex systems has increasingly incorporated higher order interactions, which capture the collective influence among three or more units, extending beyond traditional pairwise connections. Although such interactions are observed in biological neural networks, their precise role in shaping network dynamics and the feasibility of controlling these dynamics remain unclear. This article proposes a controlled diffusion hub neural network model that explicitly includes higher order interactions. To regulate the resulting spatiotemporal dynamics, a cross-node associated delayed feedback control (CNADFC) method is further introduced. Our analysis establishes conditions for local stability, Turing instability, and Hopf bifurcation. We show that while Turing instability cannot arise, spatially periodic patterns emerge under specific parametric conditions. Numerical simulations confirm these theoretical findings and highlight the pronounced effects of self-feedback, control, and first-order interaction on stability and dynamic behaviors; in contrast, higher order interactions exert a comparatively modest influence. Furthermore, simulations illustrate how the CNADFC method can effectively optimize spatiotemporal dynamics. This work advances the understanding of diffusion neural network behavior under complex higher order interaction and provides a reference for the effective control of such networks.

In this article, the evolution of social power is studied within a unified framework comprising two classes of individuals: oblivious individuals and stubborn individuals, whose opinion dynamics are described by the DeGroot averaging model and the Friedkin-Johnsen model, respectively. A proper subset of the simplex is identified to ensure the well-posedness of social power, and it is demonstrated that the corresponding opinion dynamics is convergent for each issue by restricting the initial social power to this proper subset. Through the reflected appraisal mechanism, a nonlinear mapping governing the social power evolution together with its invariant set is derived, and some sufficient conditions with linear time complexity for the convergence of social power are established by proving that this nonlinear mapping is contractive on the invariant set. Furthermore, for the final social power, it is found that both autocratic and democratic social power cannot be achieved during the evolution, and the average social power of oblivious individuals is larger than that of stubborn individuals, indicating that the network topology has a greater impact on social power than individual stubbornness. In addition, it is observed that the final social power ranking of oblivious individuals is consistent with their centrality ranking, and a rigorous lower bound on the final social power is derived for each stubborn individual. Finally, a numerical example is provided to demonstrate the correctness of the theoretical analysis.

In this article, we investigate a decentralized constrained optimization problem over time-varying directed networks. The nodes in the network aim to collaboratively minimize the aggregate of all locally known convex cost functions, subject to local nonidentical and multiple constraint sets, inequality constraints, and equality constraints. Problems of this nature arise in a number of applications in real networks, such as facility location in wireless sensor networks and image deblurring in machine learning. To address these types of problems, we propose an efficient subgradient-rescaling-based decentralized fixed-random projection (SR-DFRP) algorithm, named the SR-DFRP algorithm. In particular, the SR-DFRP algorithm employs Polyak's random projection to handle nonidentical and multiple constraints, which reduces computational load by avoiding the formulation of complex subproblems. Furthermore, by utilizing dynamically constructed row-stochastic matrices, the algorithm employs a subgradient rescaling strategy to mitigate the imbalance induced by the time-varying directed networks. Rigorous theoretical analyses are provided to establish that the SR-DFRP algorithm converges almost surely to the optimal solution. Extensive simulations on facility location and image deblurring problems are presented to validate the efficacy of the algorithm and the validity of the theoretical results.

This work reports design problem of the memory sampled-data (SD) controller for interval type-2 fuzzy singular systems (SSs) with external disturbances. First, an improved free-weighting matrix inequality is introduced for concerning fuzzy SSs to reduce conservatism of the integral terms. Then, a novel looped-functional-based Lyapunov-Krasovskii functional (LKF) is constructed that incorporates the data from sampling interval $\mathbf {z}(t)$ to $\mathbf {z}(t_{k})$ . With the help of improved integral inequality and novel LKF, a new set of admissibility conditions is developed in the form of linear matrix inequalities (LMIs). The developed criteria based on the memory SD controller ensure that the proposed systems is admissible with an $H_{\infty }$ attenuation level. Finally, numerical simulations are given to illustrate the usefulness and benefit of the proposed methods.

This article addresses the issue of output synchronization via intermittent dynamic event-triggered sampled-data (IDETSD) security control of reaction-diffusion neural networks (RDNNs) under spatially local averaged measurements (SLAMs) subject to both delays and random deception attacks, where a Bernoulli distribution is utilized to describe whether channels suffer from the cyberattacks. An IDETSD security control method under SLAMs and random deception attacks is presented to achieve the output synchronization of delayed RDNNs. Compared with time-triggered intermittent sampled-data (SD) control strategies, a dynamic event-triggered (ET) mechanism to more effectively mitigate the impact induced by random deception attacks that intentionally tamper with the state transmission signals from sensors to controllers is introduced in this article. Moreover, new output synchronization criteria are established by applying an ET-dependent switched Lyapunov functional (LF) and inequality techniques. Then, the desired IDETSD controller is obtained by solving linear matrix inequalities (LMIs). To validate the efficacy of the proposed approach, simulation outcomes from two numerical studies are presented.

This article focuses on the fixed-time (FXT) and preassigned-time (PAT) bipartite output regulation of heterogeneous linear multiagent systems (MASs), where both cooperative and adversarial interactions among neighboring agents are considered under a signed graph framework. Since the exosystem information may be unavailable to the agents, an FXT distributed observer and an FXT adaptive distributed observer are designed to accurately identify the exosystem's coefficient matrix and state, respectively. Subsequently, in the absence of the stabilizability and detectability of coefficient matrices, distributed state- and output-feedback control protocols are designed to ensure FXT bipartite output regulation. Besides, for a preset convergence time, the bipartite output regulation is explored by developing control protocols incorporating distributed PAT observers and controllers. Finally, the theoretical results are applied to warehouse automation robot systems.

Control barrier functions (CBFs) provide a rigorous framework for enforcing safety in control-affine systems by ensuring system states remain within predefined safe sets. However, practical deployment faces fundamental challenges that limit real-world applicability. This review analyzes recent progress in CBF methodologies across three interconnected domains: uncertainty handling, structural optimization, and feasibility assurance. For uncertainty, we distinguish strategies tailored to unknown dynamics, modeling discrepancies, and dynamic environments, spanning robust theoretical methods and learning-based approaches. For structural design, we examine class- $\mathcal {K}$ function selection, parameter tuning, and advanced modifications that jointly address conservatism and feasibility. For feasibility, we identify the root causes of CBF-QP infeasibility and survey solution strategies, including constraint relaxation, structural redesign, and mathematical guarantees. By synthesizing these directions into a unified framework, this review highlights key interdependencies and outlines future research opportunities for advancing CBF-based safety-critical control in robotics, autonomous systems, and beyond.

This work investigates the oriented-covert attacks and corresponding defense strategies on an unmanned aerial vehicle (UAV) equipped with a GPS sensor and an Ultra-WideBand sensor in single and double base-station scenarios. The attacker drives the UAV away from its nominal path and causes a high-velocity collision while remaining stealthy to base station detection. The attack is formulated as a constrained optimal control problem that trades off terminal deviation and impact velocity, subject to oriented-covert constraints that decompose the control input into detectable and undetectable components. To defend against the above attacks, the defender should enable the UAV to avoid collisions while reaching its nominal destination with minimal energy consumption. Essentially, the attacker–defender interaction is modeled as a Stackelberg game with the defender as the leader and the attacker as the follower. The existence and sensitivity of the game equilibrium are analyzed. Moreover, when the defender adopts the optimal strategy in the sense of a Nash equilibrium, the above game reduces to a unilateral defense optimization problem that aims to compute the optimal control inputs that minimize terminal deviation, impact velocity, and energy consumption simultaneously. Pontryagin’s Maximum Principle is used to derive the optimality conditions and theoretically validate the proposed attack and defense strategies. Finally, the effectiveness and practicality of the proposed attack and defense strategies are demonstrated through two simulation examples and one experiment.

The field of low-tubal-rank tensor recovery, especially with subspace prior information, has recently garnered significant attention. However, existing methods encounter limitations when dealing with tensor data affected by simultaneous damage and loss. Moreover, they frequently necessitate clean (with no outliers) data to generate subspace prior information, which presents practical challenges. Addressing these issues, this article proposes a generalized subspace coupling (GSC) scheme, equipped with a novel tool to quantify the accuracy of the prior subspace. Building upon this foundation, we delve into the robust low-tubal-rank tensor completion problem, aiming to recover a low-tubal-rank tensor from partially observed data corrupted by sparse noise. Importantly, we theoretically demonstrate that the proposed method achieves exact tensor recovery under significantly weaker incoherence conditions compared to those previously suggested. Additionally, to optimize the proposed model, we design a symmetric Gauss-Seidel-based alternating direction method of multipliers (sGS-ADMM) with guaranteed convergence. Experiments conducted on various datasets, including facial images, medical scans, and video sequences, validate the superiority of our model over existing competitors in both qualitative and quantitative assessments.

The main objective of this study is to develop a fault-tolerant fuzzy time-constrained memory-sampled-data control (TCMSDC) mechanism for analyzing the consensus performance of nonlinear multiagent systems (MASs) affected by stochastic actuator faults and external disturbances. To achieve this, the nonlinear MASs are first converted into quasi-linear subsystems using the Takagi–Sugeno (T-S) fuzzy model. In contrast to existing memory-sampled-data consensus methods, the constructed TCMSDC signals vary over time within each sampling period, thereby enhancing consensus performance. Thereafter, a Markov variable process is applied to model multiple stochastic actuator faults in the considered MASs. Furthermore, an aperiodic-sampling-dependent asymmetric dual-looped functional (ASADLF) is constructed, which incorporates different sets of matrices designed for each sampling instance. Subsequently, a new nonorthogonal polynomial integral inequality (NOPII) is introduced to approximate the integral quadratic terms. By leveraging this ASADLF along with the proposed NOPII technique, less conservative consensus criteria are derived in the form of linear matrix inequalities (LMIs), and the TCMSDC gain parameters are obtained to guarantee mean-square asymptotic consensus with$H_{\infty }$performance for the considered MASs. Finally, the effectiveness and advantages of the proposed TCMSDC approach in enhancing fault tolerance and achieving reliable consensus under stochastic actuator faults are validated through numerical simulations on multi-ship steering autonomous surface vehicles (SSASVs) and mass–spring systems.

This article proposes a novel adaptive predefined-time control scheme that guarantees global prescribed performance and exact tracking for a class of uncertain nonlinear systems characterized by nonstrict feedback dynamics, actuator faults, external disturbances, and sensor faults. Anovel switching feedback gain method is introduced to simultaneously handle unknown nonlinearities, disturbances, and actuator faults, while a tan-type error transformation and a new Lyapunov-like energy function are designed to ensure that all signals in the closed-loop systems remain globally bounded without requiring knowledge of control coefficients. In contrast to existing finite-time or asymptotic tracking control strategies, the proposed method ensures exact convergence to zero tracking error within a predefined time—independent of initial conditions—while strictly maintaining prescribed transient performance bounds. Simulation results validate the effectiveness of the proposed approach and its superiority over conventional methods, demonstrating faster convergence and smaller overshoot, thereby establishing a new benchmark for high-precision control of nonstrict feedback systems.

The operational optimization of integrated energy systems with high renewable energy penetration (IES-HREP) constitutes a complex systems engineering problem, primarily due to the absence of a well-defined cost model for renewable energy sources (RESs) generation and uncertainties affecting energy quality. To address these issues, this article proposes an entropy-based analysis and optimization framework to quantify RES uncertainty costs and system efficiency. First, an equivalent fuel (EF) cost model is introduced, integrating energy and information layers to quantify the cost of mitigating RES uncertainty. Building on this, entropy theory is employed to establish an information–energy quality coefficient (I-EQC) that evaluates RES energy quality by unifying thermodynamic and information entropy. In addition, a neurodynamics-based distributed algorithm is developed to perform multiobjective optimization for cost and exergy efficiency, enhancing computational speed while preserving data privacy. The simulation results demonstrate that the proposed framework reduces the cost by up to about 10% and improves the efficiency by up to about 5% compared to existing methods.

This article proposes a synergistic model-based actor–critic (AC) learning framework for secure vehicle tracking under a dynamic event-triggered scheme (DETS) and subject to deception attacks. To explicitly resolve the conflict between communication conservation and learning convergence, the AC algorithm is structurally embedded within a discrete linear quadratic tracker (LQT) and backstepping architecture. This co-design utilizes the nominal linear structure as a rigorous prior to analytically derive update laws, ensuring stable Hamilton–Jacobi–Bellman approximation even when data are sparse and stochastically corrupted. By bounding the policy search space, it ensures a minimal number of learnable parameters and a clear physical interpretation. Lyapunov analysis rigorously proves the asymptotic convergence of the tracking errors to zero. Numerical simulations demonstrate superior tracking accuracy, faster convergence, and significant bandwidth savings compared to standard model-free reinforcement learning baselines.

In this article, we investigate a Euclidean-distance-based distributed constrained optimal formation matching (EDCOFM) problem for open large-scale multiagent systems (OLSMASs), where the number of agents is large and variable. To address the open property of the multiagent system, we introduce the concept of a depository, which can provide additional agents or store redundant agents. When the number of agents is sufficient to achieve the formation configuration, a distributed formation matching algorithm for a large-scale multiagent system (DFMA-LSMAS) is proposed to search for the optimal location of the formation configuration within a designed constraint and the optimal matching relationship. Notably, the framework is applicable to open multiagent systems. When the number of agents is smaller than the requirement to achieve the formation configuration, and more than one agent needs to be provided from the depository, an unmatched phenomenon occurs, which results in the failure of the proposed algorithm. To address this, a disturbance-based approach is proposed to eliminate the phenomenon without impacting the optimal solution. When the number of agents is larger than the requirement to achieve the formation configuration, and some agents have to leave the system, a fair competition mechanism is proposed to selectthe remainder and their optimal matching relationship. This mechanism avoids multiple competitions in a centralized manner. Finally, several simulation results are provided to verify the proposed algorithms.

This work delves into an optimal hybrid transmission strategy for remote state estimation (RSE), where some smart sensors observe various systems and transmit their local state estimates to a remote estimator. Inspired by the high bandwidth of a high-frequency (HF) link alongside the robust reliability and low energy consumption of a low-frequency (LF) link, a novel hybrid transmission strategy is proposed. This strategy allows smart sensors to utilize either the HF link or the LF link dynamically, each with distinct channel characteristics and energy consumption. To achieve highly reliable and energy-efficient transmission, it is imperative to devise an optimal transmission scheduling strategy that dictates the selection of the link and channel allocation. Formulating this challenge as a Markov decision process (MDP), a sufficient condition ensuring the existence of an optimal deterministic and stationary (ODS) policy is established. The structural characteristics of the optimal policy are derived. Furthermore, a deep reinforcement learning (DRL) approach, specifically the dueling double deepQ-network (D3QN) algorithm, is employed to approximate the optimal policy. Finally, the structural results and the effectiveness of the DRL algorithm are verified by simulation examples.

When employing evolutionary algorithms (EAs) to solve multimodal optimization problems (MMOPs), effectively utilizing diversity information is crucial to prevent the population from converging to a single peak. This requires balancing diversity and objective value—a challenge that inherently constitutes a penalty problem. Although some implicit penalty methods have been proposed to address this issue, most lack flexibility in penalty formulation. In this study, we present a novel explicit penalty method (EPM) designed to effectively leverage diversity information for multimodal optimization. First, the diversity of a solution is quantified by its distance to the nearest neighbor with a better objective value. Then, an explicit penalty function is formulated by integrating diversity and objective value. This function facilitates the capture of multiple peaks and balances the search among them. If a reasonable number of peaks are identified, a local search is applied to each for refinement; otherwise, a global search is conducted across the decision space. Through this adaptive process, EPM locates multiple optima both efficiently and accurately. Extensive experiments demonstrate that EPM outperforms several multimodal optimization methods, including 11 popular approaches, eight recent state-of-the-art algorithms, and an IEEE CEC competition winner. Moreover, even when integrated with classic differential evolution (DE), EPM exhibits highly competitive performance.

This article, respectively, tackles two types of lag consensus problems for a class of nonlinear multiagent systems (MASs) suffering external disturbances. Based on a nodes-based adaptive event-triggered control (ETC) protocol, the lag consensus can be realized for MASs by utilizing Barbalat’s lemma and inequality techniques. By employing the nodes-based adaptive ETC method, lag$H_{\infty } $consensus is also addressed for MASs. Furthermore, we also prove the facts that MASs with and without external disturbances under the nodes-based adaptive ETC schemes don’t exhibit Zeno behavior. In addition, two edge-based adaptive ETC strategies are also developed to deal with two kinds of lag consensus problems of MASs. Finally, the effectiveness of the devised nodes- and edges-based adaptive ETC protocols is demonstrated via two numerical examples.

This article investigates distributed state estimation of complex networks (CNs) with limited communication capacity. A random transmission strategy is used to overcome the communication capacity constraint between two nodes. A distributed state estimator that makes use of the partially available measurements is designed. To handle the cross-term, Young’s inequality is employed, and an upper bound (UB) for the state prediction error covariance (PEC) is derived. An optimal estimation gain is then devised based on the derived state PEC. The stability of the UB is analyzed using a vectorization approach, and then a sufficient condition for stability is obtained. Finally, a numerical simulation is carried out to validate the effectiveness of the proposed distributed estimator and confirm the accuracy of the derived UB.

This article presents an adaptive ensemble control for stochastic systems subject to asymmetric noises and outliers. Asymmetric noises skew system observations, and outliers with large amplitude deteriorate the observations even further. Such disturbances induce poor system estimation and degraded stochastic system control. In this work, we model the asymmetric noises and outliers by mixed asymmetric Laplace distributions (ALDs) and propose an optimal control for stochastic systems with mixed ALD noises. Particularly, we segregate the system disturbed by mixed ALD noises into subsystems, each of which is subject to a specific ALD noise. For each subsystem, we design an iterative quantile filter (IQF) to estimate the system parameters using system observations. With the estimated parameters by the IQF, we derive the certainty equivalence (CE) control law for each subsystem. Then we use the Bayesian approach to ensemble the subsystem CE controllers, with each of the controllers weighted by its posterior probability. We finalize our control law as the weighted sum of the control signals by the subsystem CE controllers. To demonstrate our approach, we conduct three numerical simulations and Monte Carlo analyses. The results show improved tracking performance by our approach for skew noises and its robustness to outliers, compared with the RLS-based control policy.

The state transition algorithm (STA), as an intelligent optimization method grounded in constructivist learning, has been demonstrated to be highly effective in solving complex optimization problems. However, the standard STA suffers from slow convergence, particularly in the later stages when dealing with flat landscapes. Additionally, users are required to set the maximum number of iterations based on intuition. To address these issues, an enhanced STA with guaranteed optimality is introduced. This improvement involves three key components. First, novel translation transformations (TTs), inspired by predictive modeling, are developed to generate a broader set of candidate solutions by leveraging historical data. Second, adaptive parameter control strategies are incorporated to accelerate convergence. Finally, a dedicated termination condition is designed to ensure that the algorithm converges at the optimal solution, analogous to the zero gradient condition in mathematical programming. The comprehensive experimental results validate the effectiveness and superiority of the proposed method. The source codes for ESTA and EXSTA will be publicly available athttps://github.com/tiezhongyu2005/ESTA

The widespread application of intelligent storage systems has urged more requirements for system security. In this work, we consider a resilient pickup and delivery task of the multi-robot systems against strategic attacks. The strategic attacks terminate the normal motion of a fraction of key operating robots with a well-designed strategy, and thus jeopardize the cooperation among robots. A three-layered decision framework is proposed to suppress the above strategic attacks: The first layer introduces defensive strategies, which incorporate reputation mechanisms and counterfactual rescue mode allocations. The counterfactual rescue-mode allocation mechanism dynamically assesses the benefit differences between rescuing others for resilience and conducting self-tasks for efficiency. Based on the reputation mechanisms, the second layer employs a bi-level programming method for pickup/delivery mode allocations and task assignments. Then, the third layer calculates the collision-free path for the swarm using a mixed integer linear programming model. The practicality and resilience of this algorithm against strategic attacks have been demonstrated through numerical simulations involving various robot scales and task burdens.

With growing awareness of privacy protection, federated learning (FL) in vehicular network scenarios effectively addresses privacy concerns, leading to the development of federated vehicular networks (FVNs). In FVN, vehicles maintain a global model by transmitting local models and iterative processing, resulting in significant communication overhead. In the Internet of Vehicles (IoV), as the majority of the spectrum resources are allocated to Vehicle-to-Vehicle (V2V) communication, vehicles engaged in FL encounter uplink interference when these resources are reused. This compromises the efficacy of FL vehicle communication. To address this issue, we propose FedCDC, a new federated learning framework with adaptive weight clustering with knowledge distillation and channel sharing-based resource allocation. First, we implement a communication compression strategy based on clustering and distillation to alleviate transmission load. Then, we develop a resource allocation strategy to maximize the signal-to-interference-plus-noise ratio (SINR) for both FL vehicles and V2V groups, which investigates two critical components: 1) channel pairing and 2) power coordination. We formulate the optimization issue as a multiobjective optimization problem, that is, solved offline using the MIDACO solver. Due to the dynamic characteristics of FVN, we employ a multiagent deep deterministic policy gradient (MADDPG) to enhance efficiency. Finally, extensive experiments demonstrate that our approach significantly reduces data transmission and offers an efficient resource coordination plan, thus improving FL communication efficiency and ensuring the Quality of Service (QoS) for both V2V groups and FL vehicles.

This paper focuses on the fully distributed dynamic event-triggered optimized backstepping (OB) containment control for a class of stochastic nonlinear high-order strict-feedback multiagent systems (MASs) based on reinforcement learning (RL) algorithm. Firstly, an OB control scheme is proposed using the critic-actor structure. In this scheme, neural networks are applied to approximate the value function and the controllers are derived from the gradients of simple positive functions. This approach simplifies the algorithm and effectively addresses two fundamental requirements commonly encountered in RL-based optimal control: known dynamics and persistent excitation. Furthermore, the static and dynamic event-triggered mechanisms are designed in order to reduce the control actions in the highest layer for nonlinear high-order stochastic MASs. Then, the distributed event-triggered control protocols based on RL are proposed for the strict-feedback nonlinear MASs with stochastic disturbances. Finally, simulation results are shown to demonstrate the effectiveness of the theoretical analysis.

Cooperative adaptive cruise control (CACC) systems face critical cybersecurity challenges due to vulnerabilities in vehicle-to-vehicle (V2V) networks and onboard sensors. Covert attacks have become a critical research focus in CACC systems due to their inherent risks and stealthiness. In this article, a novel attack model called covert coordinated attack (CCA) is proposed, and its detection and isolation schemes are proposed to enhance the resilience of CACC systems against such an attack. First, the specific CCA strategies are proposed by revealing dual-channel attack coordination mechanisms targeting both sensors and V2V networks. Second, the detection framework based on unknown input observers (UIOs) is designed by monitoring the residual-based health characteristic. Third, based on the established two auxiliary systems, an attack isolation strategy integrating UIOs and extended state observers (ESOs) is proposed for attack reconstruction and system performance recovery. Finally, numerical simulations are conducted to validate both the stealthiness of CCA and the effectiveness of the proposed attack detection and isolation methods.

Prostate cancer is the most common malignancy among Australian men, with over 20 000 new diagnoses each year. Accurate forecasts of its incidence and mortality inform stakeholder decision-making and help mitigate its public health impact. In this context, we introduce cutting-edge lightweight neural networks into the domain of prostate cancer data forecasting with edge intelligence for the first time. To address the issue of overfitting in coarse-grained and small-scale prostate cancer datasets, we employ structurally streamlined models: the Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), representing two predominant branches of neural networks. The GRU’s simplified gating mechanism maintains excellent long-term dependencies capturing capability while drastically reducing parameter count, and the TCN combines sparse connections, parameter sharing, and causal dilated convolutions for efficient temporal modeling. To further bolster generalization, we integrate multiple regularization strategies, including the snapshot ensemble method. Comparative experiments on three real-world prostate cancer datasets demonstrate that our improved lightweight, high-performance neural networks achieve over 40% higher accuracy than linear time series forecasting suitable for small-scale datasets.

In the face of the rapid evolution and escalating complexity of financial markets, precise stock price prediction has become a critical area of research for scholars and practitioners alike. Stock markets are subject to a vast array of influencing factors, both internal and external, which complicates prediction efforts. This study proposes BiMT-TCN, a novel model combining Bidirectional Long Short-Term Memory (BiLSTM), a modified Transformer, and Temporal Convolutional Network (TCN), aimed at enhancing the accuracy and stability in stock price prediction. BiLSTM facilitates the capture of bidirectional dependencies, which aids in decoding the intricate patterns within time-series data. The modified Transformer integrates global information, enhancing the model’s capacity to manage long-range dependencies effectively. TCN, known for its parallel processing and proficiency in capturing deep historical patterns, further bolsters model stability and generalizability. Empirical evaluations on major indices such as SSE, HSI, and NASDAQ demonstrate that BiMT-TCN consistently outperforms state-of-the-art models, achieving R 2 scores of 0.9779, 0.9776, and 0.9969 respectively, along with significantly lower RMSE, MAE, and MAPE values. The implications of this work extend to practical investment decision-making, where improved forecast precision can enhance risk management, optimize trading strategies, and inform financial planning in volatile markets.

Hand gesture recognition (HGR) is a significant research area with applications in human-computer interaction, artificial intelligence, and more. In the early stage of development of HGR, there are high hardware costs and large usage requirements. To reduce the high cost expenditure and increase the application scenario, deep learning has played a crucial role. With the greater depth perception and more computing power, currently HGR is more about continuous recognition in space based on vedio. But in this article, it considers that there is a growing demand for lightweight networks with high precision for end-to-end HGR applications. In that, it still tends to recognize consecutive video frames and get results quickly. This paper introduces an enhanced network called YOLOv8-G2F, which is based on YOLOv8. It incorporates improved lightweight modules not only replace the traditional convolution module of the network's backbone and neck but also for the C2f module in YOLOv8. The network employs linear transformations, group convolution, and depthwise separable convolution to extract image information using simpler networks. Furthermore, model pruning is also used to further reduce model size and improve accuracy. The improved model achieved a recognition accuracy of 99.2% on the nus-ii gesture dataset with a model size of 2.33 MB. After extensive comparison and ablation experiments, YOLOv8-G2F demonstrated significant progress over existing algorithms.

This article delves into quasi-projective synchronization (Q-PS) problem for a class of discrete-time fractional-order T-S fuzzy complex-valued neural networks (DFTSFCNNs) with leakage and time-varying delays. First of all, according to the theory of discrete fractional calculus and properties of Mittag-Leffler function, an innovative property of discrete Mittag-Leffler function is strictly proved, and then an inequality for dealing with mixed time delays is rigorously derived. Next, by utilizing Caputo fractional difference theory and combining with property and the inequality offered in this article, some easily verifiable Q-PS criteria are derived under complex-valued fuzzy linear controller. Eventually, a numerical example is presented to demonstrate availability of the derived results.

This paper addresses the consensus control problem of heterogeneous multi-UAV systems against Byzantine attacks. A drone compromised by Byzantine attacks transmits erroneous values to its neighbors while applying wrong input signals for itself, which is aggressive and challenging to defend. Inspired by the concept of digital twin technology, we introduce a new hierarchical protocol equipped with a virtual twin layer (TL), which decouples the challenges into two defense schemes: one against Byzantine edge attacks on the TL and the other against Byzantine node attacks on the cyber-physical layer (CPL). In the TL, we provide a topology reconfiguration strategy that enhances the resilience of the communication network by judiciously adding a minimal number of key edges. We rigorously demonstrate that the control strategy attains asymptotic consensus within a finite timeframe, given that the topology on the TL adheres to a strongly$(2f+1)$-robustness criterion. Within the CPL, decentralized chattering-free controllers are proposed to ensure the resilient output consensus for the heterogeneous multi-UAV systems against Byzantine node attacks. Furthermore, the derived consensus controller exhibits an exponential convergence characteristic. The effectiveness and practicality of the obtained theoretical results are verified by a UAV swarm flight experiment. Note to Practitioners—Cooperative control of UAVs presents significant prospects for application and development, becoming a focal point of automatic control. By modeling UAV swarms as multi-agent systems, various complex distributed control methods have been conveniently proposed and implemented economically in practical systems. However, when certain agents are compromised and interfere with their neighbors, the whole network may become highly susceptible to failure. This paper specifically studies the resilient consensus control against the significant active internal threats, Byzantine attacks. The published results have primarily focused on cases where the leader UAV has no input signals. In practical applications, however, the leader often has a pre-established trajectory sent by the host and the followers are unaware of this input information. This significantly complicates the task of identifying Byzantine attackers. In this work, we introduce a new hierarchical protocol inspired by the concept of digital twin technology, which decouples the challenges into defense against Byzantine edge attacks on the TL and the defense against Byzantine node attacks on the cyber-physical layer. The experiment shows the feasibility and security of our control scheme, which provides valuable guidance for the practical applications of drones.

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. Note to Practitioners—This paper aims to develop a fully distributed MFASMC method to address the consensus problem for unknown MASs with hybrid attacks in network topologies. A hybrid attack compensation mechanism is proposed to mitigate the effects of neighbor information dropouts caused by hybrid attacks. By combining the sliding mode control theory with the model-free adaptive control strategy, the system’s robustness is improved to relieve the impact of neighbor information inaccuracies attributed to hybrid attacks. Since the proposed algorithm does not depend on the mathematical model and Laplace matrix of systems, it can be applied to large-scale MASs with unknown models to solve network topology security problems, such as multiple subway trains, wireless communication systems, and microgrid systems. Furthermore, the proposed method is simple in design, widely adaptable, and robust, making it easier to apply to practical systems and more friendly to control engineers.

This paper considers a type of high-order nonlinear multiagent systems (MASs), and the time-varying optimal lag formation control problem for such MAS is addressed. To eliminate the influences of the unknown system dynamics and external disturbance on single agent, a neural network (NN) identifier and disturbance observer based compensation controller is developed. Then, an optimal control law to ensure the practical time-varying lag formation for the MAS is introduced by exploiting the sliding-mode control (SMC) method and reinforcement learning (RL) technique. Finally, a numerical example is given to validate the effectiveness of the proposed optimal control law.

This study addresses resilient human-in-the-loop (HiTL) formation-tracking of multi-UAV systems against$f$-local Byzantine attacks. In the HiTL settings, a human operator plays a key role in detecting any physical hazard, monitoring the whole UAV swarm, and sending secure execution signals to a non-autonomous leader UAV. Moreover, there exists a fraction of Byzantine UAVs in the multi-UAV systems, which propagate incorrect information to their neighbors (called Byzantine edge attacks (BEAs)) and adopt false input signals (called Byzantine node attacks (BNAs)) when swarming. In order to suppress the above aggressive Byzantine attacks, this paper proposes a Byzantine-resilient hierarchical control scheme, including a virtual Digital Twin Layer (DTL) apart from a Cyber-Physical Layer (CPL). First, a distributed resilient estimation scheme is proposed on the DTL, which can realize resilient estimation on the state of the non-autonomous leader UAV against BEAs on the premise that the DTL topology is strongly$(2f+1)$-robust. Second, a series of decentralized and chattering-free controllers is formulated on the CPL, which is resilient to both BNAs and inter-layered faults. The asymptotical control performance of the above controllers is strictly proven based on Cromwell-Bellman Lemma. To demonstrate the practicality of the theoretical results, a resilient HiTL multi-UAV systems experiment has been further conducted. The experimental results verify the effectiveness and practicality of the designed two-layered controllers against$f$-local Byzantine attacks.Note to Practitioners—Owing to the wide application of multi-UAV systems, the resilience of the whole swarm against malicious attacks has grasped the great attention of both academia and industry. This work considers a rather aggressive kind of attacks, named Byzantine attacks, where a fraction of unidentified UAVs act as traitors. Inspired by the digital twin technology, a two-layered control architecture for multi-UAV systems is formatted, including a Digital Twin Layer (DTL) and a Cyber-Physical Layer (CPL). Here are the highlights: 1) Control Architecture: The DTL handles Byzantine edge attacks (BEAs), while the CPL addresses Byzantine node attacks (BNAs), ensuring reliable human-swarm cooperation in adversarial environments. 2) Resilient Estimation against BEAs: A novel resilient estimation scheme on the DTL is designed, using edge-based feedback, which can estimate the states of the leader UAV manipulated by human operators. 3) Adaptive Controller against BNAs and Inter-layered Faults: On the CPL, a decentralized adaptive controller with adjustable and exponential convergence is proposed, enhancing its precision and flexibility. 4) Practical Application: A UAV swarm formation-tracking experiment validates the control architecture’s effectiveness in human-in-the-loop scenarios, demonstrating its practicality in the realm of swarm robotics and human-swarm interaction.

This paper is concerned with the fixed-time optimal bipartite containment control issue for stochastic nonlinear multiagent systems in the presence of unknown Bouc-Wen hysteresis. Initially, under the optimized backstepping framework, the approximation solution of the Hamilton-Jacobi-Bellman equation is obtained via the reinforcement learning algorithm. Then, the neural networks are utilized to shape an identifier-critic-actor structure to approximate unknown dynamics, evaluate system performance, and implement control behavior, respectively. Moreover, an intermediate variable is devised to cope with the unknown control coefficient resulting from Bouc-Wen hysteresis. Theoretical analysis shows that all states of the closed-loop system are semi-global practical fixed-time stable, and its convergence time is capable of adjusting by choosing appropriate design parameters. Finally, a numerical example is performed to solidify the effectiveness of the proposed strategy. Note to Practitioners—It is well known that fixed-time convergence is one of the significance performance metrics in many industrial systems including manipulator systems, robotic systems as well as unmanned aerial systems. Motivated by this fact, this article devotes the first attempt to investigate the fixed-time optimal bipartite containment control issue for stochastic nonlinear multiagent systems in the presence of unknown Bouc-Wen hysteresis according to the reinforcement learning control strategy. Theoretical analysis and simulation examples both show that all state signals of the closed-loop system are semi-global practical fixed-time stable, and its convergence time is capable of adjusting by choosing appropriate design parameters.

This paper addresses the problem of distributed estimation and motion control (DEMC) in multi-agent systems (MASs) with both linear and Lipschitz nonlinear dynamics. Unlike conventional DEMC methods designed for MASs under ideal conditions, this work investigates scenarios where all agents are vulnerable to various forms of attacks. The considered attacks comprise false-data injection (FDI) attacks and denial of service (DoS) attacks that affect the communication channels among agents to destabilize the MAS. Also, the unbounded actuator attacks which exist in practical environments to intentionally degrade the MAS performance is considered. To cope with these kinds of attacks, two novel resilient approaches are established aimed at estimating and following a mobile target under attacks. The proposed distributed attack-resilient control strategies are designed based on a dual-layer structure, guaranteeing effective DEMC with an ultimately bounded error. The results from two simulation examples are provided to validate the presented algorithms. Note to Practitioners—The motivation of this work is to deal with the DEMC problem for MASs under multiple attacks. In most of the existing DEMC schemes for MASs, having a healthy network and dynamics is a requirement. However, in practical environments, MASs as an important subclass of cyber-physical systems are subject to different types of attacks that affect the network and dynamics of MASs and may seriously jeopardize the performance of the DEMC algorithm, or even worse, lead to instability. Therefore, a resilient hierarchical DEMC algorithm is proposed for MASs which allows agents to estimate and follow a mobile target under multiple attacks. The proposed scheme is resilient to most existing cyber-attacks and is designed for MASs with both linear and nonlinear dynamics. It can be applied to various practical engineering systems such as autonomous vehicles, mobile robots, and intelligent transportation systems. The stability and convergence of the proposed algorithms are analyzed mathematically, and it is shown that the agents not only track the estimated target but also can cope with multiple attacks through simulation experiments.

In finite-time consensus tracking (FCT) missions of unmanned aerial vehicles (UAVs), faults in aircraft actuators are crucial factors causing tracking precision degradation. In order to solve this problem, this paper develops a fuzzy fault-tolerant cooperative control scheme for UAVs to achieve FCT missions with predefined precision. Compared to existing results, the settling time formulation approach of consensus errors is significantly simplified, which facilitates operators to preset the settling time. Then a switching event-triggered mechanism is designed to flexibly change the transmission frequency of control signals, enabling the limited communication resources of UAVs to be reasonably consumed before and after the settling time. Based on the Lyapunov stability theory, it is proved that all signals of systems are bounded, and consensus errors can achieve prescribed precision in a finite time. Moreover, Zeno behavior is excluded. Finally, a simulation study demonstrates the efficiency and feasibility of the designed control scheme under various uncertainties. Note to Practitioners—In the existing FCT control schemes for UAVs, the formulation approaches of settling time depend on the structure of the communication network and the initial states of the aircraft, which hampers these schemes implemented in large-scale UAVs control tasks. How to effectively simplify the formulation approach of settling time has crucial practical significance and research value. In addition, since the communication bandwidth of UAVs is limited, the existing control strategies introduce event-triggered mechanisms to decrease the communication burden. However, adopting a single event-triggered mechanism cannot flexibly change the transmission frequency of control signals, which may make the limited communication resources fail to be reasonably consumed before and after the settling time. In order to solve the above problems, this paper develops a switching event-triggered FCT control scheme for UAVs, where the designed trigger mechanism can flexibly change the transmission frequency of control signals. Meanwhile, the settling time can be directly formulated following the wishes of operators.

The paper addresses the fault-tolerant optimized control of switched complex networks with unknown state and actuator fault. A proportional-integral intermediate observer is constructed to estimate unknown elements by relaxing known boundary requirement. An optimized controller is proposed to achieve fault-tolerant synchronization via adaptive dynamic programming scheme. A critic neural network is employed to solve the value of the Hamilton–Jacobi–Bellman equation. Sufficient conditions are established to ensure the uniformly ultimately bounded synchronization performance. Finally, an example is simulated to deliver the effectiveness of the proposed approach.

This paper is concerned with the intermittent iterative learning control for robot manipulators with packet dropouts. A composite controller is presented by a proportional-derivative feedback and an iterative learning feedforward to deal with the dropouts. A modified reference trajectory is adopted to treat input intermittence. With the help of composite energy function, rigorous theoretical derivations are provided to ensure the convergence of the estimation error of the iterative estimator and the boundedness of the tracking error. The validity of the proposed method is demonstrated by simulation studies on a single-link manipulator and a two-degrees-of-freedom planar manipulator, respectively. Note to Practitioners—Robot manipulators have drawn significant attentions due to their wide industrial applications. Due to their repetitive feature, iterative learning control is an effective strategy to achieve high tracking accuracy for them. However, this strategy faces new challenges incurred from network environment, such as packet dropouts and how to save energy. To patch this gap, an interpolation method is exploited for an iterative estimator to handle packet dropouts and a modified reference trajectory is employed to tackle with input intermittence. Simulation studies with practical background is supplied to show the validity of the proposed method. Therefore, this paper lays a theoretical basis for the tracking control of robot manipulators in practical applications.

In this paper, fixed-time and practical fixed-time control of switched Filippov systems on networks with nondifferential delays are studied. Firstly, by using the comparison principle and intermittent control interval method, intermittent fixed-time stability lemmas with unified exponential conditions are proposed. The estimation of the settling-time shows that the elasticity number in the control interval plays a vital role. In order to solve the unavailability of global information, without using the auxiliary timer function, intermittent practical fixed-time lemmas with unified exponential conditions are established, which can improve the related ones greatly. It is necessary to point out that the residual set is relevant to the constant parameters of the system. The constant parameters of the system can be used to get a needed attraction region. Based on newly established stability lemmas, the fixed-time control and practical fixed-time control of the addressed switched Filippov systems are achieved based on the designed intermittent-type quantized control strategies, which enables the quantized control to be performed in intermittent intervals. Some previous control strategies are improved. Finally, numerical examples and simulations are provided to verify the validity of the obtained results. Note to Practitioners—Intermittent control has become popular recently since it saves more costs and is more practical in engineering. As for the complex networks, it is not enough to use intermittent control only, but also to consider the bandwidth of signal transmission. So, how to enable the quantized control to be performed in intermittent intervals is an interesting question. In addition, the global information of many models is unidentifiable, practical fixed-time control will be more realistic. Is the residual set in practical fixed-time control relevant to the intermittent control interval? These questions puzzled the authors and prompted this study.

This article addresses adaptive event-triggered sampled-data (SD) fuzzy security control under spatially local averaged measurements (LAMs) for nonlinear delayed distributed parameter systems (DPSs) with denial of service (DoS) attacks and stochastic actuator failures. Firstly, a Takagi–Sugeno (T–S) fuzzy model of delayed partial differential equations (PDEs) is introduced to precisely characterize the dynamic behavior of nonlinear delayed DPS. Secondly, an adaptive event-triggered SD fuzzy security control strategy is designed under DoS attacks and stochastic actuator failures, which can be flexibly modified in accordance with the present sampling and the most recently transmitted signals, and is implemented utilizing a restricted number of sensors and actuators. Subsequently, by establishing a Lyapunov functional, sufficient conditions that guarantee the mean square exponential stability of closed-loop nonlinear delayed DPSs are obtained based on linear matrix inequalities (LMIs). Finally, two examples are provided and the presented controller are compared to demonstrate the applications and advantages of the proposed approach.

Based on PD and PI control methods, the lag output consensus problem for second-order nonlinear multiagent systems (MASs) without and with external disturbances is addressed, which generalizes the existing lag consensus results for MASs. Firstly, a lag output consensus condition is derived for the MAS by designing a proper PD control scheme. Furthermore, a PI control strategy is developed to ensure that the MAS realizes the lag output consensus. In addition, two lag${\mathcal {H}}_{\infty } $output consensus criteria are also established on the basis of PD and PI control schemes for the MAS with external disturbances. Lastly, two examples are utilized to verify the correctness and effectiveness of the devised control schemes. Note to Practitioners—Recently, virtual leader-based approach has been widely utilized to address the output consensus control problem for various MASs. Practically, each agent receiving its own output information may be later than the virtual leader. Therefore, it is more reasonable and meaningful to discuss the lag output consensus for MASs. Furthermore, considering that PID control is very simple and easy to implement, some PD and PI control strategies are developed to tackle the lag output consensus for MASs. In addition, since external disturbances are ubiquitous and may destroy the lag output consensus, the lag${\mathcal {H}}_{\infty } $output consensus for MASs is also investigated. Multiple single-link robot systems experiments verify that the approaches given in this paper are feasible and valid.

How to achieve synchronization control for Piecewise Homogeneous Markov Delayed Neural Networks (PHMDNNs) under hybrid cyber-attacks is the primary focus of this research. Firstly, a Piecewise Homogeneous Markov Process (PHMP) is employed to model the mode transitions of system parameters and controllers, accurately capturing the dynamic characteristics of practical systems and providing a solid foundation for subsequent controller design. In response to the challenges arising from hybrid cyber-attacks, a novel controller is developed based on an input retention strategy. This ensures system stability under hybrid cyber-attacks, effectively avoiding the instability issues caused by traditional zero-input strategies and enhancing control robustness. To further optimize system performance, an improved Resilient Adaptive Event-triggered Mechanism (RAETM) is proposed. By optimizing triggering conditions and thresholds, the mechanism reduces communication overhead while strengthening system security, making it well-suited for networked control systems. In addition, a generalized common Lyapunov functional is constructed by incorporating sampling instants, time delays, and Markov jump parameters. Sufficient conditions for system synchronization and stability are derived, providing a simplified analytical framework. Finally, the effectiveness and superiority of the proposed approach are confirmed through simulation results, showcasing its robust performance against hybrid cyber-attacks and its ability to achieve secure synchronization.

We consider a scenario in which a group of robots tracks a group of targets in an open space. In particular, the robots are heterogeneous, and the targets are adversarial, capable of attacking the robots and severing the links between the robots and their corresponding sensors. Additionally, each robot is required to estimate the state of the targets individually on the basis of the communication graph. We propose a framework that adaptively balances accuracy and safety while automatically repairing the communication graph. Our framework follows a two-stage strategy: In the first stage, we assess the entire team to determine if repair is necessary. If necessary, by quantifying the team’s observability using the trace of the Grammian matrix, we propose a computationally efficient repair strategy. In the second stage, safety and accuracy are quantified, with the sensing margin serving as the dynamic weight to guide robot coordination. To validate the effectiveness of our work, we simulated a monitoring and tracking task and compared our network reconfiguration strategy with greedy and One-Hop-Grammian-based methods. The simulation results demonstrate the effectiveness and efficiency of our approach.

This work studies the problem of finding optimal distributed resource allocation policies on wireless networks in the existence of an unknown malicious adding-edge attacker, depicted as the games of games (GoG) model. Specifically, two subnetwork policymakers constitute a Nash game, while a Stackelberg game captures the confrontation between each subnetwork policymaker and the malicious attacker. We first demonstrate that by utilizing the Foschini-Miljanic algorithm, the communication resource allocation of cellular networks can be converted into a geometric program (GP) that can be efficiently solved by convex optimization. Then, the upper limit of attack magnitude that the network can withstand is calculated. It is shown that the proposed GP framework is solvable within the attack bound and a Gestalt Nash equilibrium (GNE) exists for the GoG. Moreover, a heuristic algorithm that iteratively employs GP is developed to obtain the optimal policy profiles of subnetworks, which can converge asymptotically. Correspondingly, a greedy heuristic adding-edge strategy is proposed to identify the set of the most vulnerable edges for the attacker. Finally, simulation examples show that the obtained algorithm is resilient to malicious attacks and can attain the GNE. Despite the presence of malicious attacks, all channels’ transmission and interference gains can be well-adjusted within a limited budget.

This paper investigates the bipartite consensus problem for heterogeneous multi-agent systems subjected to Byzantine attacks. Byzantine agents send erroneous signals to their neighbors while utilizing incorrect input signals themselves, posing significant challenges for defense. To defend against Byzantine attacks, we propose a resilient heterogeneous impulsive bipartite consensus algorithm for multi-agent systems. This approach ensures that information transmission occurs exclusively at sampling points, significantly reducing control costs, minimizing communication redundancy, and enhancing system robustness. During each sampling event, agents eliminate the most extreme values from their neighbors and utilize the remaining information to generate the control input. By employing this resilient scheme and leveraging the properties of Sarymsakov matrices, we demonstrate that the proposed impulsive control method effectively limits the impact of Byzantine attacks. We also determine the maximum allowable number of Byzantine agents and the corresponding network robustness required to ensure the agents achieve bipartite consensus. Finally, simulations and experiments validate the effectiveness of the proposed approach.

In this article, the memristor-based selective convolutional (MSC) circuit for salt-and-pepper (SAP) noise removal was proposed. In experiments, the MSC model was built and benchmarked against a ternary selective convolutional (TSC) model. Results show that the MSC model effectively restores images corrupted by SAP noise, achieving similar performance to the TSC model in both quantitative measures and visual quality at noise densities of up to 50%. In addition, this study proposes an enhanced MSC (MSCE) model based on MSC, which reduces power consumption by 57.6% compared with the MSC model while improving performance. The MSCE model maintains reliability when memristors experience conductance drift rates of less than 30% and yields greater than 89%.

This study aims to explore the stabilization problem of state-based fuzzy switched dynamic systems with Lévy noise, non-differentiable delay and actuator saturation through a novel intermittent output feedback control mechanism. Firstly, the delay studied in this study is non-differentiable, eliminating the harsh condition that the delay in the existing conventional system must be differentiable or even require the derivative to be less than 1, and replacing the existing common Brownian motion with Lévy noise that is more realistic to simulate the inevitable stochastic noise in the system, which has more theoretical research significance and exploration value. Subsequently, by means of dynamic output feedback mechanism, a new intermittent output feedback control scheme is designed to replace the conventional intermittent state feedback control scheme, which has important practicability and flexibility in practical control. Especially, a novel stabilization criterion is developed by selecting a novel switching-Lyapunov function in combination with two novel lemma pairs for solving non-differentiable terms and actuator saturation. Furthermore, two additional algorithms are given to maximize the attraction domain of the closed-loop system and to minimize the control gain matrix in order to minimize the control cost. Finally, a numerical example is used to verify the validity of the developed results, and a meaningful additional practical electronic circuit model is provided to prove its practicability and superiority.

This study investigates the problem of designing countermeasures for resilient output time-varying formation-tracking of multi-agent systems (MASs) in a composite hazardous scenario consisting of various kinds of attacks, such as actuation attacks and denial-of-service (DoS) attacks. Combined with the digital twins concept, the above problem is decoupled into a DoS attack defense scheme on the Digital Twins Layer (DTL) and an actuation attack defense scheme on the Cyber-Physical Layer (CPL) by introducing a DTL with higher security and privacy. On the DTL, a novel Zeno-free event-triggered protocol with sampling state information, equipped with the backup topology countermeasure, is used to suppress the threat of DoS attacks. Note that the resilient protocol against DoS attacks is fully-distributed, which does not need prior knowledge of network algebraic connectivity and thus possesses sufficient scalability to large-scale networks. Moreover, the event-triggered mechanism can completely avoid Zeno phenomena by introducing a strict lower bound for the time interval between cascading triggered events. On the CPL, this study introduces a decentralized, adaptive, and resilient control strategy to address actuation attacks exhibiting exponentially unbounded destructiveness. It is proven that all followers can reach uniformly ultimately bounded convergence by the proposed two-layered control protocols. The effectiveness and resilience of the proposed algorithm are validated through a numerical simulation example.

This article proposes a dynamic self-triggered distributed model predictive control algorithm for coupled nonlinear systems facing external disturbances and constraints on state and input variables. A dynamic self-triggered mechanism that combines the advantages of event-triggered and self-triggered strategies is designed to simultaneously reduce the frequencies of both sampling and solving optimization problems. Particularly, the triggering threshold is adaptively adjusted using a dynamic variable, which can effectively balance control performance and computational resources. Furthermore, through the construction of a two-model optimal control problem and the analysis of input-to-state practical stability for the overall system, a single-mode distributed model predictive control framework is established for each subsystem within the proposed algorithm, which enables a fully distributed implementation. Sufficient conditions for recursive feasibility and robust stability are investigated, and conservatism is reduced by eliminating the requirement for the system state to reach the terminal region in finite time. Finally, the effectiveness of the developed algorithm is validated through two numerical examples with comparisons.

This paper addresses the problem of distributed online generalized Nash equilibrium (GNE) learning for multi-cluster games with delayed function feedback. Specifically, each agent in the game is assumed to be informed of a sequence of local cost functions and constraint functions, which are known to the agent with time-varying delays subsequent to decision-making at each round. The objective of each agent within a cluster is to collaboratively optimize the cluster’s cost function, subject to time-varying coupled inequality constraints and local constraint sets over time. Additionally, it is assumed that each agent is required to estimate the decisions of all other agents through interactions with its neighbors, rather than directly accessing the decisions of all agents, i.e., each agent needs to make decisions under partial-decision information. To solve such a challenging problem, a novel distributed online delay-tolerant GNE learning algorithm is developed based upon the primal-dual algorithm with an aggregation gradient mechanism. The system-wise regret and the constraint violation are formulated to measure the performance of the algorithm, demonstrating sublinear growth with respect to the time horizon$\boldsymbol {T}$under certain conditions. Finally, numerical results are presented to verify the effectiveness of the proposed algorithm.

The rapid growth of digital interaction has produced a massive volume of complex, high-dimensional data. This data reflects individual behavior, group dynamics, and large-scale patterns across social platforms, sensor networks, and digital infrastructures. Traditional methods struggle to keep up with the speed and scale of this data. New models are needed to find structure, uncover hidden patterns, and support timely decisions [1]. As digital and physical environments continue to overlap, through smart cities, wearables, and connected devices, computational social systems must adapt to handle behaviors that are increasingly fast-changing, distributed, and varied across data types.

This work investigates the time-varying formation-tracking problem of multiagent systems under hybrid attacks, including denial-of-service (DoS) attacks and actuation attacks. State estimators are designed for each node of the swarm leveraging relative information from neighboring estimators to generate the desired positional states for formation tracking. The direct use of corrupted consensus control inputs is avoided, thereby defending against actuation attacks targeted at node input signals. Furthermore, we propose an event-triggered protocol with a sampling mechanism to enhance resilience against DoS attacks on communication with neighboring estimators equipped with a topology recovery policy. This resilient protocol against DoS attacks is fully distributed and does not require prior knowledge of network topology, making it scalable to large networks. Finally, an adaptive attack-resilient control scheme is introduced to counteract potential unbounded actuation attacks via output feedback, enabling each follower to track the positional states provided by the distributed estimators. The tracking error is proven to be uniformly ultimately bounded. The proposed event-triggered hierarchical control scheme is validated through its application to spacecraft formation.

This study focuses on the state tracking problems of chaotic Lur'e systems with nondifferentiable delay and partial channel restriction via intermittent observer. Different from the results obtained by existing chaotic Lur'e systems, the time delay involved in the chaotic Lur'e system explored in this study does not require that the time delay must be differentiable and there is no limit that the time delay must be bounded, which is more in accordance with the operating situation in practical engineering. Subsequently, aiming at the inevitable cyber-attack in the communication network environment, an intermittent observer based on the loss of partial measurement information induced by cyber-attack is constructed. Especially, in the operation process of the designed intermittent estimator, it breaks through the strict requirement that the observation width must be greater than the upper limit of the delay in the existing work, which intuitively reflects the wider application range and has more exploratory significance. Moreover, a meaningful lemma is designed, which effectively overcomes the invalidity of existing Lyapunov function or Lyapunov-Krasovskii functional methods for nondifferentiable systems, and develops novel criteria for partial information loss and nondifferentiable systems, presenting an important theoretical guidance for related topics. Numerical simulations are provided to verify the effectiveness of the proposed intermittent observer mechanism and the stabilization criteria of the error system. Finally, an application of the proposed intermittent observation result for two identical chaotic systems to a secure communication approach is provided.

Privacy leakage and Byzantine issues are two adverse factors to optimization and learning processes of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this article targets the resolution of a category of nonconvex optimization problems under the Polyak-Łojasiewicz (P-Ł) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noise and adopt a resilient aggregation method, self-centered clipping (SCC), to design a differentially private (DP) and Byzantine-resilient (BR) decentralized stochastic gradient algorithm, dubbed DP-SCC-PL, aiming to address a class of nonconvex optimization problems in the presence of both privacy leakage and Byzantine issues. The convergence analysis of DP-SCC-PL is challenging, as the convergence error arises from the coupled effects of DP and BR mechanisms, as well as the nonconvex relaxation, which is resolved via seeking the contraction relationships among the disagreement measure of reliable agents before and after the SCC aggregation, together with the optimal gap. Theoretical results not only reveal the trilemma between algorithm utility, resilience, and privacy, but also show that DP-SCC-PL can achieve consensus among all reliable agents. It has also been proven that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-Ł condition under various Byzantine attacks.

This article investigates the adaptive resilient flexible output containment (FOC) control problem for semi-Markov jump fully heterogeneous multiagent systems (FHMASs) under random switching topologies and denial-of-service (DoS) attacks. In contrast to most existing containment control results, the proposed control strategy can address the challenges posed by the full heterogeneity of multiagent systems (MASs), particularly when multiple leaders exhibit different system dynamics. To better reflect real-world MASs and communication networks, multiple asynchronous semi-Markov chains are employed for the first time to capture system parameter variations and communication topology switching, incorporating generally uncertain transition rates (TRs). In order to deal with this problem, a novel adaptive observer-based FOC control framework is developed. First, by introducing an adaptive gain, the adaptive resilient observers can observe leaders' states without prior knowledge of global topology information and TRs, while resisting the impacts of random switching topologies and DoS attacks. Then, a dynamic output feedback controller is designed to ensure the achievement of FOC. Notably, the containment coefficients in the controller design are no longer tied to the Laplacian matrix and can be flexibly predefined to align with specific task requirements. Furthermore, the linear matrix inequalities (LMIs) to obtain estimator gain matrices and controller gain matrices are derived for the case of generally uncertain TRs, respectively. Finally, the effectiveness of the theoretical method is demonstrated through the simulation.

Synthetic paradoxical control circuits have been used to regulate cell population density. However, paradoxical interactions and inherent time delays in these circuits complicate stable population control due to their effects on system dynamics. This article proposes a strategy to achieve robust population control by analyzing the bistable switching and oscillatory behavior in a delayed paradoxical cell population control circuit. First, five distinct population outcomes are identified, along with stabilization criteria for these states, highlighting the presence of bistability. Specifically, we reveal two bistable switching mechanisms: one driven by the initial cell population density and the other by the time delay for blasticidin-induced cell death. Furthermore, we demonstrate that the time delay can destabilize the effective control state via a Hopf bifurcation, leading to periodic oscillations, which can be mitigated by adjusting the blasticidin concentration. Based on these findings, we propose a robust control strategy by regulating initial cell population density and blasticidin concentration. These insights advance the design of robust population control circuits, with broad implications for synthetic biology and cell therapy.

For high-dimensional heterogeneous Kuramoto oscillator networks (HDHKONs) evolving in continuous time, achieving global synchronization can be exceedingly challenging, and in many cases, it may even appear impossible for various network configurations, including Platonic solids and Archimedean solids, or large-scale networks. Herein, a pinning impulsive control approach for HDHKONs is developed to attain global synchronization on the unit sphere without imposing constraints on initial phase distributions. With this approach, the entire network is globally stabilized onto an objective trajectory by impulsively controlling only a small fraction of oscillators. Furthermore, several synchronization criteria are provided to ensure that the global synchronization procedure is successful. These criteria are easily verified and shed light on the interplay among network parameters, the percentage of controlled oscillators, impulsive intensity, and impulsive frequency. Finally, two examples are implemented to validate the theoretical results.

This study investigates the semi-global fixed-time stability (SGFTS) and global fixed-time stability (GFTS) of nonlinear impulsive systems (NISs). A key challenge in analyzing the SGFTS of such systems lies in the evolving integration methods caused by the impulses. To address this, we dynamically partition the semi-global attraction set (SGAS) and solve the corresponding differential equations within each subset. Additionally, by constructing the transition dynamics of impulse points and iteratively computing these points, we establish the conditions for SGFTS under both stabilizing and destabilizing impulses. For GFTS, the primary difficulty arises from the distinct trajectories and dynamics of points located inside and outside the SGAS. To overcome this, we introduce the concept of the maximum-minimum impulse interval and derive a sufficient condition that ensures the system can enter the SGAS from a distance under a finite number of impulses. Furthermore, we develop a criterion for GFTS under varying impulse degrees and provide convergence time estimation based on the research on SGFTS of NIS. Finally, numerical examples are presented to validate the theoretical results. Notably, in Example 3, a fixed-time impulse controller is designed based on the proposed theoretical framework to achieve global stabilization of complex systems. This example highlights the potential applications of this work in the field of control.

This article addresses the fixed-time leaderless cluster synchronization of spatiotemporal community networks (SCNs) characterized by nonidentical node dynamics and reaction-diffusion feature. First, a signed SCN with reaction-diffusion effect is formulated, where the sign-based coupling is introduced to capture the dynamics of coopetition interactions among different communities. Second, to ensure the invariance of the synchronous manifold, an improved interdegree balance condition is proposed as a prerequisite for achieving cluster synchronization of the community network. Third, based on the local state information from adjacent nodes within each community, a time-limited controller is designed to enhance intracommunity coordination while avoiding the adverse effects of intercommunity competition on synchronization. Subsequently, with the help of the matrix decomposition technique and a Lyapunov-like method, several flexible leaderless cluster synchronization criteria are derived by establishing a nontrivial integral inequality and key properties of the intracommunity Laplacian matrix. Finally, the theoretical results are substantiated through a numerical example.

We study the synchronization of delayed reaction-diffusion neural networks (RDNNs) with Neumann boundary conditions, considering both distributed and discrete delays. Particularly, boundary sampled-data (SD) control is proposed to synchronize delayed RDNNs. In the proposed synchronization strategy, boundary SD control is based on boundary and distributed SD measurements. Based on the Lyapunov stability theory and inequality techniques, some synchronization criteria via the boundary SD control are proposed for delayed RDNNs. The boundary SD control gains are obtained by solving the conditions with linear matrix inequalities. Finally, a numerical example is presented to demonstrate the feasibility and effectiveness of the proposed method.

This article deals with the bipartite synchronization in coupled fractional-order reaction-diffusion neural networks (CFRNN) with multiple state or multiple spatial diffusion couplings. A bipartite synchronization condition is derived for the multiple state CFRNN (MSCFRNN) with the help of the Lyapunov functional combined with inequality techniques, and an adaptive state-feedback control scheme is also devised to ensure the bipartite synchronization of the MSCFRNN. Similarly, several sufficient conditions are also given to guarantee the bipartite synchronization in the multiple spatial diffusion CFRNN (MSDCFRNN). Finally, the effectiveness of the devised control strategies is demonstrated through two numerical examples.

This article aims at the leader-following secure consensus problem of nonlinear multiagent systems (MASs) with switching topologies, where the agents are not only suffered from the aperiodic malicious denial-of-service (DoS) attacks but also affected by instantaneous disturbance from the external environment. Due to the existing challenge of instantaneous disturbance about occurrence time being unknown, the impulsive-based switching network structure is put forward to tackle the impact of external instantaneous disturbance on MASs. Then, a novel hybrid event-triggered and impulsive control protocol is developed to guarantee that nonlinear MASs can resist DoS attacks and achieve the consensus control objective. Contrasted with the methods of continuous control, the developed hybrid event-triggered and impulsive control protocol using the discontinuous sampled state has certain merits saving control resources. Based on the Lyapunov theory, the stability of the closed-loop system is proven, and the Zeno behavior can be excluded successfully. An example is supplied to elicit the availability of the presented methodology.

Multistate coupled coupled neural networks (MSCCNN) and multiderivative coupled coupled neural networks (MDCCNN) are introduced in this article, and the lag outer synchronization for these two networks are tackled. First, a lag outer synchronization criterion for MSCCNN is derived using a node-based adaptive event-triggered control scheme, and the fact that the Zeno behavior does not exist is also proved. Moreover, the edge-based adaptive event-triggered control method is also utilized to address the lag outer synchronization for MSCCNN, and the existence of Zeno behavior is ruled out. In addition, two lag outer synchronization criteria for MDCCNN are given on the basis of the node- and edge-based adaptive event-triggered control strategies, and the nonexistence of Zeno behavior is also established. Finally, two examples are provided to demonstrate the feasibility of the proposed control schemes.

This article is concerned with passivity and synchronization for fuzzy coupled reaction-diffusion neural networks (FCRDNNs) with multistate couplings or multiple spatial-diffusion couplings. First, by utilizing an adaptive state feedback controller, several passivity criteria for the FCRDNNs with multistate couplings are obtained. Moreover, a sufficient condition to guarantee the synchronization for the multistate coupled FCRDNNs is also given on the basis of the devised adaptive state feedback control scheme. Additionally, the problems of passivity and synchronization are also addressed for the FCRDNNs with multiple spatial-diffusion couplings by employing the adaptive control technique and Lyapunov functional method. Finally, two numerical examples are presented to validate the effectiveness of the devised adaptive control schemes.

This article focuses on the issues of security sampled-data-based$H_{\infty }$control design for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems against false-data injection (FDI) attacks and external disturbances using a supervision of machine learning (SML) algorithm. To improve the bounds of sampled data period, an SML algorithm is proposed to optimize the allocation of network resources and ensure the effective utilization of bandwidth. Besides, two improved integral inequalities are introduced to estimate the integral quadratic terms that account for signal transmission delays and sampling information. Meanwhile, an appropriate asymmetric looped Lyapunov-Krasovskii functional (ALLKF) is constructed incorporating information on the fuzzy membership function (FMF), aperiodic sampling pattern, and transmission delays to facilitate model conservativeness. Next, based on the constructed fuzzy ALLKF along with the proposed inequalities, a set of necessary conditions for achieving asymptotic stability with$H_{\infty }$performance is derived through linear matrix inequalities (LMIs), and then control parameters can be obtained to guarantee the less conservative stabilization results of considered closed-loop systems. Finally, to verify the superiority and feasibility of the proposed theoretical observations, the hacked permanent magnet synchronous generator (PMSG)-based wind energy system (WES) is numerically validated. Additionally, comparative examples are presented to demonstrate the improved conservativeness of the proposed technique.

This article proposes a novel fuzzy secure containment control strategy in an adaptive framework for fully heterogeneous nonlinear multi-agent systems (FHNMASs), which can resist the combined impacts of randomly switching topologies and denial-of-service (DoS) attacks. Unlike existing containment control protocols that rely on multiple leaders sharing the same system matrices, our approach is applicable to more general systems, where multiple leaders and followers are allowed to be equipped with non-identical system matrices and even state dimensions. The switching signal of communication graphs is regulated by the random Markov process, and the connectivity assumption is relaxed by merely requiring the union graph of possible subgraphs to be connected. Firstly, new adaptive observers are constructed, that can observe leaders' states without access to global topology information, even under randomly switching topologies and DoS attacks. Secondly, based on fuzzy logic systems and output regulation methods, fuzzy system state estimators are introduced to address challenges associated with unknown nonlinear functions and unmeasurable states of followers. Further, a new fuzzy controller is developed to guarantee the achievement of flexible secure output containment, where containment coefficients no longer depend on the Laplacian matrix and can be flexibly preset to accommodate various tasks. Finally, the theoretical algorithm is validated through simulation results.

This study combines the fuzzy logic and the data-driven technology to solve the reinforcement learning-based sliding mode control problem of interconnected nonlinear systems with unmodeled dynamics. By assigning cost functions associated with the sliding-mode function for all auxiliary subsystems, the original control problem is equivalently converted into designing a group of optimal control policies updating in a self-triggered manner. To derive the optimal policies, a single-critic network architecture under the framework of reinforcement learning is constructed. Meanwhile, a fuzzy logic-based control policy is designed to handle the dynamical uncertainty issue aroused from the unmodeled dynamics. Furthermore, a data-driven method is used to reconstruct the unknown system dynamic through a three-layer neural network. Eventually, effectiveness and superiority of the proposed control strategy are demonstrated via experiments on optimal control of an interconnected two-stage chemical reactor system.

This paper provides theoretical analysis of the fixed-time multi-almost-periodicity in switched fuzzy neural networks, employing multi-controller strategies and a state-dependent switching mechanism. Utilizing the Ascoli-Arzela theorem, the properties of$M$-matrix, Lyapunov functions method, and some inequality techniques, we establish some sufficient conditions to ascertain that the number of exponentially stable almost-periodic solutions can be up to$4^{n}$, where$n$is the number of neurons. Furthermore, we design various controllers to achieve the fixed-time stability for various almost-periodic solutions located in the positive invariant sets. Then, the settling time for the switched fuzzy networks to achieve multi-almost-periodicity is estimated. It is noteworthy to state that this paper considers fixed-time multiperiodicity and fixed-time multistability as special cases of fixed-time multi-almost-periodicity. Two numerical examples are presented to demonstrate the theoretical results.

For nonlinear coupled ODE-PDE systems with stochastic actuator failures, this article introduces an intermittent sampled-data fuzzy (ISDF) control under spatially local averaged measurements (LAMs). To precisely characterize the nonlinear coupled ODE-PDE system, a Takagi-Sugeno fuzzy model is firstly introduced. Second, considering the stochastic actuator failures caused by Markov jump, an ISDF control strategy is developed for the nonlinear coupled ODE-PDE system under spatially LAMs, which only demands a minimal number of sensors and actuators. Then, through constructing a time-dependent switched Lyapunov functional, the stochastically exponential stability conditions are given in terms of linear matrix inequalities (LMIs) and ISDF control gains are obtained by solving these LMIs. Finally, the effectiveness of the developed approach is validated through its application to hypersonic rocket car (HRC) control.

This study focuses on the problem of multiunmanned aerial vehicle cooperative pursuit in complex obstacle environments and proposes a lightweight cooperative pursuit method with safety guarantees. By incorporating obstacle motion dynamics, an enhanced control barrier function constraint framework is constructed, effectively overcoming the limitations of traditional approaches that consider only geometric collision avoidance in dynamic environments. To enable the simultaneous pursuit of multiple evading targets, a hybrid task allocation strategy is designed without requiring complex optimization solvers. Building upon this strategy, a distributed quadratic optimization problem is formulated, aiming to minimize control input variations under safety constraints. A neurodynamic approach is employed to solve this optimization problem in real time, thereby achieving efficient and safe control during the cooperative pursuit process. Subsequently, the stability and safety of the closed-loop system are theoretically analyzed. Finally, extensive simulations and physical experiments demonstrate the effectiveness and practicality of the proposed method in multitarget cooperative pursuit tasks.

The distributed set-membership filtering is investigated for linear discrete systems with incomplete measurements. A new ellipsoid-based event-triggered mechanism is proposed, which utilizes the estimation ellipsoid to design the triggered condition and reduces the transmission frequency of the estimator state and the shape matrix. Then, a distributed filter based on the event-triggered mechanism and incomplete measurements is constructed, and a sufficient condition of the feasibility of the filter is derived using mathematical induction and set theory. The desired filter gains are obtained by addressing a series of constrained optimization problems. Furthermore, a fusion algorithm is introduced to reconstruct more accurate estimation information. The constraint of the measurement noise is then updated online based on the measurement and the reconstructed estimation information. Finally, the validity of the proposed method is verified by a simulation example.

The machine learning-based model is a promising paradigm for predicting invasive disease events (iDEs) in breast cancer. Feature selection (FS) is an essential preprocessing technique employed to identify the pertinent features for the prediction model. However, conventional FS methods often fail with imbalanced clinical data due to the bias towards the majority class. In this paper, a novel FS framework based on reinforcement learning (RLFS) is developed to identify the optimal feature subset for the imbalanced data. The RLFS employs an iterative methodology, wherein data resampling technique generates a balanced dataset before each iteration. A decision network is trained using a deep RL algorithm to identify the relevant features for the dataset in the current iteration. With such an iterative training strategy, numerous constructed datasets gradually boost the FS capacity of the decision network, resulting in a robust performance for imbalanced data. Finally, a weighted model is proposed to determine the most suitable FS solution. The RLFS is employed to predict breast cancer iDEs using real follow-up data. The comparison results demonstrated that RLFS effectively reduces the number of features while outperforming several state-of-the-art FS algorithms.

This study aims to solve the mean-square asymptotic synchronization problem of additive time-varying delayed Markovian jumping neural networks (ATVMJNNs) under the framework of multiple stochastic samplings and its direct application in secure image encryption (SIE). To do this, first, we assume the existence of multiple sampled data periods that satisfy a Bernoulli distribution and introduce random variables to represent the positions of input delays and sampling periods. Then, based on these assumptions, we develop a mode-dependent discontinuous Lyapunov-Krasovskii functional (DLKF) to reduce model conservatism. Next, we introduce a new auxiliary slack-matrix-based integral inequality (ASMBII) to approximate the integral quadratic terms arising from the derivative of the DLKFs. Furthermore, we develop a multiple stochastic sampling framework to achieve asymptotic synchronization between the primary and secondary systems, and less conservative criteria for asymptotic stability in the mean square sense of the error model are derived by solving a set of linear matrix inequalities (LMIs). Finally, we present the numerical validations and corresponding experimental results in a pragmatic application of image processing to demonstrate the benefits of the proposed algorithms and techniques. From both numerical and practical results, the proposed algorithms and techniques can yield superior performance compared to existing studies.

Phase-change memory (PCM) is a novel type of nonvolatile memory and is suitable for artificial neural synapses. This article investigates the Lagrange global exponential stability (LGES) of a class of PCNNs with mixed time delays. First, based on the conductivity characteristics of PCM, a piecewise equation is established to describe the electrical conductivity of PCM. By using the proposed piecewise equation to simulate the neural synapses, a novel PCNN with discrete and distributed time delays is proposed. Then, using comparative theory and fundamental inequalities, the LGES conditions based on the M-matrix are proposed in the sense of Filippov, and the exponential attractive set (EAS) is obtained based on M-matrix and external input. Moreover, the Lyapunov global exponential stability (GES) conditions of PCNNs without external input are obtained by using the inequality technique and eigenvalue theory, which is a form of M-matrix. Finally, two simulation examples are given to verify the validity of the obtained results.

In recent years, bidirectional convolutional recurrent neural networks (RNNs) have made significant breakthroughs in addressing a wide range of challenging problems related to time series and prediction applications. However, the performance of the models is highly dependent on the hyperparameters chosen. Hence, we propose an automatic method for hyperparameter optimization and apply a bidirectional convolutional RNN based on the improved swarm intelligence optimization (sparrow search) to solve regression prediction problems. Specifically, a parallel multiscale dilated convolution (PMDC) module was designed to capture both local and global spatial correlations. This method utilizes convolution with different dilation rates to expand the receptive field without increasing the complexity of the model. Meanwhile, it integrates parallel multiscale structures to extract features at different scales and enhance the model's understanding of the input data. Then, the bidirectional gated recurrent units (BGRUs) learn temporal information from the convolutional features. To address the limitations of empirical hyperparameter selection, such as slow training and low efficiency, a novel PMDC-BGRU model integrated with a pretrained sparrow search algorithm (SSA) was proposed for hyperparameter optimization. Finally, experiments on multiple datasets verified the superiority of the algorithm and explained the flexibility of intelligent optimization algorithms in solving model parameter optimization.

This article investigates a class of systems of nonlinear equations (SNEs). Three distributed neurodynamic models (DNMs), namely a two-layer model (DNM-I) and two single-layer models (DNM-II and DNM-III), are proposed to search for such a system's exact solution or a solution in the sense of least-squares. Combining a dynamic positive definite matrix with the primal-dual method, DNM-I is designed and it is proved to be globally convergent. To obtain a concise model, based on the dynamic positive definite matrix, time-varying gain, and activation function, DNM-II is developed and it enjoys global convergence. To inherit DNM-II's concise structure and improved convergence, DNM-III is proposed with the aid of time-varying gain and activation function, and this model possesses global fixed-time consensus and convergence. For the smooth case, DNM-III's globally exponential convergence is demonstrated under the Polyak-Łojasiewicz (PL) condition. Moreover, for the nonsmooth case, DNM-III's globally finite-time convergence is proved under the Kurdyka-Łojasiewicz (KL) condition. Finally, the proposed DNMs are applied to tackle quadratic programming (QP), and some numerical examples are provided to illustrate the effectiveness and advantages of the proposed models.

This article deals with linear equations of the form $Ax = b$ . By reformulating the original problem as an unconstrained optimization problem, we first provide a gradient-based distributed continuous-time algorithm over weight-balanced directed graphs, in which each agent only knows partial rows of the augmented matrix $(A\; b)$ . The algorithm is also applicable to time-varying networks. By estimating a right-eigenvector corresponding to 0 eigenvalue of the out-Laplacian matrix in finite time, we further propose a distributed algorithm over weight-unbalanced communication networks. It is proved that each solution of the designed algorithms converges exponentially to an equilibrium point. Moreover, the convergence rate is given out clearly. For linear equations without solution, these algorithms are used to obtain a least-squares solution in approximate sense. These theoretical results are illustrated by four numerical examples.

As a pivotal subfield within the domain of time series forecasting, runoff forecasting plays a crucial role in water resource management and scheduling. Recent advancements in the application of artificial neural networks (ANNs) and attention mechanisms have markedly enhanced the accuracy of runoff forecasting models. This article introduces an innovative hybrid model, ResTCN-DAM, which synergizes the strengths of deep residual network (ResNet), temporal convolutional networks (TCNs), and dual attention mechanisms (DAMs). The proposed ResTCN-DAM is designed to leverage the unique attributes of these three modules: TCN has outstanding capability to process time series data in parallel. By combining with modified ResNet, multiple TCN layers can be densely stacked to capture more hidden information in the temporal dimension. DAM module adeptly captures the interdependencies within both temporal and feature dimensions, adeptly accentuating relevant time steps/features while diminishing less significant ones with minimal computational cost. Furthermore, the snapshot ensemble method is able to obtain the effect of training multiple models through one single training process, which ensures the accuracy and robustness of the forecasts. The deep integration and collaborative cooperation of these modules comprehensively enhance the model's forecasting capability from various perspectives. Ablation studies conducted validate the efficacy of each module, and through multiple sets of comparative experiments, it is shown that the proposed ResTCN-DAM has exceptional and consistent performance across varying lead times. We also employ visualization techniques to display heatmaps of the model's weights, thereby enhancing the interpretability of the model. When compared with the prevailing neural network-based runoff forecasting models, ResTCN-DAM exhibits state-of-the-art accuracy, temporal robustness, and interpretability, positioning it at the forefront of contemporary research.

This article presents two innovative matrix neurodynamic approaches (MNAs) designed to tackle the rank minimization problem. First, by introducing the matrix norm-normalized sign function, two variants of MNA are developed: finite-time converging MNA (FINt-MNA) and fixed-time converging MNA (FIXt-MNA). Then, the proposed approaches are shown to guarantee the existence and uniqueness of solutions, and based on Lyapunov analysis, it is demonstrated that the proposed approaches converge to the optimal solution within FINt and FIXt. In addition, upper bounds on the settling time are determined using finite-time and fixed-time lemmas, with subsequent analysis examining the influence of tunable parameters on these bounds for the two approaches through the control variable method. Finally, numerical examples and an image completion experiment confirm the effectiveness and superiority of the proposed approaches compared with the existing MNA and two classical approaches.

Aiming at the situation where the measurement matrix B has a flexible block decomposition, this article designs two novel distributed continuous- and discrete-time projection neurodynamic approaches to solve the basis pursuit (BP) problem for sparse recovery. These approaches only require information from each flexible block of the measurement matrix B, rather than from each row, column, or the entire matrix. First, with the aid of the primal-dual dynamical approach, projection operator, and second-order multiagent consensus condition, a novel distributed projection neurodynamic approach in continuous time (DPNA-CT-B) is proposed, and its optimality and global asymptotic stability are rigorously proved. Moreover, based on the forward and backward Euler methods and variable substitution methods, a corresponding distributed projection neurodynamic approach in discrete time (DPNA-DT-B) is designed. Finally, through sparse signal and image reconstruction experiments, the effectiveness and superiority of the proposed neurodynamic approaches are verified.

This article introduces an integrated guidance and control method for morphing flight vehicles, addressing model uncertainties and external disturbances through a robust deep reinforcement learning framework built on sliding-mode control (SMC). The method development begins with the establishment of a longitudinal guidance and control model and a detailed introduction to the necessary theoretical foundations. The proposed approach incorporates robust observation strategies enabled by a novel fixed-time SMC design. The agent’s actions, rewards, neural network structure, and training process are meticulously crafted to tackle practical guidance and control challenges effectively. Trained offline to achieve seamless integration of position and attitude control, the agent generates end-to-end control commands in real time during online operation. Extensive testing, including robustness evaluations, generalization assessments, and comparative performance analyses, demonstrates the superiority and reliability of the proposed method.

Discovering and tracking wildlife targets are essential for gaining insights into the behavioral patterns and habits of animals within their natural habitats. With low cost and high maneuverability, mini autonomous aerial vehicles (AAVs) can achieve robust and rapid locating and tracking of multiple targets through collaboration. This work proposes a method for multitarget task allocation and path planning for AAV swarms, addressing the challenges of locating and tracking multiple wildlife with very high frequency (VHF) radio tags while avoiding potential disturbances to the wildlife. Our approach proposes a layered framework for the multi-AAV multitarget wildlife tracking problem: 1) the state estimation layer performs fast receiver signal strength indicator (RSSI) signal acquisition and employs the particle filtering algorithm to localize targets’ positions; 2) the task assignment layer uses a quadratic allocation method for AAVs’ real-time target allocation, starting with reasonable initial target sets via mixed-integer programming and efficiently readjusting targets based on real-time environment; and 3) the motion planning layer introduces an optimization-based approach to generate smooth and executable trajectories that can simultaneously ensure desired safe distances from objects of interest. Simulation experiments validate the effectiveness of the obtained AAV swarm tracking scheme.

This article copes with the distributed control and target tracking (DCTT) problem in general linear and Lipschitz multiagent systems (MASs). In comparison to the traditional DCTT algorithms that were developed for MASs in ideal conditions, two schemes based upon a resilient protocol are proposed for linear and nonlinear MASs to estimate and track a mobile target where all agents are subject to composite attacks, including camouflage attacks, DoS attacks, sensor attacks, and actuator attacks. Based on the digital twin approach, a twin layer (TL) with high privacy and security is introduced to separate the problem of DCTT into two tasks: 1) handling DoS attacks on the TL and defending against sensor and 2) actuator attacks on the cyber-physical layer (CPL). First, two distributed estimation algorithms are established to reconstruct the agents and target dynamics for every agent on the TL in the presence of DoS attacks. Second, using the reconstructed agents and target dynamics on the TL, a resilient distributed control protocol is designed to resist sensor and actuator attacks on the CPL. The current scheme guarantees the achievement of control and target tracking such that the DCTT error of the proposed design is ultimately bounded in terms of linear matrix inequality. By applying two simulation examples, the presented algorithms are also validated.

In this article, the bipartite consensus problem of multiagent systems on time scales under switching topologies is investigated. A dynamic event-triggered control strategy is designed to reduce the number of triggers. Sufficient condition to guarantee the consensus is obtained by constructing a non-negative function and combining with the theory of time scale calculus. In addition, it is proved through a categorical discussion that the entire triggering sequence determined by the switching topology and the triggering function does not display Zeno behavior. Lastly, to confirm that the theoretical results are feasible, a numerical simulation and an application to spacecraft formation flight are provided.

Phase-change memory (PCM) is a novel type of nonvolatile memory and offers low power consumption, high integration, and significant plasticity, making it suitable for neural synapses. In this article, we investigate the global exponential stabilization (GES) of phase-change inertial neural networks (PCINNs) with discrete and distributed time-varying delays. Initially, a piecewise equation is established to model the electrical conductivity of PCM. Based on this, we use PCM to simulate neural synapses, and a class of PCINNs with discrete and distributed time-varying delays is formulated. A continuous state feedback controller is designed to obtain the${\boldsymbol {\rho} {\textrm {th}}({\rho \ge 1})}$moment GES conditions of PCINNs in the Filippov sense by using differential inclusion theory, comparison strategies, and inequality techniques. Additionally, the global exponential stability conditions of phase-change Hopfield neural networks are obtained, expressed in the form of an M-matrix. Finally, three simulation examples are provided to verify the effectiveness of the theoretical results.

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This article aims to handle a class of general composite optimization problems over multiagent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbedProx-DBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGAandLSVRG) intoProx-DBRO-VRto designProx-DBRO-SAGAandProx-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that bothProx-DBRO-SAGAandProx-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sublinearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A tradeoff between convergence accuracy and Byzantine resilience in both linear and sublinear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine learning problem under various Byzantine attacks.

This article focuses on multiagent distributed asynchronous optimization over directed networks where each agent can only access its individual local function, and the aggregate aim is to minimize the cumulative sum of all local functions. Considering the asynchrony among the agents, we develop an algorithm in which agents compute and communicate individually, without any form of synchronized coordination. Agents perform their local updates by local communication with their immediate neighbors, and this may involve the use of stale information. Since asynchrony naturally leads to latency or packet loss, an asynchronous robust gradient tracking mechanism is developed to guarantee estimating the average of agents’ gradients precisely. Moreover, it employs uncoordinated step-sizes which are more flexible and general than constant or decaying step-size. When the global objective is strongly convex and the local objectives have Lipschitz-continuous gradients, we prove that each agent executing the asynchronous algorithm linearly converges to the consensus optimal point at an$\mathcal {O}(\lambda ^{k})$rate, where$\lambda \in (0,1)$is convergence factor and k represents the iteration number, with a step satisfying a tight explicit upper bound. Numerical experiments demonstrate that our algorithm has better advantages over the state-of-the-art asynchronous algorithms.

This article addresses the disturbance decoupling problem (DDP) of probabilistic Boolean networks (PBNs). Existing research on the DDP of Boolean networks (BNs) has predominantly concentrated on deterministic BNs (DBNs), which inadequately model the complexities of real-world systems, particularly those exhibiting stochastic behavior and uncertainty. We introduce two definitions tailored to PBNs: disturbance decoupling in distribution and disturbance decoupling with probability one. Utilizing an algebraic representation framework, we derive algebraic conditions for both types of disturbance decoupling. Additionally, we employ a vertex partitioning approach to establish graphical conditions pertinent to these disturbance decoupling types. Our investigation further reveals the connections between DBNs and PBNs concerning disturbance decoupling. To demonstrate the effectiveness of our theoretical results, we present three examples.

This article addresses the adaptive iterative learning formation control (AILFC) problem of multiagent systems (MASs) with unmeasurable state subject to Denial-of-Service (DoS) attacks. To alleviate DoS attacks, a neural network (NN)-based compensation mechanism is proposed to learn communication signals, and a learning-based distributed output observer is designed to estimate the leader output. Moreover, an improved extended state observer (ESO) is designed to deal with unmeasurable states and total disturbance. Then, a time-varying boundary layer method with a normalized function is constructed to address the initial error problem. Furthermore, a multiple observer-based AILFC scheme is developed via the backstepping control technique, and the stability analysis of MASs is given by the Lyapunov theory. Finally, a simulation example is shown to illustrate the effectiveness of the developed AILFC algorithm.

In this article, the predefined-time (PdT) control of the switched neutral Filippov system (SNFS) is studied. Semi-intermittent-type and completely intermittent-type PdT stability lemmas are initially established. The indefinite function, united exponent parameter and intermittent time condition are adopted. The new completely intermittent-type PdT stability lemma shows that intermittent interval is related to the PdT parameter, and semi-intermittent-type one is not. Such results are new and some previous PdT stability lemmas are improved greatly. As an application, the SNFS is proposed, which is more generalized. Under the semi/completely intermittent time conditions framework, by designing discontinuous control strategy and bounded control strategy, the zero solution of the considered SNFS is PdT stable. For the PdT control, the control input constraint is considered for the first time. Finally, a numerical example and simulations are provided to illustrate the correctness of the main results.

This article is dedicated to a fault-tolerant synchronization control method of complex networks (CNs) via an average observer. A projected operator matrix is constructed to reduce the dimension of CNs by clustering and aggregation. An average observer with derivative and integral terms is built to estimate unknown elements with high estimation accuracy. The fault-tolerant controller is a composite form of state-feedback and the estimated faults. Sufficient criterions are set up to guarantee the uniformly ultimately bounded synchronization of the resultant close-loop system. Finally, an example is simulated to deliver the effectiveness of the proposed approach.

This article aims to study event-triggered data-driven control of nonlinear systems via Q-learning. An input-output mapping is described by a pseudo-partial derivatives form. A Q-learning-based optimization criterion is provided to establish a data-driven control law. A dynamic penalty factor composed of tracking errors is supplied to accelerate errors convergence. Consequently, a novel triggering rule related to this factor and performance cost is proposed to save communication resources. Sufficient conditions are developed for guaranteeing the ultimately uniform boundedness of the resultant tracking errors system. Two simulation studies are executed to verify the effectiveness of the presented scheme.

In this article, two kinds of dynamic event-triggered lag bipartite consensus (LBC) control problems for a linear multiagent system (MAS) with structurally balanced signed directed graph are tackled, that is, the cases with known agent state and with unknown agent state. For the former, based on an additional internal dynamic variable and the parametric Lyapunov equation (PLE), a dynamic event-triggered state-feedback control (DETSFC) protocol is devised, which not only does not exhibit the Zeno behavior but also can guarantee that the MAS realizes the LBC. Similarly, we further utilize the state observer and the dynamic event-triggered control (DETC) method to address the LBC for the MAS with unknown agent state. Finally, the double-integrator MAS and the linearized reduced-order aircraft are used to verify the advantages of the obtained theoretical results.

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations.

This article investigates the almost sure consensus (ASC) problem for sampled-data multiagent systems (MASs) operating over semi-Markov switching networks (SMSNs) and facing different types of denial-of-service (DoS) attacks. During real-time information exchange among agents, communication failures between agents occur randomly, which may result in each possible network topology occurring with a certain probability, and its sojourn time is also stochastic. This necessitates the consideration of a more general switching signal to describe the stochastic switching phenomenon of networks. In pursuit of this goal, a semi-Markov chain is introduced to characterize the switching signal of stochastic interaction networks, whose sojourn time distribution allows for arbitrary continuous-time distribution and depends on the current and next state. Additionally, this article delves into the impact of two distinct types of DoS attacks on MASs. The first type involves random DoS attacks, which are also modeled by a semi-Markov chain to capture the stochastic nature of attack durations. The second type is deterministic DoS attacks, characterized by their frequency and duration. The proposed new stochastic analysis method, based on the law of large numbers, is used to analyze the ASC for MASs featuring SMSNs under the DoS attacks. The effectiveness of the proposed approach is demonstrated by evaluating the results obtained from two illustrative numerical examples.

This article deals with the optimal super-ellipse formation tracking control problem for multiple unmanned vehicles (MUVs), where each vehicle contains nonlinear uncertainties of unmodeled basic resistance, and the objective of energy optimization includes the super-ellipse orbit tracking energy and formation motion energy on the normal and tangent directions along the super-ellipse orbits, respectively. The communication topology is the directed leader-following structure. To avoid using the inputs of neighboring MUVs and the global communication information, a novel augmented formation input is designed and integrated into the formation motion subsystem. To deal with the uncertain nonlinearity, the uncertain virtual leader information, and the limited information of neighboring MUVs in the Hamilton-Jacobi–Bellman equations, a simplified reinforcement learning (RL) energy optimization method is designed based on identifier neural networks (NNs) and optimized backstepping technique. Theoretical stability analysis of system errors are given in detail. Simulation results show that the super-ellipse formation tracking energy consumption is significantly saved and the algorithm run time is decreased through comparison.

The secure consensus problem is addressed for multiagent systems (MASs) suffering from saturated control input and denial-of-service (DoS) attacks. The communication networks’ open setting and sharing nature give rise to security issues and impact the performance of MASs. Malicious DoS attacks attempt to disrupt the information exchange and undermine consensus by compromising the availability of transmitted data. Moreover, the control input can be saturated as a result of physical device limitations or safety concerns. To tackle these challenges, a state-prediction-based dynamic event-triggered mechanism (DETM) control protocol is designed to guarantee the secure consensus of MASs while reducing redundant communication, avoiding continuous monitoring of adjacent states, and ensuring effective utilization of limited bandwidth resources. Zeno behavior is eliminated by confirming the existence of a positive lower bound on interevent intervals. Sufficient conditions are established for the co-design of the DETM and controller to accomplish the desired goal. Finally, a simulation is conducted to substantiate the effectiveness and validity of the proposed control protocol.

The global polynomial synchronization (GPS) is investigated for memristive competitive neural networks (MCNNs) with proportional delays and uncertain parameters. First, via drawing support from differential inclusion theory and adopting the time-variant state feedback controllers, their error systems are synthesized into a vector form MCNNs. Then, a new proportional delay differential inequality (PDDI) is established through norm definition and Lipschitz condition for the vector form MCNNs mentioned-above. Several algebraic forms of synchronization criteria for the system proposed are achieved by applying the new PDDI. Each of these criteria is represented by only one inequality, rather than in the form of components, which facilitates validation. Compared to the commonly used method for constructing Lyapunov functionals in studying MCNNs, it is more concise. Ultimately, numerical examples are used to inspect the results acquired, and the GPS of one example is applied in image encryption and decryption. The empirical outcomes demonstrat the efficacy of the employed synchronization control approach, exhibiting robust resilience and heighten security in secure communication applications.

In recent years, there has been a significant advancement in memristor-based neural networks, positioning them as a pivotal processing-in-memory deployment architecture for a wide array of deep learning applications. Within this realm of progress, the emerging parallel analog memristive platforms are prominent for their ability to generate multiple feature maps in a single processing cycle. However, a notable limitation is that they are specifically tailored for neural networks with fixed structures. As an orthogonal direction, recent research reveals that neural architecture should be specialized for tasks and deployment platforms. Building upon this, the neural architecture search (NAS) methods effectively explore promising architectures in a large design space. However, these NAS-based architectures are generally heterogeneous and diversified, making itchallenging for deployment on current single-prototype, customized, parallel analog memristive hardware circuits. Therefore, investigating memristive analog deployment that overrides the full search space is a promising and challenging problem. Inspired by this, and beginning with the DARTS search space, we study the memristive hardware design of primitive operations and propose the memristive all-inclusive hypernetwork that covers 2×1025 network architectures. Our computational simulation results on 3 representative architectures (DARTS-V1, DARTS-V2, PDARTS) show that our memristive all-inclusive hypernetwork achieves promising results on the CIFAR10 dataset (89.2% of PDARTS with 8-bit quantization precision), and is compatible with all architectures in the DARTS full-space. The hardware performance simulation indicates that the memristive all-inclusive hypernetwork costs slightly more resource consumption (nearly the same in power, 22%∼25% increase in Latency, 1.5× in Area) relative to the individual deployment, which is reasonable and may reach a tolerable trade-off deployment scheme for industrial scenarios.

This article studies the optimized adaptive finite-time consensus control issue for stochastic nonlinear multiagent systems subject to non-affine nonlinear faults. Under the architecture of the adaptive optimized backstepping method, this article develops the neural-network-based simplified reinforcement learning algorithm with an identifier-critic-actor structure, where the identifier, critic and actor are put forward to estimate unknown dynamics, evaluate system performance and implement control behavior, respectively. Then, the Butterworth low-pass filter is introduced to compensate for the adverse effects brought by non-affine nonlinear faults. Furthermore, it is verified by Itô differential equation and the finite-time theory that the closed-loop system is semi-global finite-time stable in probability. Finally, the effectiveness of the control algorithm is illustrated by simulation examples.Note to Practitioners—This paper was motivated by the problem of finite-time convergence is one of significance performance index in many practical application. For systems with high transient performance standards, such as robotic systems, manipulator systems and unmanned aerial systems, finite time convergence is of practical importance. Accordingly, distinguished from the previous investigation results, this article develops the neural-network (NN)-based simplified reinforcement learning (RL) algorithm with an identifier-critic-actor structure, where the identifier, critic and actor are put forward to estimate unknown dynamics, evaluate system performance and implement control behavior, respectively. We believe that the novel research method will bring a research spring for the constrained systems. Preliminary simulation experiments suggest that this approach is feasible. In future research, we will address the fixed time control protocol designs for nonlinear multi-agent systems.

In this paper, we address the event-triggered consensus problem for double-integrator multi-agent systems with prescribed performance. First, we propose a unified event-triggered consensus framework that accommodates various performance specifications. Within this framework, we present a novel nonlinear control protocol that operates at intermittent time instants, utilizing relative positions and absolute velocities. These time instants are determined by a delicately designed edge-based event-triggering mechanism, while guaranteeing the prescribed performance specifications on relative positions. Furthermore, we extend the control protocol to encompass the guaranteed performance specifications for both relative positions and relative velocities. To eliminate the need for continuous monitoring of neighboring agents’ states, a unified self-triggered consensus framework with guaranteed performance is developed. With the proposed triggering mechanisms, no agent exhibits Zeno behavior. Finally, we validate the effectiveness of the proposed control protocols through an illustrative example.

This paper investigates a neuro-adaptive fixed-time tracking control issue for switched nonlinear systems subject to asymmetric time-varying constraints and unknown control gains. Unlike the current study on constraint problems, the system’s initial condition is unavailable in this article, which causes specific difficulties in constructing the Barrier Lyapunov Function. A novel shifting function is presented to unify the initial values of all system states. In addition, the system convergence time becomes known and adjustable by utilizing the Nussbaum gain technique and fixed-time stability criterion. An adaptive neural tracking control scheme is proposed based on the learning ability of neural networks and fixed-time theory. To alleviate the computational burden, we present the single learning parameter method such that the number of adaptive laws is reduced significantly. Furthermore, a novel switching threshold mechanism that considers the system errors is developed to balance the communication burden and control performance. Finally, the simulation example illustrates the feasibility of the proposed control strategy.

Low-light image enhancement aims to obtain a normal-light image by adjusting the illumination of a low-light image. The existing methods do not fully explore the prior information hidden in low-light images, which raises the problems of detail loss and color distortion. To alleviate these issues, we propose a multi-prior collaborative network (MPC-Net) with transformer for low-light image enhancement. It extracts the indispensable prior information to facilitate high-quality image enhancement. Specifically, a pre-trained high-level vision model is employed to extract coarse texture and structure, which is then refined through a proposed self-distillation module to obtain compact representation for texture and structure. Furthermore, we design a color branch consisting of negative residual blocks and a pyramid structure to solve for noise-free color prior, aiming to provide the enhancer with a modeling mechanism for color information. Finally, a transformer-based multi-prior fusion module is developed to aggregate the content and prior information. Extensive experiments show that the proposed MPC-Net achieves superior performance on three referenced datasets and four no-referenced datasets. Our code is available at: https://github.com/Shecyy/MPC-Net.

This work proposes a memory fusion controller design methodology for sampled-data control of fractional-order (FO) systems with sliding memory window. Composed of finite-dimensional previous inputs, the devised controller is capable of handling hereditary effect and meanwhile enabling pseudo state to satisfy general integer-order (IO) discrete plant at sampling instants. Additionally, the asymptotical stability of controller and sampling error are further guaranteed. The developed fusion controller provides an "out-of-the-box" method for users who are not familiar with FO calculus and significantly facilitates the corresponding analysis. The above mentioned approach is thereafter employed in a more sophisticated case, that is, the coordination control of FO multiagent systems (MASs) subjects to intermittent sampled-data transmission. It is proved that the achievement of consensus only relates to the connectivity of communication graph. Numerical results are presented finally to substantiate the proposed control strategy.

This article delves into the distributed resilient output containment control of heterogeneous multiagent systems against composite attacks, including Denial-of-Service (DoS) attacks, false-data injection (FDI) attacks, camouflage attacks, and actuation attacks. Inspired by digital twin technology, a twin layer (TL) with higher security and privacy is employed to decouple the above problem into two tasks: 1) defense protocols against DoS attacks on TL and 2) defense protocols against actuation attacks on the cyber-physical layer (CPL). Initially, considering modeling errors of leader dynamics, distributed observers are introduced to reconstruct the leader dynamics for each follower on TL under DoS attacks. Subsequently, distributed estimators are utilized to estimate follower states based on the reconstructed leader dynamics on the TL. Then, decentralized solvers are designed to calculate the output regulator equations on CPL by using the reconstructed leader dynamics. Simultaneously, decentralized adaptive attack-resilient control schemes are proposed to resist unbounded actuation attacks on the CPL. Furthermore, the aforementioned control protocols are applied to demonstrate that the followers can achieve uniformly ultimately bounded (UUB) convergence, with the upper bound of the UUB convergence being explicitly determined. Finally, we present a simulation example and an experiment to show the effectiveness of the proposed control scheme.

In this work, we consider the safe deployment problem of multiple robots in an obstacle-rich complex environment. When a team of velocity and input-constrained robots is required to move from one area to another, a robust collision-avoidance formation navigation method is needed to achieve safe transferring. The constrained dynamics and the external disturbances make the safe formation navigation a challenging problem. A novel robust control barrier function-based method is proposed which enables collision avoidance under globally bounded control input. First, a nominal velocity and input-constrained formation navigation controller is designed which uses only the relative position information based on a predefined-time convergent observer. Then, new robust safety barrier conditions are derived for collision avoidance. Finally, a local quadratic optimization problem-based safe formation navigation controller is proposed for each robot. Simulation examples and comparison with existing results are provided to demonstrate the effectiveness of the proposed controller.

The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this article, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking framework, two distributed algorithms are, respectively, designed over balanced and unbalanced graphs, where momentum terms and two time-scales are involved. Furthermore, it is demonstrated that the designed distributed algorithms attain linear speedup convergence rates provided that the momentum coefficients and the step size are appropriately selected. Finally, numerical simulations verify the effectiveness and the global accelerated effect of the designed algorithms.

This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.

This article is concerned about fixed-time (FT) synchronization of spatiotemporal networks (STNs) with the Robin boundary condition. Above all, a switching-type FT stability theorem and an integral inequality are established, which provide a novel theoretical tool for the rigorous analysis of FT control in STNs. Subsequently, three kinds of nontrivial power-law controllers are developed which are separately acted on the interior, the boundary, and the whole of the spatial domain. Based on these control schemes and Lyapunov-like method, several flexible criteria are obtained to achieve FT synchronization of STNs, and the upper bound of the synchronization time is explicitly estimated. Note that, the derived results here are also perfectly applicable to STNs with Neumann or Dirichlet boundary condition. Several illustrate examples are presented at final to confirm the developed controllers and criteria.

In this article, we investigate the lag consensus and lag$ \mathcal{H}_{\infty}$consensus problems for second-order nonlinear multiagent systems (MASs) by utilizing the proportional-derivative (PD) and proportional-integral (PI) control methods. On the one hand, a criterion is developed for ensuring the lag consensus of the MAS by choosing an appropriate PD control protocol. Moreover, a PI controller is also provided to guarantee that the MAS can achieve lag consensus. On the other hand, several lag$\mathcal{H}_{\infty}$consensus criteria are also given for the case in which external disturbances appear in the MAS; these criteria are developed by exploiting the PD and PI control strategies. Finally, the devised control schemes and the developed criteria are verified by employing two numerical examples.

For a nonlinear parabolic distributed parameter system (DPS), a fuzzy boundary sampled-data (SD) control method is introduced in this article, where distributed SD measurement and boundary SD measurement are respected. Initially, this nonlinear parabolic DPS is represented precisely by a Takagi-Sugeno (T-S) fuzzy parabolic partial differential equation (PDE) model. Subsequently, under distributed SD measurement and boundary SD measurement, a fuzzy boundary SD control design is obtained via linear matrix inequalities (LMIs) on the basis of the T-S fuzzy parabolic PDE model to guarantee exponential stability for closed-loop parabolic DPS by using inequality techniques and a acrlong LF. Furthermore, respecting the property of membership functions, we present some LMI-based fuzzy boundary SD control design conditions. Finally, the effectiveness of the designed fuzzy boundary SD controller is demonstrated via two simulation examples.

This article concentrates on solving the k -winners-take-all (k WTA) problem with large-scale inputs in a distributed setting. We propose a multiagent system with a relatively simple structure, in which each agent is equipped with a 1-D system and interacts with others via binary consensus protocols. That is, only the signs of the relative state information between neighbors are required. By virtue of differential inclusion theory, we prove that the system converges from arbitrary initial states. In addition, we derive the convergence rate as O(1/t) . Furthermore, in comparison to the existing models, we introduce a novel comparison filter to eliminate the resolution ratio requirement on the input signal, that is, the difference between the k th and (k+1) th largest inputs must be larger than a positive threshold. As a result, the proposed distributed k WTA model is capable of solving the k WTA problem, even when more than two elements of the input signal share the same value. Finally, we validate the effectiveness of the theoretical results through two simulation examples.

This article explores the fuzzy adaptive bipartite consensus problem of stochastic multiagent systems (MASs) using a singularity-free prescribed performance control (PPC) approach. When bipartite consensus errors approach constraint boundaries under the effect of adverse factors, the conventional PPC method may encounter a singularity issue, which can degrade system performance or lead to system instability. To address this issue, this article generalizes the concept of shear mapping to the PPC approach of stochastic MASs. Subsequently, a reference performance function is designed to guide the evolution trend of bipartite consensus errors, which effectively decreases the overshoot of bipartite consensus errors. Moreover, a scaling function is designed to remove the feasibility conditions in the existing PPC results. The proposed approach ensures that all signals of the closed-loop systems are semiglobally ultimately uniformly bounded in probability. Finally, a set of simulation results is provided to confirm the effectiveness of the proposed approach.

This article addresses fault-tolerant event-triggered sampled-data (SD) fuzzy control for nonlinear delayed parabolic partial differential equation (PDE) systems under spatially point measurements (SPMs). First, a Takagi–Sugeno fuzzy delayed PDE model is presented to accurately describe the nonlinear delayed parabolic PDE systems. Second, a fault-tolerant event-triggered SD fuzzy control strategy under SPMs is designed to cope with Markov jump faults occurring in actuators, which can effectively reduce the unnecessary data transmission and be achieved by finite sensors and actuators. The membership functions of the proposed controller are determined by the measurement output and independent of the fuzzy delayed PDE plant model. Then, by constructing a Lyapunov functional, sufficient conditions that guarantee the stochastically exponential stability of closed-loop nonlinear delayed parabolic PDE systems are obtained based on linear matrix inequalities. Finally, two examples are given to illustrate the designed approach.

A novel aperiodic event-triggered control is adopted to address the synchronization issue of T-S fuzzy neural networks with time delay. This control strategy refers to the execution of control tasks in a control system based on real-time events, rather than following a fixed time interval. It allows for more flexible and faster responses to real-time events, and can reduce the computational load, energy consumption, and system costs. At first, a linear event-triggered control mechanism is formulated, in which its triggering condition includes an exponential term. Subsequently, the synchronization criteria based on linear matrix inequalities (LMIs) are deduced under the formulated event-triggered control. In addition, a novel approach that employs the reduction to absurdity technique is proposed to address the nonexistence of Zeno behavior. Eventually, the proposed theory's efficacy is demonstrated by employing an example and an accompanying simulation.

This paper addresses the adaptive fuzzy faulttolerant output containment control of fully-heterogeneous nonlinear multi-agent systems (FHNMASs) subject to stochastic disturbances, where not only followers but also leaders are equipped with non-identical dynamics. Firstly, fully distributed fixed-time observers are designed to observe active leaders' information including system matrices, positions, and external inputs, which are only accessible to a limited group of followers. Subsequently, adaptive algorithms are utilized, informed by the observations of leaders' system matrices, to solve the regulator equations. Then, the state observer-based controller is proposed, which employs the adaptive fuzzy-based approximation law to approximate the unknown nonlinear item and integrates the adaptive gains to cope with the actuator faults in the follower's dynamics. Additionally, a set of modest conditions and rigorous proofs is constructed to guarantee the accomplishment of predetermined output containment through the output regulation method and Lyapunov stability theory. Finally, the theoretical analysis is validated through performing a numerical simulation.

In this article, finite/fixed-time stabilization and energy consumption estimation of a class of discontinuous fuzzy inertial Cohen-Grossberg neural networks (CGNNs) are considered. The saturation function is utilized to design the control laws, which is different from the previous ones with the sign function since the saturation function is continuous and can reduce the chattering phenomenon. By using the Lyapunov method, comparison principle, and differential inequality theory, the finite/fixed-time stabilization is analyzed by analyzing the state variables inside and outside the unit spherical area, which is different from the direct calculation method using finite/fixed-time stability lemmas. Detailed analyses show the differences in the estimations of the settling time between the initial values inside and outside of the unit spherical area. The energy consumption is also estimated when the finite/fixed-time stabilization is achieved under the designed controller, which is discussed for the first time in the inertial networks. Finally, the validity of the theoretical results is demonstrated by a typical numerical example.

In this article, global exponential stabilization of Takagi–Sugeno (T–S) fuzzy systems with discrete time-varying delays on time scales under denial-of-service (DoS) attacks is investigated. When a DoS attack occurs, the control channel is blocked and the controller is disabled. Combining analytical method, inequality techniques, and time scale theory, stabilization criterion for the underlying systems is obtained via a fuzzy controller. Furthermore, the corresponding outcomes on continuous and discrete time domains are provided, respectively. Finally, two numerical simulations and an application of the Chua's circuit are exhibited to validate the effectiveness of the theories.

This article introduces a fuzzy boundary sampled-data (SD) control approach for a nonlinear distributed parameter system (DPS) with random time-varying delay, which belongs to two intervals and is considered by a probabilistic way to take the influence of uncertain factors, and boundary and distributed SD measurements are respected. Initially, this nonlinear DPS is represented precisely by a Takagi–Sugeno (T–S) fuzzy delayed partial differential equation (PDE) model. Subsequently, a fuzzy boundary SD control design is achieved under boundary and distributed SD measurements, employing linear matrix inequalities based on the T–S fuzzy delayed PDE model. This design ensures mean square exponential stability for the closed-loop delayed DPS through the use of inequality techniques and a Lyapunov functional. The membership functions of the proposed fuzzy boundary SD control law are independent of the fuzzy delayed PDE plant model and determined by the measurement output. Finally, the effectiveness of the designed fuzzy boundary SD controller is demonstrated via two simulation examples.

A mixed fuzzy intermittent control method based on boundary control under boundary measurement and distributed control under spatial local averaged measurements (SLAMs) is introduced for nonlinear ordinary differential equations (ODE)-partial differential equations(PDE) coupled systems in this article. To accurately characterize the nonlinear ODE-PDE coupled systems, a Takagi–Sugeno fuzzy model is first employed. Then, based on the fuzzy model, the switched Lyapunov function is proposed to design the mixed fuzzy intermittent controller under boundary measurement and SLAMs. Sufficient conditions on stability for the closed-loop coupled system are obtained via a set of space dependent linear matrix inequalities. The simulation results ultimately confirm the effectiveness of the proposed design approach in controlling hypersonic rocket car.

For nonlinear distributed parameter systems (DPSs) with multiple delays, this study considers a fuzzy fault-tolerant boundary control (BC) with stochastic actuator failures under boundary measurement. First, we exactly represent the nonlinear DPS with multiple delays by the Takagi-Sugeno (T-S) fuzzy delayed partial differential equation (PDE). Next, on basis of T-S fuzzy delayed PDE model, a fuzzy fault-tolerant BC design with stochastic actuator failures under boundary measurement guaranteeing the stochastically exponential stability for closedloop DPS with multiple delays is subsequently presented by linear matrix inequalities (LMIs). Lastly, the effectiveness of the investigated fuzzy fault-tolerant BC strategy with stochastic actuator failures under boundary measurement is proposed via a simulation example.

This article is centered on the synchronization issue of fuzzy reaction–diffusion dynamic networks (RDDNs) with multiple cyberattacks. A novel attack-resilient dynamic event-triggered policy and the relevant fuzzy attack-resilient controller are designed via considering the deception and irregular (aperiodic) denial-of-service (DoS) attacks simultaneously. Based on a novel piecewise Lyapunov–Krasovskii functional with the realistic sampling information and some inequality techniques, some criteria are derived to ensure that the synchronization of fuzzy RDDNs can be realized in the presence of the deception and irregular DoS attacks. Lastly, simulation examples containing the colour image encryption/decryption are supplied to exhibit the advantages and practicality of the developmental theories.

This work proposes a fully distributed moving horizon estimation method with an event-triggered communication strategy over wireless sensor networks. The proposed method calculates a local state estimation of each sensor by minimizing a quadratic objective function, which involves a fused arrival cost that is computed in a distributed manner. This approach adjusts data transmission rate by selectively transmitting local information and allows the incorporation of constraints on the noise and state variables. In addition, the estimation error is proved to be uniformly bounded in the mean square sense under the condition that the network topology is strongly connected and the system is collectively observable. Finally, a target tracking example is presented to demonstrate the validity of the proposed approach.

Numerous studies on learning algorithms have been done in the neural networks community, however concerning to training strategy is limited. In this article, to enhance the performance of fuzzy neural network (FNN) in industrial process identification, an FNN based on two-stage training (TSTFNN) is proposed. First, to initialize TSTFNN, we propose a supervised clustering method. It determines the initial centers and number of neurons for TSTFNN through clustering part of samples based on their labels. Then, the gradient-based algorithm is employed to further learning the parameters of TSTFNN. To improve its generalization performance, we developed a two-stage training scheme. In the first stage, a standard 10-fold cross-validation is executed to learn parameters. The samples whose prediction errors exceed a preset threshold are utilized to train the network in the second stage. According to Lyapunov criterion, we show the convergence of TSTFNN if the learning rate is selected in an appropriate range. The effectiveness of TSTFNN is validated by simulations on two benchmark problems. The proposed TSTFNN performs better and outperforms the state-of-the-art. Compared with supervised long short-term memory, root mean square error is decreased by 17%. Then, we apply it to the industrial rougher flotation process for soft sensor modeling. The experimental results show that TSTFNN achieves a satisfactory prediction performance.

State-of-charge (SoC) balancing in distributed energy storage systems (DESS) is crucial but challenging. Traditional deep reinforcement learning approaches struggle with real-world multiagent cooperation for SoC balance in these decentralized systems. To address these significant hurdles, this article pioneers an innovative fully-decentralized multiagent reinforcement learning (FDMARL) strategy, specifically tailored for DESS. First, the SoC balancing problem is formulated into a finite decentralized Markov decision process with action constraints derived from the microgrid context. Then, the average consensus algorithm is introduced for expanding the agent's observation and obtaining global information through a communication network. To improve multiagent system cooperation, a novel demand balance algorithm is proposed to refine agent actions for precise demand distribution. By the above modules, the FDMARL reveals outstanding performance in a fully-decentralized system without any expert experience or modeling. Finally, numerous simulations were carried out on pymgrid, (i.e., a Python-based open-source microgrid platform), which shows that: The FDMARL exceeds traditional reinforcement learning in decentralized cooperation, effectively manages large-scale systems with random states and demand/PV series, and maintains robustness with ESU failure or integration.

Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to third-order tensors. Against these issues, in this article, two efficient low-rank tensor approximation approaches fusing random projection techniques are first devised under the order-d ( d ≥ 3 ) T-SVD framework. Theoretical results on error bounds for the proposed randomized algorithms are provided. On this basis, we then further investigate the robust high-order tensor completion problem, in which a double nonconvex model along with its corresponding fast optimization algorithms with convergence guarantees are developed. Experimental results on large-scale synthetic and real tensor data illustrate that the proposed method outperforms other state-of-the-art approaches in terms of both computational efficiency and estimated precision.

Data deduplication is of vital importance for mobile cloud computing to cope with the explosive growth of outsourced mobile data. In order to ensure the privacy of sensitive mobile data against an untrusted cloud, Message-Locked Encryption (MLE) has been proposed to enable deduplication over ciphertext. However, MLE prohibits data access control since it uses deterministic content-derived encryption keys. Recently, a lightweight rekeying-aware encrypted deduplication system (REED) has been proposed to achieve dynamic access control for secure data deduplication. However, REED is vulnerable to key-retaining attack and stub-retaining attack, which leads to insecure access revocation, and thus cannot support secure dynamic access control. In response, we present AC-Dedup, an encrypted deduplication storage system that supportssecure dynamic access controlfor mobile cloud storage. At the core of AC-Dedup are two novel encryption techniques namedmixed message locked encryptionandrandom stub re-encryptionto resist the two types of attacks, respectively. To the best of our knowledge, AC-Dedup is the first practical system that achieves secure data deduplication and secure dynamic access control simultaneously. We conduct security analysis and experimental evaluation on mobile device and cloud platform with real-world IoT datasets. The results show that AC-Dedup enables secure and efficient dynamic access control while preserving deduplication effectiveness.

This article focuses on the problem of prefixed-time synchronization for stochastic multicoupled delay dynamic networks with reaction-diffusion terms and discontinuous activation by means of local intermittent sampling control. Notably, unlike the existing common fixed-time synchronization, this article puts forward a new synchronization concept, prefixed-time synchronization, based on the fact that stochastic noise and discontinuous activation can be seen everywhere in practical engineering, which can effectively perfect and improve the existing works. Specifically, a local intermittent in the time domain and point sampling control strategy in the spatial domain is proposed instead of a simple single intermittent control approach, which greatly reduces the control cost. In addition, by some effective means, including the famous Young's inequality, Jensen's inequality, and Hölder's inequality, we obtain two different synchronization criteria of the networks without delay and with multicoupling delays and deeply reveal the quantitative relationship among control period, point sampling length, and network scale. Finally, a numerical example is given to verify the effectiveness of the developed method and the practicability by Chua's circuit model.

In this article, we investigate the boundedness and convergence of the online gradient method with the smoothing group regularization for the sigma-pi-sigma neural network (SPSNN). This enhances the sparseness of the network and improves its generalization ability. For the original group regularization, the error function is nonconvex and nonsmooth, which can cause oscillation of the error function. To ameliorate this drawback, we propose a simple and effective smoothing technique, which can effectively eliminate the deficiency of the original group regularization. The group regularization effectively optimizes the network structure from two aspects redundant hidden nodes tending to zero and redundant weights of surviving hidden nodes in the network tending to zero. This article shows the strong and weak convergence results for the proposed method and proves the boundedness of weights. Experiment results clearly demonstrate the capability of the proposed method and the effectiveness of redundancy control. The simulation results are observed to support the theoretical results.

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., social networks and knowledge graphs) on traditional computing architectures (CPU, GPU, or TPU). But, the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption; to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled “block partition” schemes are proposed. However, these methods are coarse-grained or static and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model [long short-term memory (LSTM), combined with the dynamic-fill scheme] generates remarkable mapping performance on the small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms.

With the rapid progress of deep neural network (DNN) applications on memristive platforms, there has been a growing interest in the acceleration and compression of memristive networks. As an emerging model optimization technique for memristive platforms, bit-level sparsity training (with the fixed-point quantization) can significantly reduce the demand for analog-to-digital converters (ADCs) resolution, which is critical for energy and area consumption. However, the bit sparsity and the fixed-point quantization will inevitably lead to a large performance loss. Different from the existing training and optimization techniques, this work attempts to explore more sparsity-tolerant architectures to compensate for performance degradation. We first empirically demonstrate that in a certain search space (e.g., 4-bit quantized DARTS space), network architectures differ in bit-level sparsity tolerance. It is reasonable and necessary to search the architectures for efficient deployment on memristive platforms by the neural architecture search (NAS) technology. We further introduce bit-level sparsity-tolerant NAS (BST-NAS), which encapsulates low-precision quantization and bit-level sparsity training into the differentiable NAS, to explore the optimal bit-level sparsity-tolerant architectures. Experimentally, with the same degree of sparsity and experiment settings, our searched architectures obtain a promising performance, which outperform the normal NAS-based DARTS-series architectures (about 5.8% higher than that of DARTS-V2 and 2.7% higher than that of PC-DARTS) on CIFAR10.

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

This article addresses the stabilization and synchronization problems of coupled neural networks (NNs) via an impulsive adaptive control (IAC) strategy. Unlike the traditional fixed-gain-based impulsive methods, a novel discrete-time-based adaptive updating law for the impulsive gain is designed to maintain the stabilization and synchronization performance of the coupled NNs, where the adaptive generator only intermittently updates its data at the impulsive instants. Several stabilization and synchronization criteria for the coupled NNs are established based on the impulsive adaptive feedback protocols. Additionally, the corresponding convergence analysis are also provided. Finally, the effectiveness of the obtained theoretical results is illustrated using two comparison simulation examples.

This article investigates the finite-time synchronization (FTS) and$H_{\infty }$synchronization for two types of coupled neural networks (CNNs), that is, the cases with multistate couplings and with multiderivative couplings. By designing appropriate state feedback controllers and parameter adjustment strategies, some FTS and finite-time$H_{\infty }$synchronization criteria for CNNs with multistate couplings are derived. In addition, we further consider the FTS and finite-time$H_{\infty }$synchronization problems for CNNs with multiderivative couplings by utilizing state feedback control approach and selecting suitable parameter adjustment schemes. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed criteria.

This article investigates the event-triggered adaptive containment control problem for a class of stochastic nonlinear multiagent systems with unmeasurable states. A stochastic system with unknown heterogeneous dynamics is established to describe the agents in a random vibration environment. Besides, the uncertain nonlinear dynamics are approximated by radial basis function neural networks (NNs), and the unmeasured states are estimated by constructing the NN-based observer. In addition, the switching-threshold-based event-triggered control method is adopted with the hope of reducing communication consumption and balancing system performance and network constraints. Moreover, we develop the novel distributed containment controller by utilizing the adaptive backstepping control strategy and the dynamic surface control (DSC) approach such that the output of each follower converges to the convex hull spanned by multiple leaders, and all signals of the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in mean square. Finally, we verify the efficiency of the proposed controller by the simulation examples.

This article focuses on finite-time passivity (FTP) and finite-time synchronization (FTS) for complex dynamical networks with multiple state/derivative couplings based on the proportional-derivative (PD) control method. Several criteria of FTP for complex dynamical networks with multiple state couplings (CDNMSCs) are formulated by utilizing the PD controller and constructing an appropriate Lyapunov function. Furthermore, FTP is further used to investigate the FTS in CDNMSCs under the PD controller. In addition, the FTP and FTS for complex dynamical networks with multiple derivative couplings (CDNMDCs) are also studied by exploiting the PD control method and some inequality techniques. Finally, two numerical examples are worked out to demonstrate the validity of the presented PD controllers.

Since the depth information of images facilitates the analysis of the spatial distance of objects in computer vision applications, it is necessary to protect the image depth information. Thus this article proposes a novel red-green-blue-depth (RGB-D) image protection algorithm, which is implemented with the finite-time synchronization (FTS) of neural networks (NNs) with proportional delays via the quantized intermittent control to derive the system synchronization criterion based on Lyapunov stability theory. The performance of RGB-D image protection depends on the synchronization error of the system by driving the system sequence to encrypt the RGB-D image and responding to the system sequence to decrypt the encrypted image. Subsequently, the validity of the proposed criteria is verified by simulation examples, and the practical application of RGB-D image protection is verified.

Recent research shows that the sole accuracy metric may lead to the homogeneous and repetitive recommendations for users and affect the long-term user engagement. Multiobjective reinforcement learning (RL) is a promising method to achieve a good balance in multiple objectives, including accuracy, diversity, and novelty. However, it has two deficiencies: neglecting the updating of negative action values and limited regulation from the RL Q-networks to the (self-)supervised learning recommendation network. To address these disadvantages, we develop the supervised multiobjective negative actor-critic (SMONAC) algorithm, which includes a negative action update mechanism and multiobjective actor-critic mechanism. For the negative action update mechanism, several negative actions are randomly sampled during each time updating, and then, the offline RL approach is utilized to learn their values. For the multiobjective actor-critic mechanism, accuracy, diversity, and novelty values are integrated into the scalarized value, which is used to criticize the supervised learning recommendation network. The comparative experiments are conducted on two real-world datasets, and the results demonstrate that the developed SMONAC achieves tremendous performance promotion, especially for the metrics of diversity and novelty.

This article is dedicated to studying the discrete time output sampling stabilization problem of T–S fuzzy (TSF) time-varying delay systems with stochastic disturbance and actuator saturation. Different from the existing sampling control that is converted to input time-delay type, the fuzzy sampling in this article is output sampling feedback control at discrete time points. More importantly, the proposed method eliminates the limitation of previous results on the relation between the magnitude of the time delay and the upper/lower bound of the sampling interval. Especially, based on the provided two different analysis methods of the multipiecewise time-dependent Lyapunov–Krasovskii functional and the multisegment time-dependent Lyapunov–Razumikhin function, which can effectively capture more dynamic characteristics of the target system and greatly reduce the conservatism of the relevant results. Finally, two practical models are provided to demonstrate the rationality and superiority of the developed approach.

This article aims to study the fixed-time (FxT) synchronization of different dimensional Filippov systems. New FxT stability lemmas containing the classical inequality$\dot {V}\leq {-c}_{1}V^{a}-c_{2}V^{b}$proposed by Polyakov are established. Different from the previous FxT stability lemmas in the literature, the proposed one shows the new conclusion that the settling times can be larger or smaller as long as$c_{1}$and$c_{2}$satisfy the certain relationships, which synchronously reveals that the relationships between the control gains can lead to different settling times. Besides, a generalized economical inequality condition$\dot {V} \leq -c_{1}V-c_{2}V^{\upsilon +{\mathrm{ sign}}(V-r)}$is proposed and a novel FxT stability lemma is also established. The complete theoretical proof is given to reveal that$r=1$leads to desired settling time, some previous related results are improved. Based on the new FxT stability lemmas and differential inclusion theory, algebraic inequality conditions are provided to guarantee the FxT synchronization, which reports the first result on the FxT synchronization of different dimensional Filippov systems. Finally, numerical examples are provided to verify the correctness of the main theoretical results.

This article addresses the consensus tracking control of multiagent systems (MASs) via a quadratic programming (QP) optimization framework, where the control Lyapunov function (CLF) condition serves as a constraint. The optimal controllers, derived through the QP solver, not only ensure the tracking control objective but also minimize the cost functions of agents. To enhance energy efficiency, discontinuous control methods, such as intermittent control strategy and event-triggered mechanism, are employed in the control framework. The CLF-based QP controllers are only updated at specific time instants, in order to reduce the frequency of QP problem-solving. In addition to considering optimization, the proposed methods are extended to uncertain MASs to enhance robustness, where the uncertainty is modeled by Gaussian process regression. In the end, simulation results are provided to demonstrate the feasibility of the theoretical analysis.

This article presents an adaptive control strategy for lane-keeping task of automatic steering systems via sliding-mode technique. First, considering the road-vehicle lateral dynamics, a standard single track steering model has been developed for the lane-keeping task. To cope with the time-variant nature of the longitudinal velocity and uncertainty of measurement, a class of interval type-2 fuzzy sets considered in this article are employed to reconstruct the steering system dynamics mathematical model. Based on the fuzzy system, an integral sliding surface is proposed and the asymptotic convergence criterion for the overall system is derived with extended dissipation. Furthermore, an adaptive control law is provided to achieve the reachability of the assigned sliding surface and improve the attenuation ability to unknown curvature and exogenous disturbance. Finally, several scenarios with different path-following tasks are given in the simulations. Results demonstrate that the proposed sliding-mode control method has the capability to track the road centerline and is robust to external unknown disturbances.

This article is primarily concerned with the quasi-projective synchronization phenomenon between the a leader node and response nodes of an observer-based delayed dynamical network (DDN) instead of complete synchronization because of projective functional factor and parameters mismatch. First, a novel observer-based drive-response dynamical network (DN) with time-varying discrete-distributed delays and parameters mismatch is constructed to study its synchronization phenomena by introducing projective functional factor. Then, in order to obtain the sufficient criteria of quasi-projective synchronization for this system, the special impulsive control strategies and the definition of matrix measure are introduced in this article. After that, by the different impulsive phenomena and the properties of the projective functional factor, appropriate Lyapunov functional, Cauchy matrix, and inequality techniques are used to discuss and derive quasi-projective synchronization conditions of this observer-based delayed DN. In addition, some conclusions of synchronization for special DNs models are given as corollaries. Finally, an example with one leader node and four different response nodes and its corresponding simulation figures are given to demonstrate the obtained results.

This article proposes a distributed capture strategy optimization method for the pursuit-evasion game involving multiple unmanned surface vehicles. Considering the limited perception range of each pursuer, a multiagent proximal policy optimization method combined with a novel velocity control mechanism is utilized to guide the pursuers in approaching the evader and form a dynamic encirclement. Moreover, to facilitate deep reinforcement learning (DRL) training, a bidirectional gated recurrent unit feature network is constructed to extract the fixed-length vector representations from the variable-length observation sequences. In terms of the policy training, by employing virtual barriers and curriculum learning techniques during the training process, the generalization capabilities and convergence speed of the policy have been further improved. Finally, our method is compared with the other DRL methods through the comparative simulation experiments and virtual reality scene testing based on the gazebo three dimensional physics engine, verifying its significant advantages in the policy convergence speed, capture efficiency, and generalization capabilities.

This article addresses the distributed cooperative protocol for nonlinear agents with the aim of attaining the optimal leader-following consensus. The main challenges encountered when deriving the cooperative optimal protocol are caused due to the conservatism of Lipschitz nonlinear dynamics, uncertainties, disturbances, and coupling of agents. Until now, the consensus optimal protocols for Lipschitz nonlinear agents are provided in the existing literature, which is conservative and has limited applications. To tackle these issues, a cooperative protocol is provided for intelligent nonlinear systems that are optimal for the performance index and robust against uncertainties and disturbances. The performance index of the optimal protocol depends on the Lipschitz nonlinearities is converted into linear parameter-varying (LPV) form. The LPV approach reduces the conservatism of the existing methods for Lipschitz nonlinearities that improves the scope of the design approach and is applicable to practical applications of nonlinear multiagents. The optimal solution is obtained by solving the algebraic Riccati equation. The proposed optimal scheme increases the region of the feasibility of the Lipschitz constant by extracting precise information about nonlinearities. Moreover, a robust cooperative optimal protocol is designed that has ensured optimal consensus for the nonlinear agents and eliminated the negative effects of parameter uncertainties and disturbances. Finally, the results are verified through a simulation example of multiple nonlinear robotic manipulators.

In this article, exponential synchronization of complex dynamical networks (CDNs) on time scales is researched. An IDET control strategy is designed to decrease the number of the event-triggered updating instants. Leveraging intermittent event detections and event-triggered sampling, and combining the analytical method with the time-scale theory, synchronization criteria are obtained for the underlying CDNs. Moreover, a parameter selection algorithm is given to acquire control parameters. In addition, two lemmas on exponential functions of time scales are proposed to prove the exclusion of Zeno behavior. Two numerical simulations and an application of formation control of spacecrafts are given to verify the validity of theoretical results.

The neural-approximation-based adaptive nonlinear containment control issue for multiagent systems with full-state constraints is studied by invoking the backstepping approach. First, the barrier Lyapunov functions are established to deal with the state constraining issue in the multiple leaders/followers control scenarios. Then, by introducing the first-order filter, the system communication burden is substantially reduced. Moreover, the event-triggered controller is constructed by utilizing the switching-based mechanism so that the system security, control accuracy, resource consumption, and imposed state constraints are neatly balanced. We prove the output of each follower can converge to the desired hull formulated by leaders under the premise that the imposed state constraints are never violated. Besides, the considered closed-loop signals are uniformly bounded. We finally present a simulation example to show the validity of the developed approach.

Finite-time observability of switching Boolean networks (SBNs) with Markov jump parameters (MJPs) is studied in this article. Via a parallel extension method, the observability of the considered SBN with MJPs is equivalent to that zero vector is reachable from an initial set of the new constructed system. A necessary and sufficient condition based on the extended structure matrix is presented for finite-time observability. Further, for unobservable systems, mode-dependent pinning control is first introduced and applied to achieve the observability. After the set of pinning subsystems is selected, for each pinning subsystem, mode-dependent pinning nodes, output-feedback controls (OFCs), and the adding approaches are designed. An algorithm is provided to find the set of pinning subsystems. Moreover, a necessary condition is given to solve mode-dependent pinning nodes, and the solvability of mode-dependent OFCs and the adding approaches are guaranteed. Finally, a numerical example is presented to show the effectiveness of the obtained results.

Under intermittent noncollocated boundary measurement (BM), this article introduces a dynamic intermittent boundary output-feedback control for reaction–diffusion systems (RDSs). Since the system state is not fully available and intermittent noncollocated BM makes the intermittent BC design very difficult, an observer-based control technique is given to surmount this design difficulty. Initially, a PDE state observer under intermittent noncollocated BM is provided to estimate the RDS state. Then, the exponential stability of closed-loop RDSs is ensured by constructing an observer-based controller. Sufficient conditions of such dynamic controller are subsequently presented by linear matrix inequalities (LMIs) via employing a switching time-dependent LF and inequality techniques. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design approach.

Sparse optimization problems have been successfully applied to a wide range of research areas, and useful insights and elegant methods for proving the stability and convergence of neurodynamic algorithms have been yielded in previous work. This article develops several neurodynamic algorithms for sparse signal recovery by solving the$\ell _{1}$regularization problem. First, in the framework of the locally competitive algorithm (LCA), modified LCA (MLCA) with finite-time convergence and MLCA with fixed-time convergence are designed. Then, the sliding-mode control (SMC) technique is introduced and modified, i.e., modified SMC (MSMC), which is combined with LCA to design MSMC-LCA with finite-time convergence and MSMC-LCA with fixed-time convergence. It is shown that the solutions of the proposed neurodynamic algorithms exist and are unique under the observation matrix satisfying restricted isometry property (RIP) condition, while finite-time or fixed-time convergence to the optimal points is shown via Lyapunov-based analysis. In addition, combining the notions of finite-time stability (FTS) and fixed-time stability (FxTS), upper bounds on the convergence time of the proposed neurodynamic algorithms are given, and the convergence results obtained for the MLCA and MSMC-LCA with fixed-time convergence are shown to be independent of the initial conditions. Finally, simulation experiments of signal recovery and image recovery are carried out to demonstrate the superior performance of the proposed neurodynamic algorithms.

The problem of fault estimation is addressed for one-dimensional (1-D) linear boundary control and boundary observation (BCBO) parabolic partial differential equations (PDEs) with a faulty boundary measurement. The considered plant is subjected to simultaneous unknown multiplicative faults entering the boundary input and boundary measurement. Difficulties arise due to the coupling between the sensor fault parameter and unknown boundary state appearing in the measurement. With the only boundary input and faulty boundary measurement, it is rather challenging to estimate the accurate values of faults and state simultaneously. Therefore, most existing results only consider correct and healthy measurement for PDE systems. To this end, novel adaptation laws and an adaptive observer are designed in this work to provide exponential convergent joint fault-state estimation, where we design and leverage a set of novel filters. It is first time that unknown multiplicative fault parameter in the measurement can be estimated accurately in the PDE systems.

In this article, the finite-time (FnT) control of a class of discontinuous memristor-based fuzzy inertial neural networks with proportional delays is discussed. The proposed model is more generalized. Different from the use of the finite/fixed-time stability lemmas in the previous works, by constructing the unit ball and discussing the subordinate relationship between the states and the unit ball, the finite/fixed-time stability are analyzed in detail. This method can avoid the rough thinking that the state variables of the system start from greater than 1. Also, the control constraint is taken into account and a quantized controller is designed, which can not only help achieve the finite/fixed-time stability, but also can prevent the singularity when using the norm forms and chattering when using the sign function in the controllers. In addition, the energy consumptions generated by the quantized controller are estimated in FnT and fixed-time stability processes. The criteria show that the energy consumptions are relevant to the proportional delays, when the proportional coefficient is 1, the energy loss reaches the minimum value. Finally, numerical example and simulation are provided to verify the validity of the obtained results.

This article studies the impact of constrained optimal stealthy attacks on the state estimator, where man-in-the-middle attacks with a linear form can compromise innovations transmitted through a wireless network. First, a novel resource-constrained attack model is proposed, in which there are only a finite number of attack instants within a fixed interval. Second, the evolution of the estimation error covariance under attacks is obtained, and the covariance at the ultimate instant of the attack interval is regarded as the attacker’s cost function. Moreover, a relaxed condition of the strict stealthiness, named Kullback–Leibler divergence, is employed to describe the attacker’s the stealthiness metric. Third, the one-time and holistic optimization problems of stealthy attacks are solved by exploiting the Lagrange multiplier method. Then the constrained optimal attack strategies are obtained to produce the largest ultimate estimation error covariance. Finally, two simulation cases are provided to confirm the correctness of the designed attack strategies.

This article investigates consensus problems of multiple-input-multiple-output (MIMO) linear multiagent systems (MASs) with directed topology under event-triggered (ET) sampling. First, an observer based on relative outputs is designed to estimate relative full states by utilizing an unknown input observer technique. Second, a controller with static coupling strength and a dynamic ET mechanism (DETM) is proposed. And by developing a Lyapunov function, we show that asymptotical consensus is achieved if the coupling strength is larger than a positive threshold. The threshold, however, depends on some global parameters which are difficult to acquire. Later, an adaptive controller with dynamic coupling strengths as well as a DETM is proposed. And we show that asymptotical consensus is achieved in a fully distributed fashion. Moreover, we show Zeno behavior is avoided. Finally, two examples are given to validate theoretical results.

This article investigates a fully distributed inertial neurodynamic approach for sparse recovery. The approach is based on proximal operators and inertia items. It aims to solve the$L_{1}$-norm minimization problem with consensus and linear observation constraints over directed communication networks. The proposed neurodynamic approach has the advantages of only requiring the communication network to be directed and weight-balanced, does not involve a central processing node and global parameters, which means that no single node can access the entire network and observe it at any time, so it is fully distributed. To effectively deal with the nonsmooth objective function,$L_{1}$-norm, the proximal operator method is used here. For efficiently handling linear observation and consensus constraints, a primal-dual method is applied to the inertial dynamic system. With the aid of maximal monotone operator theory and Baillon-Haddad lemmas, it reveals that the trajectories of our approach can converge to consensus solution at the optimal solution, provided that the distributed parameters satisfy technical conditions. In addition, we aim to demonstrate the weak convergence of the trajectories in our proposed neurodynamic approach toward the zeros of the optimal operator in Hilbert space, using Opial’s lemma. Finally, comparative experiments on sparse signal and image recovery confirm the efficiency and effectiveness of our proposed neurodynamic approach.

In this paper, a novel Field-Programmable-Gate-Array (FPGA) implementation framework based on Lagrange programming neural network (LPNN), projection neural network (PNN) and proximal projection neural network (PPNN) is proposed which can be used to solve smooth and nonsmooth optimization problems. First, Count Unit (CU) and Calculate Unit (CaU) are designed for smooth problems with equality constraints, and these units are used to simulate the iteration actions of neural network (NN) and form a feedback loop with other basic digital circuit operations. Then, the optimal solutions of optimization problems are mapped by the output waveforms. Second, the digital circuit structures of Path Select Unit (PSU), projection operator and proximal operator are further designed to process the box constraints and nonsmooth terms, respectively. Finally, the effectiveness and feasibility of the circuit are verified by three numerical examples on the Quartus II 13.0 sp1 platform with the Cyclone IV E series chip EP4CE10F17C8.

Runoff forecasting is the key to the rational use and protection of water resources by mankind. The large-scale application of machine learning and neural networks in hydrological models has made accurate and reliable short-term runoff forecasting possible. In this article, a novel short-term runoff forecasting framework called ResGRU Plus is proposed with gated recurrent unit (GRU) as the backbone. GRU has the characteristics of long short-term memory (LSTM) that can selectively memorize and forget information while merging gating units to reduce the amount of parameters. Residual network (ResNet) is also deeply integrated with GRU, and its unique shortcut connection effectively solves the degradation problem of traditional neural networks, making it possible to train deep neural networks based on the recurrent architecture. Moreover, a lightweight attention mechanism module: squeeze-and-excitation network (SENet) is embedded in the framework. SENet explicitly models the interdependence between feature dimensions through one global average pooling layer (GAP) and two fully connected (FC) layers, and rescales the original features through the learned weights to adaptively amplify or suppress features. Snapshot ensemble method is also used to train ResGRU Plus, which can integrate multiple homogeneous weak learners through one training process to improve the performance of the model at a small cost. In this article, the hourly runoff of the Columbia River is used as the data set. The Nash–Sutcliffe coefficient (NSE) of Efficiency and coefficient of determination$(R^{2})$, which are two common evaluation metrics for hydrological models, are used to measure the performance of the models. Multiple sets of ablation experiments show that the proposed ResGRU Plus, which combines ResNet and the attention mechanism, is able to improve depth by a factor of over 4 and accuracy by nearly 18% compared to the vanilla GRU, which further fully validates the effectiveness of combining the residual structure and attention mechanism with the recurrent architecture-based neural network and the feasibility of applying it to runoff forecasting. In addition, several sets of comparative experiments have also demonstrated the state-of-the-art performance of ResGRU Plus with significant improvement in accuracy compared to mainstream time-series forecasting models.

Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems. In many real-world scenarios, tasks often have several conflicting objectives and may require multiple agents to cooperate, which are the multi-objective multi-agent decision-making problems. However, only few works have been conducted on this intersection. Existing approaches are limited to separate fields and can only handle multi-agent decision-making with a single objective, or multi-objective decision-making with a single agent. In this paper, we propose MO-MIX to solve the multi-objective multi-agent reinforcement learning (MOMARL) problem. Our approach is based on the centralized training with decentralized execution (CTDE) framework. A weight vector representing preference over the objectives is fed into the decentralized agent network as a condition for local action-value function estimation, while a mixing network with parallel architecture is used to estimate the joint action-value function. In addition, an exploration guide approach is applied to improve the uniformity of the final non-dominated solutions. Experiments demonstrate that the proposed method can effectively solve the multi-objective multi-agent cooperative decision-making problem and generate an approximation of the Pareto set. Our approach not only significantly outperforms the baseline method in all four kinds of evaluation metrics, but also requires less computational cost.

In this paper, two neurodynamic algorithms and the corresponding Field-Programmable-Gate-Array (FPGA) implementation scheme are presented, respectively. Firstly, based on Lagrange programming neural networks (LPNN) and sliding mode control technique, an algorithm with finite-time convergence and an algorithm with fixed-time convergence is proposed and used to solve constrained optimization problems. Then, the Update module, the Control module, the Gradient module, the finite-time processing (FTP) module, and the fixed-time processing (FxTP) module are designed to form an FPGA hardware implementation of neurodynamic optimization algorithms. The LPNN-based FPGA reconfigurable circuit framework can be structured by invoking the Update module, the Control module, and the Gradient module. The FTP module or the FxTP module can be called into the above framework to form the finite-time or fixed-time stable FPGA reconfiguration framework. Finally, the effectiveness and practicality of the proposed FPGA hardware implementation scheme is verified through example simulations implemented on the Vivado 2019.1 platform.

This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme.

This article deals with the fully distributed pull-based event-triggered bipartite fixed-time output consensus problem of heterogeneous linear multiagent systems (HLMASs) with an active leader, whose information can be merely accessed by a small fraction of followers. First, a class of fully distributed fixed-time observers is proposed for each follower to estimate the leader's system matrices, position, and control input under the signed communication topology, respectively. Then, based on the estimations of leader's system matrices, two adaptive algorithms are given to solve the regulator equations. Furthermore, the fully distributed fixed-time observer-based controllers associated with state feedback and output feedback are, respectively, proposed by employing the pull-based event-triggered mechanism (ETM) where each agent merely updates controller at its own triggering instants. Correspondingly, some sufficient criteria and the rigorous proofs are provided to ensure the implementation of bipartite output consensus in fixed time by using the Lyapunov stability theory and fixed-time stability theory. Moreover, the strictly positive lower bounds of intervals between two adjacent event-triggered times are derived, which means the Zeno behavior is ruled out. Finally, numerical simulations are performed to demonstrate the theoretical analysis.

This article mainly focuses on putting forward new fixed-time (FIXT) stability lemmas of delayed Filippov discontinuous systems (FDSs). By providing the new inequality conditions imposed on the Lyapunov-Krasovskii functions (LKF), novel FIXT stability lemmas are investigated with the help of inequality techniques. The new settling time is also given and its accuracy is improved in comparison with pioneer ones. For the purpose of illustrating the applicability, a class of discontinuous fuzzy neutral-type neural networks (DFNTNNs) is considered, which includes the previous NTNNs. New criteria are derived and detailed FIXT synchronization results have been obtained. Finally, typical examples are carried out to demonstrate the validity of the main results.

This article dedicates to automatically explore efficient portrait parsing models that are easily deployed in edge computing or terminal devices. In the interest of the tradeoff between the resource cost and performance, we design the multiobjective reinforcement learning (RL)-based neural architecture search (NAS) scheme, which comprehensively balances the accuracy, parameters, FLOPs, and inference latency. Finally, under varying hyperparameter configurations, the search procedure emits a bunch of excellent objective-oriented architectures. The combination of two-stage training with precomputing and memory-resident feature maps effectively reduces the time consumption of the RL-based NAS method, so that we complete approximately 1000 search iterations in two GPU days. To accelerate the convergence of the lightweight candidate architecture, we incorporate knowledge distillation into the training of the search process. This also provides a reasonable evaluation signal to the RL controller that enables it to converge well. In the end, we conduct full training with outstanding Pareto-optimal architectures, so that a series of excellent portrait parsing models (with only approximately 0.3M parameters) is received. Furthermore, we directly transfer the architectures searched on CelebAMask-HQ (Portrait Parsing) to other portrait and face segmentation tasks. Finally, we achieve the state-of-the-art performance of 96.5% MIOU on EG1800 (portrait segmentation) and 91.6% overall F1 -score on HELEN (face labeling). That is, our models significantly surpass the artificial network on the accuracy, but with lower resource consumption and higher real-time performance.

This article investigates$H_{\infty }$global asymptotic synchronization (GAS) of switched nonlinear systems with delay. By introducing mode-dependent double event-triggering mechanisms (DETMs), the communication resources in both system–controller (S-C) channel and controller–actuator (C-A) channel are saved as much as possible. By designing a new multiple Lyapunov–Krasovskii functional (LKF) with time-varying matrices and developing novel analysis techniques such that the increment of the LKF at switching instant is smaller than one, not only the conservatism of obtained results is greatly reduced but also the nonweighted$\mathcal {L}_{2}$-gain is convenient to be derived without using any conservative transformation. The exclusion of the Zeno behavior of the DETMs is proved. Synchronization criteria formulated by linear matrix inequalities (LMIs) are given, by which the control gains, event-triggering weights, as well as the minimum$\mathcal {L}_{2}$-gain are simultaneously designed. Numerical examples demonstrate the low conservatism of the theoretical analysis. Meanwhile, image processing on the basis of the$H_{\infty }$GAS is provided to further illustrate the perfect performance.

This study focused on the asynchronous event-triggered output-feedback controller design problem for discrete-time singular Markov jump systems (MJSs). A hidden Markov model (HMM) was employed to estimate the system mode, which cannot always be ideally detected in practice. Because the full state is also difficult to obtain in practical scenarios, an output-feedback control scheme was used. In addition, an HMM-based event-triggered mechanism was also employed in the design of the controller to reduce the communication burden of the networked system. Sufficient conditions for the stochastic admissibility of a closed-loop singular MJS with a prescribed$H_{\infty }$performance index were established using the Lyapunov functional technique. Finally, design procedures for an asynchronous event-triggered controller were summarized as a linear-matrix-inequality-based optimization algorithm. Two examples were considered to verify the effectiveness of the asynchronous event-triggered output-feedback controller design method.

This article focuses on the neural-network (NN)-based adaptive tracking control issue for a class of high-order nonlinear multiagent systems both subjected to the immeasurable state variables and unknown external disturbance. Combining with the radial basis function NNs (RBF NNs), the composite disturbance observer and state observer for each follower are established, respectively. The purpose of this work is to develop NN-based adaptive tracking control schemes such that the output of each follower ultimately tracks that of the leader and all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded by utilizing the backstepping technique. Furthermore, so as to cope with the sparsity of the control resources, the proposed method is extended to the event-triggered case and the adaptive event-triggered tracking control protocol is formulated for nonlinear multiagent systems. Finally, the numerical example is performed to verify the efficacy of the proposed approach.

For nonlinear delayed distributed parameter systems (DDPSs), this article considers a fuzzy boundary control (FBC) under boundary measurements (BMs). Initially, we accurately describe the nonlinear DDPS through a Takagi-Sugeno (T-S) fuzzy partial differential-difference equation (PDDE). Then, in accordance with the T-S fuzzy PDDE model, an FBC design under BMs ensuring the exponential stability for closed-loop DDPS is subsequently presented by spatial linear matrix inequalities (SLMIs) via using Wirtinger's inequality, Halanay's inequality, and the Lyapunov direct method, which respects the fast-varying and slow-varying delays. Moreover, we formulate SLMIs as LMIs for solving the fuzzy boundary controller design of nonlinear DDPSs under BMs. Finally, the effectiveness of the proposed FBC strategy is presented via simulation examples.

This article focuses on the robust$\mathcal {H}_{\infty }$synchronization of two types of coupled reaction–diffusion neural networks with multiple state and spatial diffusion couplings by utilizing pinning adaptive control strategies. First, based on the Lyapunov functional combined with inequality techniques, several sufficient conditions are formulated to ensure$\mathcal {H}_{\infty }$synchronization for these two networks with parameter uncertainties. Moreover, node-based pinning adaptive control strategies are devised to address the robust$\mathcal {H}_{\infty }$synchronization problem. In addition, some criteria of$\mathcal {H}_{\infty }$synchronization for these two networks under parameter uncertainties are developed via edge-based pinning adaptive controllers. Finally, two numerical examples are presented to verify our results.

The spread of electric vehicles (EVs) could reduce greenhouse gas emissions and achieve sustainable travel patterns. However, the rapidly increasing charging demand will bring challenges to the operation of charging stations and power systems. Therefore, a two-stage EV management scheme is introduced in this article to overcome these challenges and promote sustainable transport. A charging dispatch model based on fuzzy multicriteria decision making is proposed in the first stage, where users' preferences are in the form of intuitionistic fuzzy sets to address the fuzziness and uncertainty of subjective factors and human judgment. A$\sigma$-cut similarity matrix is proposed to increase the users' satisfaction by excluding options with lower similarity. In the second stage, a noncooperative game model is proposed to incentive EVs to participate in supplementary frequency regulation (SFR). A fuzzy set is employed to reflect users' willingness to adjust charging power. The existence and uniqueness of the Nash equilibrium are investigated. Moreover, a distributed proximal best response algorithm with linear convergence is employed to find Nash equilibrium. Numerical simulations indicate that the proposed method can reduce charging costs while meeting users' preferences and facilitate EVs to participate in SFR.

For nonlinear delayed parabolic partial differential equation (PDE) systems, this article addresses fault-tolerant stochastic sampled-data (SD) fuzzy control under spatially point measurements (SPMs). Initially, a T–S fuzzy PDE model is given to accurately describe the nonlinear delayed parabolic PDE system. Second, in consideration of possible actuator failure, a fault-tolerant SD fuzzy controller with stochastic sampling under SPMs is designed for nonlinear delayed parabolic PDE system, where two sampling periods are considered whose occurrence probabilities are given constants and satisfy the Bernoulli distribution. Then, by constructing a novel time-dependent Lyapunov functional, sufficient conditions that guarantee the mean square exponential stability of closed-loop delayed PDE system are obtained based on linear matrix inequalities. Last, three examples are given to illustrate the designed approach.

This article is concerned with global exponential synchronization of delayed fuzzy neural networks with reaction diffusions (RDFNNs). By adopting analytic method and some inequality techniques, a global exponential synchronization criterion in terms of$p$-norm ($p\geq 2$) is obtained for the RDFNNs via adaptive intermittent control. One numerical example is provided to demonstrate the validity of the proposed outcomes. Two other examples are given to show the applications in image encryption and pseudorandom number generation, respectively.

Joint uncertainties and state estimation of a class of linear coupled hyperbolic partial differential equation systems in the presence of unstructured and structured uncertainties are studied in this paper. For unstructured uncertainties which are completely unknown, by employing Takagi-Sugeno fuzzy logic system to approximate the unstructured uncertainties, a novel adaptive fuzzy boundary observer is developed to estimate both unknown system states as well as unknown weights in the fuzzy logic system, and the estimation errors are ultimately bounded. Therein, in the design of the proposed observer, a set of swapping filters and infinite dimensional backstepping technique are combined. On the other hand, for structured uncertainties that can be described in a concrete parameterized form, the proposed method can easily achieve the exact estimation of weights and states to their true values. The rigorous proof is provided to show that the ultimately bounded estimation errors for the case of unstructured uncertainties and the exponential convergent estimation errors for the case of structured uncertainties can be realized. Finally, three illustrative simulations are carried out to show the feasibility and effectiveness of the developed methods in this paper.

For nonlinear space-varying parabolic partial differential equation systems (PPDESs) with random time-varying delay, this article introduces a dynamic fuzzy boundary output feedback (DFBOF) control under noncollocated boundary measurement (NCBM). Initially, the nonlinear delayed PPDESs are represented by Takagi–Sugeno (T–S) fuzzy models and random time-varying delay is considered by taking the influence of uncertain factors, which belongs to two intervals in a probabilistic way. Since the system state is not fully available and NCBM makes the boundary control design very difficult, a fuzzy observer under NCBM is presented to surmount the design difficulty. Subsequently, an observer-based fuzzy boundary controller is proposed and spatial linear matrix inequality (SLMI)-based sufficient conditions to ensure mean-square exponential stability are obtained for closed-loop delayed PPDESs by utilizing the Lyapunov direct method and Wirtinger inequality. Then, to solve the SLMIs, the feasibility conditions of DFBOF controller design for nonlinear delayed PPDES are expressed in LMIs. Finally, two examples are offered to demonstrate the validity of the presented dynamic fuzzy boundary control approach.

The impulsive consensus problems of nonlinear time-delay multiagent system under communication constraints are analyzed and investigated in this article. In view of the limited communication channel, global input saturation constraint is integrated into the design of impulse control protocol. Furthermore, in order to reduce energy consumption and communication cost, two kinds of impulsive control protocols are designed that conclude general impulsive control protocols and event-triggered impulsive control protocols. For event-triggered impulsive control protocols, by setting event decision time at impulse time, this novel consensus control protocols not only decrease communication frequency and energy consumption but also avoid “Zeno behavior.” Through theoretical derivation, some sufficient conditions that guarantee the leader-following consensus of multiagent system are presented. In the end, for the sake of explaining the superiority of impulse control combined with event-triggered control strategy in more detail, the simulations of two control methods are given, respectively, for the same multiagent system, which can be better compared and explained.

Accurate state of charge (SOC) can effectively improve safety performance and prolong the cycle life of the batteries. The widely used model-based SOC estimation methods have underlying assumptions of complete measurements and accurate estimator gains, which are not always reasonable in practical applications. Thus, this article designs a dual Kalman filter-type resilient filter to estimate SOC and parameter jointly with the random missing measurement phenomenon which is modeled by a Bernoulli distributed sequence. Besides, the filter gain variations, in both online parameter identification and state estimation, are characterized by mutually independent multiplicative noise terms. Then, based on the minimum-variance principle, the filter gains are designed to minimize the effects of the missing measurement and gain variations on the estimation performance. Finally, extensive simulations and experiments are conducted to validate the effectiveness and resilience of the proposed method.

In this article, a distributed adaptive continuous-time optimization algorithm based on the Laplacian-gradient method and adaptive control is designed for resource allocation problem with the resource constraint and the local convex set constraints. In order to deal with local convex sets, a distance-based exact penalty function method is adopted to reformulate the resource allocation problem instead of the widely used projection operator method. By using the nonsmooth analysis and set-valued LaSalle invariance principle, it is proven that the proposed algorithm is capable of solving the nonsmooth resource allocation problem. Finally, two simulation examples are presented to substantiate the theoretical results.

This article investigates the adaptive performance guaranteed tracking control problem for multiagent systems (MASs) with power integrators and measurement sensitivity. Different from the structural characteristics of existing results, the dynamic of each agent is a power exponential function. A method called adding a power integrator technique is introduced to guarantee that the consensus is achieved of the MASs with power integrators. Different from existing prescribed performance tracking control results for MASs, a new performance guaranteed control approach is proposed in this article, which can guarantee that the relative position error between neighboring agents can converge into the prescribed boundary within preassigned finite time. By utilizing the Nussbaum gain technique and neural networks, a novel control scheme is proposed to solve the unknown measurement sensitivity on the sensor, which successfully relaxes the restrictive condition that the unknown measurement sensitivity must be within a specific range. Based on the Lyapunov functional method, it is proven that the relative position error between neighboring agents can converge into the prescribed boundary within preassigned finite time. Finally, a simulation example is proposed to verify the availability of the control strategy.

Many actual industrial production processes are dynamic and uncertain. When uncertain information are described by subjective experience and experts' knowledge based on scanty or vague information, fuzzy uncertainty exists. Fuzzy chance-constrained dynamic programming are applicable to industrial production modeling accompanied by fuzzy uncertainty and dynamics, where constraints need not or cannot be completely satisfied. In this article, a fuzzy chance-constrained dynamic optimization (FCCDO) formulation on the basis of credibility theory is established, in which, the credibility is used to measure the fuzzy uncertainty level of constraints. To solve the FCCDO problem (FCCDOP), an improved fuzzy simulation technique based on Hammersley sequence sampling is raised to transform fuzzy chance constraints to their deterministic equivalents, and then a data-driven state transition algorithm (DDSTA) using deep neural networks (DNNs) is put forward to achieve a stable, global and robust optimization performance. Finally, the successful applications of the FCCDO method to industrial studies demonstrate its advantages.

The issue of finite-time state estimation is studied for discrete-time Markovian bidirectional associative memory neural networks. The asymmetrical system mode-dependent (SMD) time-varying delays (TVDs) are considered, which means that the interval of TVDs is SMD. Because the sensors are inevitably influenced by the measurement environments and indirectly influenced by the system mode, a Markov chain, whose transition probability matrix is SMD, is used to describe the inconstant measurement. A nonfragile estimator is designed to improve the robustness of the estimator. The stochastically finite-time bounded stability is guaranteed under certain conditions. Finally, an example is used to clarify the effectiveness of the state estimation.

This work proposes a neural architecture search (NAS) method for portrait parsing, which is a novel up-level task based on portrait segmentation and face labeling. Recently, NAS has become an effective method in terms of automatic machine learning. However, remarkable achievements have been made only in image classification and natural language processing (NLP) areas. Meanwhile, state-of-the-art portrait segmentation and face labeling approaches are all manually designed, but few models reach a tradeoff between efficiency and performance. Thus, we are extremely interested in improving existing NAS methods for dense-per-pixel prediction tasks on portrait datasets. To achieve that, we resort to a cell-based encoder-decoder architecture with an elaborate design of connectivity structure and searching space. As a result, we achieve state-of-the-art performance on three portrait tasks, including 96.8% MIOU on EG1800 (portrait segmentation), 91.2% overall F1 -score on HELEN (face labeling), and 95.1% overall F1 -score on CelebAMask-HQ (portrait parsing) with only 2.29M model parameters. That is, our approach compares favorably with all previous works on portrait datasets. More crucially, we empirically prove that even a fundamental encoder-decoder architecture may reach an outstanding result on the aforementioned tasks with the help of the innovative approach of NAS. To the best of our knowledge, our work is also the first to report the success of applying NAS on these portrait tasks.

As edge computing platforms need low power consumption and small volume circuit with artificial intelligence (AI), we design a compact and stable memristive visual geometry group (MVGG) neural network for image classification. According to characteristics of matrix–vector multiplication (MVM) using memristor crossbars, we design three pruning methods named row pruning, column pruning, and parameter distribution pruning. With a loss of only 0.41% of the classification accuracy, a pruning rate of 36.87% is obtained. In the MVGG circuit, both the batch normalization (BN) layers and dropout layers are combined into the memristive convolutional computing layer for decreasing the computing amount of the memristive neural network. In order to further reduce the influence of multistate conductance of memristors on classification accuracy of MVGG circuit, the layer optimization circuit and the channel optimization circuit are designed in this article. The theoretical analysis shows that the introduction of the optimized methods can greatly reduce the impact of the multistate conductance of memristors on the classification accuracy of MVGG circuits. Circuit simulation experiments show that, for the layer-optimized MVGG circuit, when the number of multistate conductance of memristors is$2^{5}= 32$, the optimized circuit can basically achieve an accuracy of the full-precision MVGG. For the channel-optimized MVGG circuit, when the number of multistate conductance of memristors is$2^{2}= 4$, the optimized circuit can basically achieve an accuracy of the full-precision MVGG.

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

In this article, we investigate the pinning spatiotemporal sampled-data (SD) synchronization of coupled reaction-diffusion neural networks (CRDNNs), which are directed networks with SD in time and space communications under random deception attacks. In order to handle with the random deception attacks, we establish a directed CRDNN model, which respects the impacts of variable sampling and random deception attacks within a unified framework. Through the designed pinning spatiotemporal SD controller, sufficient conditions are obtained by linear matrix inequalities (LMIs) that guarantee the mean square exponential stability of the synchronization error system (SES) derived by utilizing inequality techniques, the stochastic analysis technique, and Lyapunov-Krasovskii functional (LKF). Finally, a numerical example is utilized to support the presented pinning spatiotemporal SD synchronization method.

We study the asymptotical consensus problem for multi-agent systems (MASs) consisting of a high-dimensional leader and multiple followers with unknown nonlinear dynamics under directed switching topology by using a neural network (NN) adaptive control approach. First, we design an observer for each follower to reconstruct the states of the leader. Second, by using the idea of discontinuous control, we design a discontinuous consensus controller together with an NN adaptive law. Finally, by using the average dwell time (ADT) method and the Barbǎlat's lemma, we show that asymptotical neuroadaptive consensus can be achieved in the considered MAS if the ADT is larger than a positive threshold. Moreover, we study the asymptotical neuroadaptive consensus problem for MASs with intermittent topology. Finally, we perform two simulation examples to validate the obtained theoretical results. In contrast to the existing works, the asymptotical neuroadaptive consensus problem for MASs is firstly solved under directed switching topology.

This article investigates the approximate optimal control problem for nonlinear affine systems under the periodic event triggered control (PETC) strategy. In terms of optimal control, a theoretical comparison of continuous control, traditional event-based control (ETC), and PETC from the perspective of stability convergence, concluding that PETC does not significantly affect the convergence rate than ETC. It is the first time to present PETC for optimal control target of nonlinear systems. A critic network is introduced to approximate the optimal value function based on the idea of reinforcement learning (RL). It is proven that the discrete updating time series from PETC can also be utilized to determine the updating time of the learning network. In this way, the gradient-based weight estimation for continuous systems is developed in discrete form. Then, the uniformly ultimately bounded (UUB) condition of controlled systems is analyzed to ensure the stability of the designed method. Finally, two illustrative examples are given to show the effectiveness of the method.

In the cooperative control for multiagent systems (MASs), the key issues of distributed interaction, nonlinear characteristics, and optimization should be considered simultaneously, which, however, remain intractable theoretically even to this day. Considering these factors, this article investigates leader-to-formation control and optimization for nonlinear MASs using a learning-based method. Under time-varying switching topology, a fully distributed state observer based on neural networks is designed to reconstruct the dynamics and the state trajectory of the leader signal with arbitrary precision under jointly connected topology assumption. Benefitted from the observers, formation for MASs under switching topologies is transformed into tracking control for each subsystem with continuous state generated by the observers. An augmented system with discounted infinite LQR performance index is considered to optimize the control effect. Due to the complexity of solving the Hamilton-Jacobi-Bellman equation, the optimal value function is approximated by a critic network via the integral reinforcement learning method without the knowledge of drift dynamics. Meanwhile, an actor network is also presented to assure stability. The tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded. Finally, two illustrative examples are given to show the effectiveness of this method.

This article focuses on developing distributed optimization strategies for a class of machine learning problems over a directed network of computing agents. In these problems, the global objective function is an addition function, which is composed of local objective functions. Such local objective functions are convex and only endowed by the corresponding computing agent. A second-order Nesterov accelerated dynamical system with time-varying damping coefficient is developed to address such problems. To effectively deal with the constraints in the problems, the projected primal-dual method is carried out in the Nesterov accelerated system. By means of the cocoercive maximal monotone operator, it is shown that the trajectories of the Nesterov accelerated dynamical system can reach consensus at the optimal solution, provided that the damping coefficient and gains meet technical conditions. In the end, the validation of the theoretical results is demonstrated by the email classification problem and the logistic regression problem in machine learning.

In this article, we establish an experience-correction (E-C) framework to deal with sampled-data fractional-order systems in the manner of general integer-order discrete-time model at sampling instants by coping with the memory effect. The designed E-C controller can be partitioned into local input and history input, respectively. Generated by current sampling, the local input is in a sense analogous to the input of integer-order system. In particular, the history input can correct all previous experience via incorporating earlier local inputs. Emphasize that the weights of earlier local inputs, norm of E-C controller together with corresponding sampling error are proved to descend to origin no slower than the Pareto function, i.e.,$\mathcal {O}(t^{-\alpha })$, where$\alpha $represents the derivative’s order. Similar to the well-known forgetting curve, we witness a rapid decline in the proportion of earlier past in E-C controller, which is with respect to one of the memory failures: transience, the deterioration of memory with the passage of time. The closer the order$\alpha $is to 1, the faster the forgetting is. The above analysis accounts for a phenomenon not previously understood: well-performed simulation results could still be expected in existing research who ignored memory effect and only considered local input. This is owing to the fractional orders are usually hypothesized to be larger than 0.5 and thus the weight of earlier past decays sharply over time. Furthermore, both absolute and proportional decline of earlier past’s weight will slow down when time goes by, which is consistent with memory consolidation and Jost’s law in psychology. Overall, the E-C method keeps an elegant formulation which theoretically describes the evolution of hereditary effect. Meanwhile, it is also suitable for first-order systems. Thereafter, we address related applications in fractional-order multiagent systems (MASs) subject to intermittent sampled-data communication. Numerical simulations are provided finally to substantiate the effectiveness of theoretical results.

In this article, a preassigned finite time control problem of nonlinear systems in strict-feedback form is investigated. From the perspective of arbitrary settling time, an appropriate preassigned finite-time performance function (PFPF) is constructed, and the preassigned finite-time stability (PAFS) is established, where the settling (convergence) time is not only completely unconcerned with initial conditions and design parameters but also more flexible. Furthermore, the backstepping technique and reduced-order observer are used to obtain the preassigned finite-time control scheme. The stability criteria of PAFS are developed to guarantee that the output can quickly converge to an arbitrarily small zone in preassigned time, and all signals of the closed-loop control system are PAFS. In the end, simulation examples verify the effectiveness of the presented method.

This article studies the resilient formation tracking control problem for spacecraft swarm while considering actuation attacks and input saturation. A distributed Lyapunov-based model predictive controller (DLMPC) framework is designed for spacecraft swarm to track the target trajectory in a preset formation shape and achieve attitude consensus. To ensure formation safety, a collision avoidance term is introduced into the DLMPC framework. To guarantee the feasibility and stability of the DLMPC, we first construct the Lyapunov-based adaptive auxiliary controller and then use its stability to construct the stability constraint. The DLMPC inherits the characteristic of the Lyapunov-based adaptive auxiliary controller and employs online optimization to guarantee better formation tracking performance. As a novel framework for spacecraft formation control, the proposed DLMPC has the advantage of improving the formation tracking performance through persistently online optimization, especially, in adversarial dynamic environments. The simulation results validate the superiority and resilience of the DLMPC, and the proposed DLMPC framework shows improvement in formation tracking performance.

This article addresses the tracking control problem of multiagent systems (MASs) with unknown dynamics. First, by designing a compensator, an augmented neighborhood error system is proposed. Then, a multithreading iterative$Q$-learning algorithm is developed, which can transform the tracking control problem into the optimal regulation of the augmented error system. In this algorithm, the agent controller is multithread, which consists of thread unit, global unit, and replay buffer. The thread unit is used to create the thread to collect the transitions with different control policies, which can reduce data relativity and improve data collection speed and exploration capability. The replay buffer is used to store the generated transitions. The global unit is used to generate the control policy. Moreover, the convergence analysis of the proposed algorithm is given, which shows that the neighborhood error converges to zero and the tracking control problem can be solved. Finally, the numerical example not only verifies the effectiveness of the obtained results but also shows the proposed algorithm performs better than the$Q$-iterative algorithm.

This work deals with the optimal epidemics policy-seeking problem on networks-of-networks (NoN) in the presence of unknown malicious adding-edge attacks. This problem is investigated in a framework of games-of-games (GoG), in which the conflicts between each network policymaker and the attacker are captured by a series of the Stackelberg games, while all network policymakers together compose a Nash game. First, the tolerable maximum attack magnitude is investigated and given implicitly. Then, we prove the existence of the gestalt Nash equilibrium (GNE) under mild attacks bounded by the above magnitude. A Heuristic algorithm based on iterative geometric programming is proposed to seek the GNE of the above GoG, whose asymptotical convergence is verified. Correspondingly, a greedy Heuristic strategy for the malicious attacker to compromise the NoN topology is developed. The practicability and validity of the above theoretical results and algorithms are illustrated via a simulation example.

In this article, based on the classical Degroot and Friedkin–Johnsen model, two opinion dynamics models with antagonistic relationship and multiple interdependent topics are proposed. The proposed models aim to characterize the evolution of individual opinions when individuals in a signed social network are able to discuss multiple interdependent topics simultaneously. Considering that different individuals may have different perceptions of the same thing, a set of heterogeneous logical matrices is used to represent the logical interdependence between different topics. In Model I with time-varying topologies, both the structurally balanced and unbalanced network topologies are investigated. Some sufficient conditions for the modulus consensus about topics are obtained in our study. About Model II with the stubborn individuals, we rigorously concentrate on its convergence and stability for the structurally balanced and unbalanced social networks. And some conditions on the convergence and stability are obtained. All the obtained conditions depend on the network topology and logical matrices and fully show how the network topology and logical matrices influence the evolution of opinions. Finally, two examples are used to verify our results.

This article proposes fixed-time stability lemmas for the Filippov system via some new inequality approaches. The adopted method no longer needs to integrate the Lyapunov function$V$on the two integral intervals, which is quite different from the existing ones. Some new estimations of the settling times are provided. Also, the steepness exponents of the Lyapunov function$V$in the previous fixed-time stability lemmas are improved. In order to further study the nondifferentiable delayed neural networks modeled by the Filippov system, a class of discontinuous uncertain Cohen–Grossberg neural networks (CGNNs) with mixed delays is formulated and the distributed delays are nondifferentiable, which is more general. Due to the existence of nondifferentiable distributed delays, the existence of the periodic solutions is proved by Kakutani’s fixed-point theorem before considering the stability. By virtue of the obtained fixed-time stability lemmas and the constructed delay-product-type Lyapunov–Krasovskii functional, the fixed-time stabilization is obtained via a no-chattering controller. Clearly, the designed controller does not contain integral terms and delay terms for dealing with the time delays in the closed-loop system, which is more simplified and practical. Finally, two examples help examine the correctness of the main results.

Safety is always one of the most critical principles for a control system. This article investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefiting from the dynamic regressor extension and mixing (DREM), an extended elementwise parameter identification law is utilized to dismiss the uncertainty. It is shown that the proposed control scheme can always ensure safety in the identification process with injected excitation noise. Besides, the elementwise identification process using DREM can minimize the theoretical conservatism of the safe adaptation law compared to other existing adaptive CBF (aCBF) algorithms. The stability of the proposed safe control scheme is proven, where the safety is guaranteed by constructing appropriate forward invariant aCBF. Furthermore, the robustness of our algorithms under bounded disturbances is analyzed. Finally, the proposed framework is tested on two simulation-based examples, including the adaptive cruise control problem where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances, and the potential crash risk is avoided by our safe control scheme. These examples illustrate the effectiveness of our algorithm.

Pinning control is applied to ensure the stabilization of Boolean networks (BNs) with time delay parameter. The time delay parameter in this article follows an independent identical distribution. Using semi-tensor product of matrices, the considered BN with time delay is converted to a high dimensional switching BN and the switching signal is the time delay signal. Different from general switching BN, the structure matrices of all subsystems are independent with each other, structure matrices of all subsystems for the high dimensional switching BN depend on the original BN structure matrix. Pinning control is designed to guarantee the global stochastic stability of the considered system. Furthermore, the global stochastic stability of BN with time delay parameter is proved to be equivalent to the stability of BN without time delay parameter, which greatly reduces the computational complexity and simplifies the control design. For the considered BN with finite cycles, constrained pinning control is applied with limitation that only one state of each undesired cycle is under control. With this constraint, the minimal number of pinning control nodes is further investigated. Some algorithms are presented to obtain the new structure matrix, with which, pinning control can be solved by the obtained methods. Both numerical examples and biological example illustrate the effectiveness of the results.

This article presents an innovative efficient safety-critical control scheme for nonlinear systems by combining techniques of control barrier function (CBF) and online time-varying optimization. The idea lies in that when directly calling the complete optimization solvers used in the CBF method, such as CBF-based quadratic programming (CBF-QP), is computationally inefficient for complex tasks, the suboptimal solutions obtained from online learning techniques can be an alternatively reliable choice for ensuring both efficiency and control performance. By using the barrier-based interior point method, the original optimization problem with CBF constraints is reduced to an unconstrained one with approximate optimality. Then, Newton- and gradient-based continuous dynamics are introduced to generate alternative cheap solutions while ensuring safety. By further considering the lag effect of online tracking, a prediction term is added to the dynamics. In this way, the online cheap solutions are proven to exponentially converge to the time-varying suboptimal solutions of the interior point method. Furthermore, the safety criteria are established, and the robustness of the designed algorithms is analyzed theoretically. Finally, the effectiveness is illustrated by conducting two experiments on obstacle avoidance and anti-swing tasks.

It is challenging and critical to achieve zero error consensus tracking in multiagent systems (MASs) with nonautonomous leaders (i.e., leaders with nonzero inputs). The traditional approach is to use discontinuous controllers which may cause a chattering phenomenon. How to achieve zero error consensus tracking via a chattering-free controller is still open. We propose a class of adaptive continuous controllers to achieve zero error consensus tracking for Lipschitz nonlinear MASs with a nonautonomous leader and directed communication topology. Unlike existing works that use discontinuous functions to eliminate the impacts of leaders’ inputs, we use a continuous function by introducing an exponential decay function into the denominator. First, we design a continuous controller with fixed coupling strengths and prove that zero error consensus tracking can be achieved if the coupling strengths are greater than some positive constants. Second, we design a continuous controller with dynamic coupling strengths under which fully distributed zero error consensus tracking can be achieved. Moreover, the case with undirected communication topology is studied. Finally, three examples are given to verify the theoretical results. Specifically, convergence results between the continuous controller here and that is developed via the boundary layer technique are compared. Compared with existing works, the designed adaptive continuous controllers here can not only achieve zero error consensus tracking but also is chattering free.

This article focuses on the secure consensus problem of linear multiagent systems (MASs) with quantized event-triggered control (ETC) against denial-of-service (DoS) attacks. DoS attacks with constrains on frequency and duration are discussed, which intend to block the communication links between agents to destroy the consensus. Due to the limited transmission capacity and communication resources, a uniform quantizer and an event-triggered mechanism (ETM) are considered to economize energy consumption. An ETC protocol based on the quantized relative state is designed to resist malicious DoS attacks. Then, sufficient conditions to guarantee the practical consensus of MASs are derived with finite data transmission rates, and the tolerance of DoS attack frequency and duration are given. The Zeno behavior does not exhibit by proving that lower positive bounds exist for all agents, indicating the feasibility of the proposed ETM. Finally, two simulation examples are given to verify the validity and superiority of the theoretical results.

This article addresses the dynamic task allocation problem with limited communication and velocity. The main challenge lie in the selection of$k$fittest winner participants and the participant contention that one winner participant may be selected by multiple targets simultaneously. Existing methods take the distance between the targets and participants as the evaluation index to select winners, which may lead to futile selection since the winner participant locating at the opposite direction of the target cannot intercept the target with limited velocity. By carefully considering both the distance between the targets and participants and the motion direction of the targets, an improved evaluation index for each target is proposed and employed such that the futile selection can be avoided in the executing process of the algorithm. Moreover, an extra evaluation index for each winner participant is presented to select one winner target to overcome the participant contention. Based on these, the control protocols are developed for targets interception, and their stability is proven by the Lyapunov theory under some suitable conditions. Finally, simulation examples are presented to illustrate the effectiveness and advantages of the proposed algorithms.

In this article, the global polynomial synchronization (GPS) using proportional delay inertial neural networks (PDINNs) as a drive-response system is studied. A feedback controller is designed, which is easy to implement in hardware. The order reduction method is used to reduce the error system of the studied systems to the first-order differential equations. The novel proportional delay differential inequalities (DDIs) in norm form are established by applying Lagrange’s mean value theorem, derivative definition, constructing auxiliary function method, and properties of matrix and vector norm. By applying the novel constructed DDIs, two GPS criteria of the studied systems are acquired, which are embodied in the form of matrix norm and are easy to verify. Ultimately, the theoretical results are supported by numerical examples.

As is known to all that the Lyapunov-Krasovskii functional (LKF) has played a vital role in studying the fixed-time (FXT) stability of the time-delayed Filippov discontinuous systems. When investigating the FXT stability of the time-delayed discontinuous systems, the interesting topic is to provide the more relaxed conditions imposed on the LKF and obtain the more accurate settling-time (ST). This paper aims to investigate the new FXT stability lemma with more relaxed conditions for the time-delayed Filippov discontinuous systems. By using some novel inequality techniques and analytical methods, the new FXT stability lemma is established and more accurate ST is estimated. The traditional discussion about$0 < V < 1$and$V \geq 1$is not required. For the purpose of validation, FXT stabilization of discontinuous inertial networks with mixed time delays is considered. By using differential inclusions theory and the new FXT stability lemma, the delay-dependent FXT stabilization criteria are derived. It is the first time to derive the delay-dependent FXT stabilization criteria for the discontinuous inertial networks. Finally, examples are given to show the effectiveness of the established results.

This paper deals with global synchronization problem of multiple discrete-time Markovian jump memristor-based neural networks (DTMJMNNs) with mixed mode-dependent delays via a novel event-triggered impulsive coupling control (ETICC). The parameters of the multiple DTMJMNNs and the mixed time delays (both discrete and distributed delays) switch randomly according to a Markov chain. In the ETICC strategy, the controller does not work all the time, but only works at impulse instants determined by specific events. In particular, the coupling matrix can be non-Laplacian. By using the Lyapunov stability theory, linear matrix inequalities (LMIs), and the Kronecker product, some sufficient conditions for global synchronization of multiple DTMJMNNs under the event-triggered strategy are derived. Two examples are presented to test the validity of the theoretical analysis results.

This paper investigates the leader-following consensus control problem for a class of nonlinear multiagent systems subject to sensor and actuator faults under a fixed directed graph. First, a fault compensation mechanism is proposed because of multiple faults wherein the adaptive parameters substitute the fault coefficients. Then, the command filtering method is employed to avoid the burst of complexity rendered by the duplicative differentiation of the virtual control signal. Furthermore, the neural networks-based state observers are designed to reconstruct the unmeasurable states of the nonlinear multiagent systems. According to the given design approach, a switching threshold-based event-triggered adaptive fault-tolerant control strategy is developed and ensures all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, the simulation result is provided to demonstrate the validity of the presented method.

This work focuses on the stability analysis of positive Takagi-Sugeno (T-S) fuzzy descriptor systems with time-varying delays. An equivalent augmented system is constructed to investigate the positivity and stability of T-S fuzzy descriptor time-delay systems. By using this transformed system, a necessary and sufficient positivity condition is first derived for systems, which can be verified by linear programming (LP). Then, based on the positivity of T-S fuzzy descriptor systems, a sufficient condition is put forward for the asymptotic stability of systems with bounded and unbounded time-varying delays. Finally, several examples are provided to show the effectiveness of the obtained results.

We aim to address the Nash equilibrium (NE) seeking problem for multiple players over Markovian switching communication networks in this article, where a new type of distributed synchronous discrete-time algorithm is proposed and utilized. Specifically, each player in the present game model is assumed to employ a gradient-like projection algorithm to choose its action based upon the estimated ones for all the others. Under the mild condition that the union network of all communication network candidates is connected, we show that the players' actions could converge to an arbitrarily small neighborhood of the NE in the mean-square sense by adjusting the algorithm parameters. It is further found that the unique NE is mean-square stable when it is not restricted by any constraint set. In addition, we show that the proposed distributed discrete-time NE seeking algorithm can be utilized to deal with the energy trading problem in microgrids where each microgrid is modeled as a rational player using a purchase price as its action to buy energy from other microgrids with surplus supplies. The energy market allocates the excess energy according to the principle of proportional distribution. Some numerical simulations are finally presented to verify the validity of the present discrete-time NE seeking algorithm in solving the energy trading problem.

The analysis and design problems of formation-containment control for high-order linear time-invariant (LTI) multiagent systems (MASs) on directed graphs with observer-based output-feedback protocols are given in this work. To expand the feasibility of state formation configuration, two well-structured compensation signals are introduced for the leaders and followers in the protocols, respectively. Benefitting from the compensation signal of followers, the decoupling between formation control of leaders and containment control of followers is achieved. Thus, a necessary and sufficient condition is first established such that the formation-containment control for high-order LTI MASs can be achieved. Moreover, a heuristic iterative algorithm is developed to compute the controller gains, observer gains, as well as the compensation signals. Finally, two numerical examples are implemented to illustrate the time-varying formation-containment control of high-order MASs, which shows the validity and practicability of the theoretical results and algorithm.

For networked control systems, it is known that various communication parameters in the channel will pose some fundamental limitations on output tracking control (OTC) performance. In this study, we mainly discuss the limitations resulting from model uncertainties, involving channel and plant. Through using the bivariate stochastic process to model packet loss, and the assumption that channel noise is additive white Gaussian noise (AWGN), two explicit expressions of output tracking performance limitations are derived with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structure, which shows that the performance of OTC is closely related to the inherent characteristics of the plant, as well as the packet loss rate and power spectral density (PSD) of AWGN. Finally, by considering an illustrative example, the simulation results are verified and analyzed to ensure the effectiveness of treatment methods and results.

The output tracking control problem is investigated in this article. First, a new tradeoff performance index is presented for single-input-multioutput (SIMO) systems. Based on the frequency-domain method, the tracking performance limitations under time delay, packet loss, and channel noise effects are derived. We use a bivariate stochastic process to model the packet loss, and assume that channel noise is additive white Gaussian noise (AWGN). Two explicit expressions of the best tradeoff performance are given with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structures. It is shown that the tracking control performance has a close relation with the intrinsic characteristic of the plant, as well as the time delay, packet-dropouts rate, and power spectral density of AWGN. We also demonstrate that compared with the SDOF control structure, the TDOF control structure can improve the systems' attainable performance. A simulation example is finally discussed to validate the conclusions.

This article discusses the fixed-time stability (FTS) of a kind of delayed discontinuous system (DS) in Filippov sense. Based on the set-valued map, the FTS analysis of the general solution is first transformed into the zero solution of the differential inclusion. Second, the new criteria of the Lyapunov-Krasovskii functional (LKF) are given and LKF is proved to possess the indefinite derivatives by using the simple integral inequalities. In addition, the FTS of the considered delayed DS is achieved and the new settling time is estimated. Third, to demonstrate the applicability of the new FTS theorems, the FTS control of a class of discontinuous inertial neural networks (DINNs) with time-varying delays is solved. Finally, two numerical examples are given to examine the theoretical results and simulations are also provided to make some illustrations.

This article explores the exponential stabilization issue of a class of state-based switched inertial complex-valued neural networks with multiple delays via event-triggered control. First, the state-based switched inertial complex-valued neural networks with multiple delays are modeled. Second, by separating the real and imaginary parts of complex values, the state-based switched inertial complex-valued neural networks are transformed into two state-based switched inertial real-valued neural networks. Through the variable substitution method, the model of the second-order inertial neural networks is transformed into a model of the first-order neural networks. Third, an event-triggered controller with the transmission sequence is designed to study the exponential stabilization issue of neural networks constructed above. Then, by constructing the Lyapunov functions and based on some inequalities, we obtain sufficient conditions for exponential stabilization of the proposed neural networks. Furthermore, it is proved that the Zeno phenomenon cannot happen under the designed event-triggered controller. Finally, a simulation example is given to illustrate the correctness of the results.

This article focuses on multiagent distributed-constrained optimization problems in a dynamic environment, in which a group of agents aims to cooperatively optimize a sum of time-changing local cost functions subject to time-varying coupled constraints. Both the local cost functions and constraint functions are unrevealed to an individual agent until an action is submitted. We first investigate a gradient-feedback scenario, where each agent can access both values and gradients of cost functions and constraint functions owned by itself at the chosen action. Then, we design a distributed primal-dual online learning algorithm and show that the proposed algorithm can achieve the sublinear bounds for both the regret and constraint violations. Furthermore, we extend the gradient-feedback algorithm to a gradient-free setup, where an individual agent has only attained the values of local cost functions and constraint functions at two queried points near the selected action. We develop a bandit version of the previous method and give the explicitly sublinear bounds on the expected regret and expected constraint violations. The results indicate that the bandit algorithm can achieve almost the same performance as the gradient-feedback algorithm under wild conditions. Finally, numerical simulations on an electric vehicle charging problem demonstrate the effectiveness of the proposed algorithms.

This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stability (GAS) and global exponential stability (GES) of the underlying DBAMNNs are of concern in terms of p -norm ( p ≥ 2 ). Meanwhile, GES of the addressed DBAMNNs is also analyzed in terms of 1-norm. When distributed time delay is neglected, the GES of the corresponding bidirectional associative memory neural networks is presented as an M -matrix, which includes certain existing outcomes as special cases. Two examples are finally provided to substantiate the validity of theories.

This article investigates finite-time stabilization of competitive neural networks with discrete time-varying delays (DCNNs). By virtue of comparison strategies and inequality techniques, finite-time stabilization of the underlying DCNNs is analyzed by designing a discontinuous state feedback controller, which simplifies the controller design and proof processes of some existing results. Meanwhile, global exponential stabilization of the DCNNs is provided under a continuous state feedback controller. In addition, global exponential stability of the DCNNs is shown as an M-matrix, which contains some published outcomes as special cases. Finally, three examples are given to illuminate the validity of the theories.

These days, the increasing incremental cost consensus-based algorithms are designed to tackle the economic dispatch (ED) problem in smart grids (SGs). However, one principal obstruction lies in privacy disclosure for generators and consumers in electricity activities between supply and demand sides, which may bring great losses to them. Hence, it is extraordinarily essential to design effective privacy-preserving approaches for ED problems. In this article, we propose a two-phase distributed and effective heterogeneous privacy-preserving consensus-based (DisEHPPC) ED scheme, where a demand response (DR)-based framework is constructed, including a DR server, data manager, and a set of local controllers. The first phase is that Kullback-Leibler (KL) privacy is guaranteed for the privacy of consumers' demand by the differential privacy method. The second phase is that (ε,δ) -privacy is, respectively, achieved for the generation energy of generators and the sensitivity of electricity consumption to electricity price by designing the privacy-preserving incremental cost consensus-based (PPICC) algorithm. Meanwhile, the proposed PPICC algorithm tackles the formulated ED problem. Subsequently, we further carry out the detailed theoretical analysis on its convergence, optimality of final solution, and privacy degree. It is found that the optimal solution for the ED problem and the privacy preservation of both supply and demand sides can be guaranteed simultaneously. By evaluation of a numerical experiment, the correctness and effectiveness of the DisEHPPC scheme are confirmed.

We aim to address the consensus tracking problem for multiple-input-multiple-output (MIMO) linear networked systems under directed switching topologies, where the leader is subject to some nonzero but norm bounded inputs. First, based on the relative outputs, a full-order unknown input observer (UIO) is designed for each agent to track the full states' error among neighboring agents. With the aid of such an observer, a discontinuous feedback protocol is subtly designed. And it is proven that consensus tracking can be achieved in the closed-loop networked system if the average dwell time (ADT) for switching among different interaction graph candidates is larger than a given positive threshold. By using the boundary layer technique, a continuous feedback protocol is skillfully designed and employed. It is shown that the consensus error converges into a bounded set under the designed continuous protocol. Second, as part of the full states' error can be constructed via the agents' outputs, a reduced-order UIO is thus designed based on which discontinuous and continuous feedback protocols are, respectively, proposed. By using the stability theory of the switched systems, it is proven that the consensus error converges asymptotically to 0 under the designed discontinuous protocol, and converges into a bounded set under the designed continuous protocol. Finally, the obtained theoretical results are validated through simulations.

This article tackles the global exponential stability for a class of delayed complex-valued inertial neural networks in a discrete-time form. It is assumed that the activation function can be separated explicitly into the real part and imaginary part. Two methods are employed to deal with the stability issue. One is based on the reduced-order method. Two exponential stability criteria are obtained for the equivalent reduced-order network with the generalized matrix-measure concept. The other is directly based on the original second-order system. The main theoretical results complement each other. Some comparisons with the existing works show that the results in this article are less conservative. Two numerical examples are given to illustrate the validity of the main results.

This article aims to propose time-delayed and simplified inequality conditions and establish more novel fixed-time (FXT) stability principles of delayed Filippov systems. First, the sign function is used to unify the orders of the Lyapunov–Krasovskii functional, and the time-delayed term is introduced in the inequality conditions, then some new FXT stability theorems are established and new estimations of the settling time involving delayed parameter are given that are absolutely different from the previous ones. The traditional inequality conditions$\dot{V}(z)\leq -aV^\alpha (z)-bV^\beta (z)$is optimized and improved. For the purpose of illustrating the applicability, a class of delayed discontinuous fuzzy Cohen–Grossberg neural networks (DDFCGNNs) is studied. Some new criteria are derived, and the FXT stabilization of the addressed DDFCGNNs is achieved. Finally, a representative example and simulations are provided to examine the correctness.

In this article, the issue of intermittent event-triggered exponential stabilization for state-dependent switched fuzzy neural networks with mixed delays is discussed and resolved. By combining event-triggered control with intermittent control, an intermittent event-triggered control strategy is proposed, which only focuses on events during control time. Consequently, compared with some existing event-triggered control strategies, it greatly reduces the amount of samplings and saves control costs. Based on the fuzzy intermittent event-triggered controller designed in this article, two event-triggering mechanisms are proposed to determine the trigger instants. In this context, some easily testified algebraic conditions are obtained for the exponential stabilization of the state-dependent switched fuzzy neural networks with mixed delays. In addition, a positive lower bound for the inter-event time is given. The results of theoretical analysis are verified through a simulation example.

This article introduces a spatially local piecewise fuzzy control (SLPFC) for nonlinear delayed distributed parameter systems (DPSs) with random packet losses. Due to the limited bandwidth of the channels, the random packet losses could occur simultaneously in the communication channels from the sensor to the controller and from the controller to the actuator, which are assumed to obey the Bernoulli random binary distribution. An SLPFC design is given such that, for all possible random packet losses, the closed-loop delayed DPS is mean square exponentially stable. Delay-dependent conditions are obtained and then two procedures are given for designing the spatially local piecewise fuzzy controller: one casts the controller design into an iterative linear matrix inequality (LMI) algorithm and the other casts the controller design into a parameter-dependent LMI problem. Two examples are provided to illustrate the usefulness of the presented design approach.

In this article, we devote to solve the consensus problem for nonlinear multiagent system under energy consumption constraint. A novel control protocol which combines impulse control with event-triggered mechanism is proposed. By setting the event decision time at pulse time, it can not only reduce triggered times but also avoid the occurrence of “Zeno behavior.” In order to reduce the conservatism of control protocol, input saturation constraint and state saturation constraint are both discussed, for the limited receiving channel of the agent and limited sampling range of the sampler, respectively. Further, the dynamic model of multiagent systems with fuzzy information is proposed for the uncertain factors in actual environment. Finally, for the sake of proving the effectiveness about the control protocols, some simulation examples are presented.

Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion (LRTC) has achieved unprecedented success in addressing various pattern analysis issues. However, existing studies mostly focus on third-order tensors while order- d ( d ≥ 4 ) tensors are commonly encountered in real-world applications, like fourth-order color videos, fourth-order hyper-spectral videos, fifth-order light-field images, and sixth-order bidirectional texture functions. Aiming at addressing this critical issue, this paper establishes an order- d tensor recovery framework including the model, algorithm and theories by innovatively developing a novel algebraic foundation for order- d t-SVD, thereby achieving exact completion for any order- d low t-SVD rank tensors with missing values with an overwhelming probability. Emperical studies on synthetic data and real-world visual data illustrate that compared with other state-of-the-art recovery frameworks, the proposed one achieves highly competitive performance in terms of both qualitative and quantitative metrics. In particular, as the observed data density becomes low, i.e., about 10%, the proposed recovery framework is still significantly better than its peers. The code of our algorithm is released at https://github.com/Qinwenjinswu/TIP-Code.

This article studies the observer-based adaptive synchronization of multiagent systems (MASs) with unknown parameters under attacks. First, to estimate the state of agents, the observer for MAS is introduced. When disturbance, nonlinear function, and system model uncertainty are not considered, the nominal controller is proposed to achieve synchronization and state estimation. Then, in order to eliminate the effect of unknown parameters in the disturbance, nonlinear function, and system model uncertainty, the adaptive controller with switching term is introduced. However, the attack will lead to the destruction of the network topology so as the destruction of the nominal controller. By constructing an appropriate Lyapunov function, we analyze the effect caused by attacks, and the security control law is given to make sure the synchronization of the MASs under attacks. Finally, a numerical simulation is given to verify the validness of the obtained theorem.

Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural network circuit designs, however, are based on generic frameworks that are not optimized for memristors. Furthermore, to the best of our knowledge, there are no existing efficient memristor-based implementations of complex neural network operators, such as deconvolutions and squeeze-and-excitation (SE) blocks, which are critical for achieving high accuracy in common medical image analysis applications, such as semantic segmentation. This article proposes convolution-kernel first (CKF), an efficient scheme for designing memristor-based fully convolutional neural networks (FCNs). Compared with existing neural network circuits, CKF enables effective parameter pruning, which significantly reduces circuit power consumption. Furthermore, CKF includes the novel, memristor-optimized implementations of deconvolution layers and SE blocks. Simulation results on real medical image segmentation tasks confirm that CKF obtains up to 56.2% reduction in terms of computations and 33.62-W reduction in terms of power consumption in the circuit after weight pruning while retaining high accuracy on the test set. Moreover, the pruning results can be applied directly to existing circuits without any modification for the corresponding system.

This article investigates the joint effects of connection weight and time delay on pattern formation for a delayed reaction-diffusion BAM neural network (RDBAMNN) with Neumann boundary conditions by using the$({k_{1}},{k_{2}})$mode Hopf-zero bifurcation. First, the conditions for${k_{1}}$mode zero bifurcation are obtained by choosing connection weight as the bifurcation parameter. It is found that the connection weight has a great impact on the properties of steady state. With connection weight increasing, the homogeneous steady state becomes inhomogeneous, which means that the connection weight can affect the spatial stability of steady state. Then, the specified conditions for the${k_{2}}$mode Hopf bifurcation and the$({k_{1}},{k_{2}})$mode Hopf-zero bifurcation are established. By using the center manifold, the third-order normal form of the Hopf-zero bifurcation is obtained. Through the analysis of the normal form, the bifurcation diagrams on two parameters’ planes (connection weight and time delay) are obtained, which contains six areas. Some interesting spatial patterns are found in these areas: a homogeneous periodic solution, a homogeneous steady state, two inhomogeneous steady state, and two inhomogeneous periodic solutions.

-minimization problem is proposed, which is based on classic PNN and sliding mode control technique. Furthermore, the proposed network can be used to make sparse signal reconstruction and image reconstruction. First, a sign function is introduced into the PNN model to design fixed-time PNN (FPNN). Then, under the condition that the projection matrix satisfies the restricted isometry property (RIP), the stability and fixed-time convergence of the proposed FPNN are proved by the Lyapunov method. Finally, based on the experimental results of signal simulation and image reconstruction, the proposed FPNN shows the effectiveness and superiority compared with that of the existing PNNs.

This article works on the consensus problem of nonlinear multiagent systems (MASs) under directed graphs. Based on the local output information of neighboring agents, fully distributed adaptive attack-free protocols are designed, where speaking of attack-free protocol, we mean that the observer information transmission via communication channel is forbidden during the whole course. First, the fixed-time observer is introduced to estimate both the local state and the consensus error based on the local output and the relative output measurement among neighboring agents. Then, an observer-based protocol is generated by the consensus error estimation, where the adaptive gains are designed to estimate the unknown neural network constant weight matrix and the upper bound of the residual error vector. Furthermore, the fully distributed adaptive attack-free consensus protocol is proposed by introducing an extra adaptive gain to estimate the communication connectivity information. The proposed protocols are in essence attack-free since no observer information exchange among agents is undertaken during the whole process. Moreover, such a design structure takes the advantage of releasing communication burden.

In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural network (MCNN) layer, two single-memristive blaze blocks (SMBBs), four double-memristive blaze blocks (DMBBs), a global Avg-pooling (GAP) layer, and a memristive full connected (MFC) layer. SMBBs and DMBBs mainly utilize the depthwise separable convolution neural network (DwCNN) that can be implemented with a much smaller memristor crossbar (MC). In the backward propagation, we use batch normalization (BN) layers to accelerate the convergence. In the forward propagation, this circuit combines DwCNN layers/CNN layers with nonseparate BN layers, which means that the required number of operational amplifiers is cut by half as long as the greatly reduced power consumption. A diode is added after the rectified linear unit (ReLU) layer to limit the output of the circuit below the threshold voltage$V_{t}$of the memristor; thus, the circuit is more stable. Experiments show that the proposed memristor-based circuit achieves an accuracy of 84.38% on the CIFAR-10 data set with advantages in computing resources, calculation time, and power consumption. Experiments also show that, when the number of multistate conductance is 28and the quantization bit of the data is 8, the circuit can achieve its best balance between power consumption and production cost.

The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently. However, minimizing the t-TNN faces some drawbacks. For example, the obtained solution could be suboptimal to the original problem due to its loose approximation. In this article, we extract a unified nonconvex surrogate of the tensor tubal rank as a tighter regularizer, which involves many popular nonconvex penalty functions. An iterative reweighted t-TNN algorithm is proposed to solve the resulting generalized nonconvex tubal rank minimization for tensor recovery. It converges to a critical point globally with rigorous proofs based on the Kurdyka-Łojasiwicz property. Furthermore, we provide the theoretical guarantees for exact and robust recovery by developing the tensor null space property. Extensive experiments demonstrate that our approach markedly enhances recovery performance compared with several state-of-the-art convex and nonconvex methods.

Distributed stabilization problem is studied in this article for multiple heterogeneous agents in the uncertain strong–weak competition network with exogenous disturbances, where the agents are modeled by the second-order systems with different nonlinear intrinsic dynamics, and the network uncertainty is characterized by unknown nonzero parameters, which contains three different relationships among agents: 1) cooperation; 2) strong competition; and 3) weak competition. To achieve distributed stabilization, the whole network is first divided into two parts: 1) identifiable part and 2) unidentifiable part, and a new distributed robust integral sign of the error (RISE) controller is designed for each agent, where the selection rules of the corresponding parameters are given. It is shown that the heterogeneous multiagent system (MAS) can achieve distributed stabilization no matter whether the identifiable part is structurally balanced or not. Furthermore, it is proved that the global distributed stabilization is achieved for the heterogeneous agents provided that the partial derivatives of the nonlinear intrinsic dynamics are bounded. Finally, two numerical examples are given to demonstrate the effectiveness of the designed controller.

This article deals with the fully distributed dual-terminal dynamic event-triggered bipartite output containment control of heterogeneous linear multiagent systems (HLMASs) subject to actuator faults. First, a class of fully distributed dynamic event-triggered observers is proposed over the directed signed communication network for each follower to estimate the leaders’ system matrices and (symmetric) combination states, which are merely available to a small fraction of followers. Then, based on the estimations of leaders’ system matrices, adaptive algorithms are employed to solve the regulator equations. Furthermore, the fully distributed observer-based dynamic event-triggered controllers are proposed by integrating the adaptive gains to compensate for actuator faults. Dual-terminal dynamic event-triggered mechanisms (ETMs) are addressed to exclude not only continuous control updates but also continuous communication among agents. Correspondingly, some mild criteria and the rigorous proofs are established to ensure the implementation of bipartite output containment through the Lyapunov stability theory. Moreover, the strictly positive lower bounds of intervals between two adjacent event-triggered time instants are derived, which indicates that Zeno behavior is ruled out in the dual-terminal dynamic ETMs. Finally, numerical simulations are performed to demonstrate the theoretical analysis.

In this article, a class of discontinuous inertial neural networks (DINNs) with parameter uncertainties and time delays is studied. The main aim is to investigate the new fixed-time stability (FTS). In order to achieve the targets, first, by introducing the generalized variable transformation and differential inclusions theory, two kinds of drive–response differential inclusion systems are established. Based on the definition of FTS and inequality technologies, by constructing the Lyapunov–Krasovskii functional (LKF), whose derivative is allowed to be indefinite, new delay-dependent criteria shown by some simple inequalities are derived for the purposing of achieving the FTS based on the designed discontinuous control strategies. Moreover, the new settling time (ST) is given. Compared to the previous stability results on INNs, the results established and the approaches applied are absolutely new. Finally, examples are given to show the effectiveness of the established results.

In this article, we consider a class of decentralized nonsmooth composite optimization problems over undirected graphs. The global optimization problem is to minimize the sum of local objective functions consisting of a Lipschitz-differentiable convex function and two possibly nonsmooth convex functions, one of which contains a bounded linear operator. The goal is to solve the global optimization problem through decentralized computation and communication over a network of agents without a central coordinator. Through using triple proximal splitting operators to deal with the nonsmooth terms, we come up with a novel decentralized algorithm with uncoordinated stepsizes, where the stepsizes with independent upper bounds are also distributed for agents or edges over the communication network. Furthermore, we establish the sublinear convergence rate for the proposed algorithm in terms of the first-order optimality residual in a nonergodic sense. Simulation experiments on a constrained quadratic programming problem and an optimal load-sharing problem are carried out to verify the correctness of the theoretical results.

This article studies the formation control problem of second-order multiagent networks, in which cooperative and antagonistic interactions of the agents spontaneously coexist in the communication process. Based on the convex analysis theory, several convex polytopes that do not require some kinds of system constraints are constructed in the presence of these interactions. Then, the matrix perturbation theory and some mathematical techniques are utilized to analyze these convex polytopes. The obtained results show that the agents with cooperative interactions monotonously converge to their own specified formation shape while maintaining the desired relative position of the other agents with antagonistic interactions. Subsequently, two numerical examples are presented to illustrate the obtained results.

The fully distributed consensus tracking of stochastic nonlinear multiagent systems (MASs) is investigated with Markovian switching topologies and intermittent control strategy, where the dynamics of agents are depicted by Itô differential equations and the leader’s information is just known for a fraction of followers. The switching mechanism of interaction topologies is modeled as a Markov process. A novel class of fully distributed control protocols is proposed via intermittent control method, which is only associated with the relative state measurements of neighbors and does not involve any global information. Meanwhile, the control gains are designed to be intermittently adaptive, which can effectively reduce energy consumption and avoid the gains being larger than those needed in practice. Several sufficient conditions and corresponding proofs are provided by using the Lyapunov stability theory. Finally, numerical simulation is presented to state the feasibility of the theoretical results.

This article addresses the cooperative fault-tolerant control problem for networks of stochastic nonlinear systems with actuator faults and input saturation. The fuzzy neural networks (FNNs) are employed to estimate the unknown functions and stochastic disturbance terms. To analyze the nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is constructed to estimate the saturation function. A novel adaptive fault-tolerant control protocol is proposed by using backstepping design technique. By using the stochastic Lyapunov functional strategy, it is proved that all the followers’ outputs eventually converge to a small neighborhood of the leader’s output, and all the signals in the closed-loop systems are bounded in probability. Finally, the performance of the proposed control strategy is illustrated through simulation.

This article investigates global exponential stabilization (GES) of Takagi–Sugeno (T–S) fuzzy memristive neural networks with multiple time-varying delays (DFMNNs) via intermittent control strategy. By resorting to differential inclusion theory, comparison means, and inequality techniques, some results are developed to ensure GES of the underlying DFMNNs via a fuzzy intermittent state feedback control law within the sense of Filippov. The outcome is generalized to GES of FMNNs with infinite distributed time delays. Additionally, the global exponential stability of FMNNs with discrete time-varying delays is explored in terms of 1-norm. The derived conditions herein contain certain existing ones as special cases. Finally, three examples are presented to illuminate the validness of the outcomes.

Synchronization of a class of drive-response timescale-type nonautonomous proportional-delayed neural networks (TNPNNs) is addressed in this article. The key technique to cope with the proportional term is using comparison principle. By timescale theory, inequality technique, and comparison principle, criteria of synchronization are obtained. The method used in this article is effective to cope with TNPNNs and it is a direct approach as well by eliminating the conventional exponential transformation. The obtained results are verified with three examples.

In this article, the containment control problem of multiagent systems (MASs) with active leaders under a directed communication graph is addressed. A novel pull-based event-triggered protocol combined with aperiodic sampled-data mechanism is first presented, where each agent merely updates controller at its own triggering instants. Under the proposed control protocols, the information is only exchanged and calculated at the aperiodic sampling instants, therefore, it can reduce communication congestion and save computing resources. Furthermore, the Zeno behavior is naturally excluded because the trigger instants are in the set of aperiodic sampled instants. By virtue of the algebraic graph theory and Lyapunov stability analysis, it is shown that the active leaders can reach their desired states predefined arbitrarily and all followers are driven to the convex hull formed by the leaders. Finally, an illustrative example is given to validate the theoretical results and emphasize the advantages of the proposed protocols.

In this article, we propose a memristor-based ShuffleNetV2 for image classification. Because of the low power consumption and high integration, this circuit is suitable for edge computing. The memristor-based ShuffleNetV2 is divided into four kinds of units, and each unit is composed by a series of basic memristive neural circuits, such as memristive convolutional neural networks (MCNNs), memristive batch normalization (MBN) layers, memristive fully connection (MFC) layers, rectified linear unit (ReLU) layers, max-pooling layers, etc. To solve the imbalance of required memristor crossbars in MCNN and the huge power consumption of the input voltage inverting, the inverters are shifted behind the memristor crossbar.This circuit uses (M+1)×2 memristor crossbars instead of the original (2M+1)×1 memristor crossbars. The world lines of memristor crossbars required can be reduced from (2M+1) to (M+1). The number of inverters in MCNN for a kernel can be decreased to one, and the power consumption in MCNN is greatly decreased. The MBN is designed by combining operational amplifiers with memristors. An output limiter circuit is added behind the ReLU layer implemented by diode. The output voltage of the ReLU layer is limited to the threshold voltage Vpof memristor. The memristive ShuffleNetV2 is established for image classification in experiments. The effectiveness of the circuit is verified on the FER2013 dataset along with the analysis of the memristor resources, calculation periods, and circuit power consumption of the network. The experimental results show that this that the inference time of this memristive ShuffleNetV2 could be as less as 137 ns with a max power consumption 2.8 μW of a memristive neurons.

This article presents a new convolution algorithm: convolution kernel first operated (CKFO), which can solve the problem that the actual calculation is not reduced after pruning the weight of the convolution neural network. According to the convolution algorithm, this article proposes a simulated memristor implementation of a convolutional neural network (CNN). After that, we use the method of ex-situ training to train CNN in Tensorflow and then download the trained parameters to the Simulink system by compiling the conductance value of memristor to test the proposed simulation model. Finally, the effectiveness of the proposed model is verified. In addition, we prune the weights of CNN and retrain it, then adjust the simulation model according to the parameters after being pruned. We are surprised to find that the convolution layer designed according to the new convolution algorithm can apply the results of the pruned weight without any modification to the circuit, which is very cumbersome in other memristor-based CNN because the distribution of the pruned weight is irregular. The parameters are reduced by 75.24% and the number of multiplication operations in the convolution layer was reduced by 30.1%, while the accuracy is just reduced by 0.06%.

In 1971, Dr. Leon Chua, known as the father of nonlinear circuits and cellular neural networks, postulated the existence of memristor, a portmanteau of memory resistor, in his seminal paper: Memristor-the missing circuit element published in IEEE Transactions on Circuit Theory, the predecessor of IEEE Transactions on Circuits and Systems. Thirty-seven years after he predicted its existence, in the May 1 (2008) issue of the journalNature, a team at HP Labs led by the scientist R. S. Williams proved that the memristor was real by formulating a physics-based model of a memristor and build nanoscale devices in their lab that demonstrate all of the necessary operating characteristics. Since then, the extensive interest of academic and industrial circles on neuromorphic computing based on memristor has been skyrocketed. Moreover, the unusual electrical properties of circuits and systems based on memristor can mimic the functionalities of the human brain, and can provide an in-depth understanding of key design implications of memristor-based memories, such as learning and anticipating. As a result, neuromorphic computing based on memristor is expected to bring significant breakthrough in dynamic neuromorphic memories, memristor-based resistive RAM, non-volatile memory technology, and so on.

In 1971, Dr. Leon Chua, known as the father of nonlinear circuits and cellular neural networks, postulated the existence of memristor, a portmanteau of memory resistor, in his seminal paper: “Memristor—The missing circuit element” published in IEEE Transactions on Circuit Theory, the predecessor of IEEE Transactions on Circuits and Systems—I: Regular Papers. In 2008, Hewlett-Packard researchers made nanomemristor devices for the first time, setting off an upsurge of memristor research. The emergence of nanomemristor devices is expected to realize nonvolatile RAM. Moreover, the integration, power consumption, and read–write speed of the RAM based on memristor are superior to those of traditional RAMs. The hardware network based on memristor synaptic devices is an important development direction of neuromorphic computing. It is a powerful technical candidate to break through the traditional von Neumann computing architecture in the post-Moore era, which will provide a feasible scheme about a technological breakthrough for surpassing Moore’s law.

The group consensus problem for fractional-order multiagent systems is investigated in this paper. With the help of double-tree-form transformations, the group consensus problem of fractional-order multiagent systems is proved to be equivalent to the asymptotical stability problem of reduced-order error systems. A class of distributed control protocols and some simple LMI sufficient conditions as well as necessary and sufficient conditions are proposed in this paper to solve the group consensus problem for fractional multiagent systems. Moreover, pinning control strategy has been taken into consideration. It is shown that the system converges more rapidly when the designed pinning protocols are adopted. In addition, the case of fractional system with switching topologies is also discussed and some corresponding sufficient conditions are obtained. Finally, some simulation results are presented to illustrate the theoretical results.

This article is devoted to analyzing the finite-time and fixed-time synchronization of coupled memristive neural networks with time delays. The synchronization is leaderless rather than leader-follower as the tracking targets are uncertain. By designing a proper controller and using the Lyapunov method, several sufficient conditions are obtained to achieve the finite-time and fixed-time synchronization of coupled memristive neural networks by introducing a class of special auxiliary matrices. Moreover, the settling times can be estimated for finite-time synchronization that depends on the initial values as well as fixed-time synchronization that is uniformly bounded for any initial values. Finally, two examples are presented to substantiate the effectiveness of the theoretical results.

The distributed stabilization problem is studied in this article for a group of heterogeneous second-order agents in the strong-weak competition network containing three kinds of relationships among agents: 1) cooperation; 2) strong competition; and 3) weak competition. The entire network satisfies the structural balance condition which can be partitioned into two subnetworks, while the strong and weak competitions are alternate actions on the agents from different subnetworks. To stabilize such heterogeneous networked systems in a distributed way, the switched system approach is developed and utilized in this article, where it is revealed that distributed stabilization can be achieved provided that the ratio on the activating periods of strong and weak competition is chosen appropriately. As an extension, a periodical switching law is taken into account to simplify the design process, where the periodical competition function is introduced correspondingly and several effective sufficient conditions are attained. Finally, the derived analytical results are demonstrated by performing numerical simulations.

Under practical environments, some certain noises may arise from the communication of agents due to electromagnetic interference, sensor error, and other disturbances, which are usually modeled by additive noise in the relative position state between an agent and its neighbors. In this article, a formation control strategy is considered for this kind of relative position state with the additive noise, where the unmeasured velocity information is estimated by an observer-based strategy. On the other hand, the considered problem is transformed into a convergence problem of infinite products of a sequence of general stochastic matrices. The general stochastic matrix means that a matrix has row sum one but its entries do not necessarily require non-negative. This article develops the convex analysis to cope with such infinite products. Then, a sufficient condition is obtained to ensure that the specified formation shape in a noisy environment can be achieved. Subsequently, an example is presented to show the effectiveness of the result.

This paper studies the distributed state estimator design method for periodic systems with time-varying delays and periodic communication scheduling strategy over sensor networks. To mitigate the stability attenuation and the performance loss caused by delays, a periodic delay-dependent Lyapunov function is constructed, which relaxes the conservatism and improves the ${H_\infty }$ performance of the estimators. Then, a periodic scheduling strategy, which updates the correlative innovations of each node during a period in an average way by solving an integer programming problem, is proposed to save energy. Periodic gains of distributed estimators are designed by using analog tool. Finally, a visual example is utilized to describe the periodic communication scheduling. The effectiveness of the strategy and the novel Lyapunov function is demonstrated by the example.

This article investigates the problem of distributed online optimization for a group of units communicating on time-varying unbalanced directed networks. The main target of the set of units is to cooperatively minimize the sum of all locally known convex cost functions (global cost function) while pursuing the privacy of their local cost functions being well masked. To address such optimization problems in a collaborative and distributed fashion, a differentially private-distributed stochastic subgradient-push algorithm, called DP-DSSP, is proposed, which ensures that units interact with in-neighbors and collectively optimize the global cost function. Unlike most of the existing distributed algorithms which do not consider privacy issues, DP-DSSP via differential privacy strategy successfully masks the privacy of participating units, which is more practical in applications involving sensitive messages, such as military affairs or medical treatment. An important feature of DP-DSSP is tackling distributed online optimization problems under the circumstance of time-varying unbalanced directed networks. Theoretical analysis indicates that DP-DSSP can effectively mask differential privacy as well as can achieve sublinear regrets. A compromise between the privacy levels and the accuracy of DP-DSSP is also revealed. Furthermore, DP-DSSP is capable of handling arbitrarily large but uniformly bounded delays in the communication links. Finally, simulation experiments confirm the practicability of DP-DSSP and the findings in this article.

In this paper, the periodic event-triggered suboptimal control (PETSOC) method is developed for continuous-time linear systems. Different from event-triggered control, where the triggering condition is monitored continuously, the developed PETSOC method only verifies the triggering condition periodically at sampling instants, which further reduces computational resources. First, the control gain of the PETSOC is designed based on the algebraic Riccati equation. Subsequently, the periodic event-triggering condition is proposed for the suboptimal control method, which is only verified at sampling instants periodically. The sampling period is determined and analyzed based on the continuous form of the triggering condition. Moreover, the stability and the performance upper bound of the closed-loop system with the PETSOC are proved. Finally, the effectiveness of the developed PETSOC is validated through simulation on an unstable batch reactor.

We aim to solve the consensus problem of linear multiagent systems (MASs) with input saturation under directed interaction graphs in this article, where only local output information of neighbors is available for each agent. By introducing the multilevel saturation feedback control approach, a fully distributed adaptive anti-windup protocol is proposed, where a local observer, a distributed observer, as well as an anti-windup observer are separately constructed for each agent to estimate consensus error, achieve consensus for a certain internal state, and provide anti-windup compensator, respectively. A dual protocol is further presented with the distributed observer designed based on the input matrix, which gives a thorough view on the connection between the distributed observer and the anti-windup observer, and provides the opportunity to reduce the order of the controller by designing the integrated distributed anti-windup observer. Then, three types of distributed anti-windup protocols are proposed based on the integrated distributed anti-windup observer, which requires different assumptions. Specifically, the first protocol needs two-hop relay information to generate the local observer to estimate consensus error; the second protocol designs the local observer with absolute output information to estimate the state instead; while the last protocol introduces certain assumption on transmission zero of agents' dynamics to design the unknown input observer to estimate consensus error. All of the protocols are validated by strictly theoretical proof, and are illustrated by performing simulation examples.

This article investigates the global exponential stabilization (GES) of inertial memristive neural networks with discrete and distributed time-varying delays (DIMNNs). By introducing the inertial term into memristive neural networks (MNNs), DIMNNs are formulated as the second-order differential equations with discontinuous right-hand sides. Via a variable transformation, the initial DIMNNs are rewritten as the first-order differential equations. By exploiting the theories of differential inclusion, inequality techniques, and the comparison strategy, the p th moment GES ( p ≥ 1 ) of the addressed DIMNNs is presented in terms of algebraic inequalities within the sense of Filippov, which enriches and extends some published results. In addition, the global exponential stability of MNNs is also performed in the form of an M-matrix, which contains some existing ones as special cases. Finally, two simulations are carried out to validate the correctness of the theories, and an application is developed in pseudorandom number generation.

In building-microgrid communities, renewable generation and time-varying load usually cause power fluctuations, which influence the ancillary support to the main grid. Thermostatically controlled loads (TCLs) can be utilized to compensate such power variations due to their aggregated and controllable power consumptions. Meanwhile, one basic requirement for the users' side of TCLs is to realize the fair sharing of power states and comfort states. This article proposes a distributed event-based control strategy, where information of neighboring TCLs is exchanged only when a dynamic event-triggered condition is satisfied, and thus it intelligently determines the necessary transmission frequency to save communication resources. From a cybersecurity perspective, the communication network of TCLs may be subject to hybrid attacks, for example, denial-of-service (DoS) and false data-injection (FDI) attacks. During DoS attack intervals, no information can be communicated even through the event-triggered condition is satisfied. Furthermore, the control inputs may also be tampered by FDI attacks. By utilizing the Lyapunov stability and hybrid control theories, sufficient conditions regarding the attack parameters are derived such that fair sharing of power states and comfort states of all involved TCLs can be achieved exponentially. The exclusion of Zeno behaviors is proved and a corollary for ideal communication situations is also deduced. Finally, simulation examples with various attack parameters are conducted to verify the effectiveness of the main results.

This article investigates the synchronization problem of multiple memristive neural networks (MMNNs) in the case of switching communication topologies and parameter mismatch. First, the distributed event-triggered control under continuous sampling conditions is studied. Then, a periodic event-triggered control (PETC) model is proposed to substantially reduce control consumption. Using the Lyapunov method, the properties of M -matrix, and some inequalities, the sufficient criteria of synchronous control are derived. The results can be used in the analysis of other multiagent nonlinear systems. A norm-based threshold function is given to determine the update time of the controller, and it is proved that the trigger condition excludes the Zeno behavior. Subject to parameter mismatch, a quasisynchronous control strategy is proposed, which can be extended to complete synchronization provided that the system mismatch or disturbance disappears. It is worth mentioning that this article introduces the signal function into the controller, so that the theoretical error can be limited to an arbitrarily small range. Furthermore, this new controller is used in the PETC strategy which automatically avoids the Zeno behavior. Finally, one example is given to illustrate our results.

In this paper, the output synchronization problem for complex dynamical networks (CDNs) with multiple output or output derivative couplings is discussed in detail. Under the help of Lyapunov functional and inequality techniques, an output synchronization criterion is presented for CDNs with multiple output couplings (CDNMOCs). To ensure the output synchronization of CDNMOCs, an adaptive control scheme is also devised. Similarly, we also take into account the adaptive output synchronization and output synchronization of CDNs with multiple output derivative couplings. At last, several numerical examples are designed to testify the effectiveness of the proposed results.

In this article, two kinds of complex dynamical networks (CDNs) with state and derivative coupling are investigated, respectively. First, some important concepts about finite-time passivity (FTP), finite-time output strict passivity, and finite-time input strict passivity are introduced. By making use of state-feedback controllers and adaptive state-feedback controllers, several sufficient conditions are given to guarantee the FTP of these two network models. On the other hand, based on the obtained FTP results, some finite-time synchronization criteria for the CDNs with state and derivative coupling are gained. Finally, two simulation examples are proposed to verify the availability of the derived results.

This article investigates the finite-time output synchronization and$H_{\infty }$output synchronization problems for coupled neural networks with multiple output couplings (CNNMOC), respectively. By choosing appropriate state feedback controllers, several finite-time output synchronization and$H_{\infty }$output synchronization criteria are proposed for the CNNMOC. Moreover, a coupling-weight adjustment scheme is also developed to guarantee the finite-time output synchronization and$H_{\infty }$output synchronization of CNNMOC. Finally, two numerical examples are given to verify the effectiveness of the presented criteria.

Contrary to many existing works based on the continuous-time inertial neural network, this article considers the quasisynchronization issue for the discrete-time inertial neural network. To obtain the main results, we adopt the generalized matrix-measure concept. A condition ensuring the quasisynchronization is attained at first. To make the result less conservative, further analysis based on the generalized matrix measure is proceeded. An example is given to demonstrate the validity and effectiveness of the main results.

This article aims to analyze the fixed-time synchronization of a class of discontinuous fuzzy inertial neural networks with time-varying delays based on the new improved fixed-time stability lemmas. First of all, by using the generalized variable transformation and Filippov solution theory, the discontinuities of the considered neural system can be coped with, and the error system is established. By relaxing the conditions of the$C$-regular Lyapunov function, two new fixed-time stability lemmas are proved via simple inequality techniques. The setting times are also estimated and are more accurate in comparison with the previous ones. Finally, one typical numerical example is carried out to verify the correctness and the advantages of the main results.

This article is devoted to solving the event-triggered stabilization problem of a new type of Takagi–Sugeno fuzzy complex-valued memristive neural networks with mixed time-varying delays. By introducing an event-triggered scheme with static and dynamic event-triggered conditions, the fuzzy event-triggered controller is designed. After combining the inequality technique with the Lyapunov function approach, some easily verified sufficient conditions are established to ensure stabilization of the Takagi–Sugeno fuzzy complex-valued memristive neural networks with mixed time-varying delays under the proposed event-triggered scheme. In addition, since the inter-event time with the proposed event-triggered scheme is deduced to exist a nonzero positive lower bound, Zeno behavior is not going to happen. Finally, the effectiveness of results is verified by a numerical example.

This article studies the distributed observer-based event-triggered bipartite tracking control problem for stochastic nonlinear multiagent systems with input saturation. First, different from conventional observers, we construct a novel distributed reduced-order observer to estimate unknown states for the stochastic nonlinear systems. Then, an event-triggered mechanism with relative threshold is introduced to reduce the burden of communication. In addition, the bipartite tracking controller is proposed for stochastic multiagent systems by using fuzzy logic systems and the backstepping approach. Meanwhile, it is proved that the designed method can guarantee that all the signals in the closed-loop systems are bounded in probability, and the distributed consensus tracking errors can converge to a small neighborhood of the origin via the Lyapunov stability theory. Finally, a simulation example is given to prove the effectiveness of the designed scheme.

Under spatially local averaged measurements (SLAMs), this article introduces a sampled-data fuzzy control (SDFC) for nonlinear delayed distributed parameter systems (DDPSs). First, we use a Takagi–Sugeno (T–S) fuzzy parabolic partial differential-difference equation (PDDE) to accurately describe the nonlinear DDPS. Then, on basis of the T–S fuzzy PDDE model, an SDFC design under SLAMs via space-dependent linear matrix inequalities (SDLMIs) is subsequently developed to ensure the exponential stability of the closed-loop nonlinear DDPSs by using inequality techniques and Lyapunov functional, where slow-varying and fast-varying delays are respected. Furthermore, to solve SDLMIs, the SDFC design problem for nonlinear DDPS under SLAMs is formulated as a linear matrix inequality feasibility problem. Finally, numerical simulations of two examples are presented to support the given SDFC strategy.

Industrial processes often operate in the complex dynamic environment. They challenge the cost-effective and reliable operation of industrial processes. This article proposes a multiobjective optimization and control strategy for the iron removal process to be optimally operated in the dynamic environment. In the optimization layer, multiobjective optimization and local optimization problems, minimizing the process cost and maximizing economic index, are developed to obtain the optimal set-points of the outlet ferrous ion concentrations. In the control layer, model predictive control based on the system dynamic model is constructed to control the outlet ferrous ion concentrations to track the set-points. To reduce the influences from dynamic disturbances and improve the tracking performance, we use least squares support vector machine (LSSVM) to establish the correction model. Fuzzy-logic-based compensation is proposed to compensate the set-points according to the priori and posteriori information. Finally, simulations using real-world plant data are carried out to verify the effectiveness of the proposed control strategy. The simulation results demonstrate that the proposed control strategy achieves a satisfactory tracking performance with less process consumption. It improves the iron content in the goethite precipitate, which has more commercial value. The proposed control strategy can make the plant more profitable.

Realistic industrial process is usually a dynamic process with uncertainty. Chance constraints are applicable to industrial process modeling under uncertain conditions, where constraints cannot be strictly met, or need not be fully met. Therefore, chance constrained dynamic optimization (CCDO) formulation is available to address realistic industrial process issues. Because of the dynamic and uncertainty, chance constrained dynamic optimization problems (CCDOPs) arising from practical industries are hard to cope with. In this article, a novel CCDO method is proposed to resolve this issue, where an adaptive sample average approximation method, a control vector parameterization method, and a state constraint handling strategy are integrated. Specially, a hybrid intelligent optimization algorithm is introduced to realize a global and efficient optimization performance. The proposed method is applied to CCDOPs modified by dynamic optimization standard test functions and industrial experiments to demonstrate its effectiveness. The experimental results show that the proposed method has good performance in solving CCDOPs.

In this article, we investigate a class of memristive neural networks (MNNs) with time-varying delays and leakage delays via sliding mode control (SMC) with and without control disturbance. SMC is used to ensure MNNs' stability. According to the characteristics of the MNNs, we consider the following three models: the first is the MNNs with time-varying delays, the second is the MNNs with time-varying delays and the control disturbance, and the third is the MNNs with time-varying delays, leakage delays, and the control disturbance. We quote some assumptions and lemmas to ensure that our main results are true. The sliding surface, the corresponding sliding mode controller, and the Lyapunov functions are constructed in different models to ensure MNNs' stability. Finally, some examples and simulations verify the validity of our main results by solving linear matrix inequality (LMI), and the conclusions and analysis of the results are given.

This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader's output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.

This article focuses on the observer-based quasi-synchronization problem of delayed dynamical networks with parameter mismatch under impulsive effect. First, since the state of each node is unknown in the real situation, the state estimation strategy is proposed to estimate the state of each node, so as to design an appropriate synchronization controller. Then, the corresponding controller is constructed to synchronize the slave nodes with their leader node. In this article, we take the impulsive effect into consideration, which means that an impulsive signal will be applied to the system every so often. Due to the existence of parameter mismatch and time-varying delay, by constructing an appropriate Lyapunouv function, we will eventually obtain a differential equation with constant and time-varying delay terms. Then, we analyze its trajectory by introducing the Cauchy matrix and prove its boundedness by contradiction. Finally, a numerical simulation is presented to illustrate the validness of obtained results.

This article is concerned with the issue of l2- l∞state estimation for nonlinear coupled networks, where the variation of coupling mode is governed by a set of switching signals satisfying a persistent dwell-time property. To solve the problem of data collisions in a constrained communication network, the round-robin protocol, as an important scheduling strategy for orchestrating the transmission order of sensor nodes, is introduced. Redundant channels with signal quantization are used to improve the reliability of data transmission. The main purpose is to determine an estimator that can guarantee the exponential stability in mean square sense and an l2- l∞performance level of the estimation error system. Based on the Lyapunov method, sufficient conditions for the addressed problem are established. The desired estimator gains can be obtained by addressing a convex optimization case. The correctness and availability of the developed approach are finally explained via two illustrative examples.

This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays (DMNNs). By means of inequality techniques, theories of the M-matrix, and the comparison strategy, the Lagrange exponential stability of the underlying DMNNs is considered in the sense of Filippov, and the globally exponentially attractive set is estimated through employing the M-matrix and external input. Especially, when the external input is not concerned, the Lyapunov exponential stability of the corresponding DMNNs is developed immediately in the form of an M-matrix, which contains some published outcomes as special cases. Furthermore, by constructing an M-matrix-based differential system, the Lyapunov exponential stability of the DMNNs is studied, which is less conservative than some existing ones. Finally, three simulation examples are carried out to examine the validness of the theories.

Decentralized dual methods play significant roles in large-scale optimization, which effectively resolve many constrained optimization problems in machine learning and power systems. In this article, we focus on studying a class of totally non-smooth constrained composite optimization problems over multi-agent systems, where the mutual goal of agents in the system is to optimize a sum of two separable non-smooth functions consisting of a strongly-convex function and another convex (not necessarily strongly-convex) function. Agents in the system conduct parallel local computation and communication in the overall process without leaking their private information. In order to resolve the totally non-smooth constrained composite optimization problem in a fully decentralized manner, we devise a synchronous decentralized dual proximal (SynDe-DuPro) gradient algorithm and its asynchronous version (AsynDe-DuPro) based on the randomized block-coordinate method. Both SynDe-DuPro and AsynDe-DuPro algorithms are theoretically proved to achieve the globally optimal solution to the totally non-smooth constrained composite optimization problem relied on the quasi-Fejér monotone theorem. As a main result, AsynDe-DuPro algorithm attains the globally optimal solution without requiring all agents to be activated at each iteration and thus is more robust than most existing synchronous algorithms. The practicability of the proposed algorithms and correctness of the theoretical findings are demonstrated by the experiments on a constrained Decentralized Sparse Logistic Regression (DSLR) problem in machine learning and a Decentralized Energy Resources Coordination (DERC) problem in power systems.

In this article, the problem of the distributed convex optimization is investigated, where the target is to collectively minimize a sum of local convex functions over an unbalanced directed multiagent network. Each agent in the network possesses only its private local objective function, and the sum of all local objective functions constitutes the global objective function. We particularly consider the scenario, where the underlying interaction network is strongly connected and the relevant weight matrix is row stochastic. To collectively figure out the optimization problem, a distributed accelerated convergence algorithm where agents utilize uncoordinated step-sizes is presented by incorporating consensus of multiagent networks into distributed inexact gradient tracking technique. Most of the existing methods require all agents to possess the out-degree information of their in-neighbors, which is impractical and hardly inevitable as interpreted in this article. By utilizing the small-gain theorem, we prove that if the maximum step-size is positive and sufficiently small (constrained by a specific upper bound), the proposed algorithm, termed as SGT-FROST, converges geometrically to the optimal solution given that the objective functions are smooth and strongly convex. A certain convergence rate is also shown. Simulations confirm the findings in this article.

This article is concerned with the problem of the containment control with respect to continuous-time semi-Markovian multiagent systems with semi-Markovian switching topologies. Two kinds of classical control schemes, which are dynamic containment control and static containment control schemes, are utilized to address the proposed synthesis problem. Based on the linear matrix inequality (LMI) method, the dynamic containment controller and static containment controller are designed to plunge into the studied semi-Markovian multiagent systems, respectively. Moreover, the random switching topologies with the semi-Markovian process, the partly unknown transition rates, and the generally uncertain transition rates are taken into account, which can be applicable to more practical situations. Finally, the simulation results are provided to illustrate the effectiveness of the proposed theoretical results.

In this article, we concentrate on dealing with the distributed optimization problem over a directed network, where each unit possesses its own convex cost function and the principal target is to minimize a global cost function (formulated by the average of all local cost functions) while obeying the network connectivity structure. Most of the existing methods, such as push-sum strategy, have eliminated the unbalancedness induced by the directed network via utilizing column-stochastic weights, which may be infeasible if the distributed implementation requires each unit to gain access to (at least) its out-degree information. In contrast, to be suitable for the directed networks with row-stochastic weights, we propose a new directed distributed Nesterov-like gradient tracking algorithm, named as D-DNGT, that incorporates the gradient tracking into the distributed Nesterov method with momentum terms and employs nonuniform step-sizes. D-DNGT extends a number of outstanding consensus algorithms over strongly connected directed networks. The implementation of D-DNGT is straightforward if each unit locally chooses a suitable step-size and privately regulates the weights on information that acquires from in-neighbors. If the largest step-size and the maximum momentum coefficient are positive and small sufficiently, we can prove that D-DNGT converges linearly to the optimal solution provided that the cost functions are smooth and strongly convex. We provide numerical experiments to confirm the findings in this article and contrast D-DNGT with recently proposed distributed optimization approaches.

This article deals with the issue of dissipative synchronization for Markov jump neural networks subject to time-varying interval delays by using the sampled-data control method. First, a new two-sided looped-functional is introduced to improve the informativeness of the formed Lyapunov-Krasovskii functional, which takes into an account the information of the entire sampling period. Compared with some traditional methods, the information between x(t) and x(tk+1) is also emphasized. On this basis, an improved inequality technique is considered valid to acquire the less conservative synchronization criterion. In the end, two numerical examples are displayed, and a comparative example powerfully demonstrates the availability and the superiority of the developed technique.

Distributed consensus tracking problem of networked agent systems with directed topologies under denial-of-service (DoS) attacks is investigated. The considered networked agent network consists of a physical layer with fixed physical links and a cyber layer with cyber control units. Both the communication network connecting these two layers and the cyber communication network within the cyber layer may subject to malicious DoS attacks. These two types of attacks have different impacts on the networked system; the former one affects the timely update of control inputs, and the latter influences the connection weights of the cyber communication graph. First, for DoS signals occurring in the communication network which transmits the state and control input information between the two layers, the distributed control protocol based on the event-triggered scheme and locally deployed estimators are designed. Efficient algorithms for selecting event-triggered control parameters and Zeno-free triggered parameters are given to ensure the mean-square consensus. The relationships between the system's parameters and the features of DoS attacks are successfully revealed. Second, corresponding theoretical analysis is derived for consensus tracking of the networked systems when DoS attacks are launched within the cyber layer. Conditions concerning the length of repairing time and indexes of DoS attacks are given by utilizing hybrid control theory. At last, the effectiveness of the obtained results is demonstrated by performing simulations on multirobot systems.

This article considers the rendezvous problem of a group of heterogeneous second-order agents subject to information transmission delays. A major challenge to construct a fully distributed rendezvous protocol is to deal with these delays, which are assumed to be nonuniform and time-varying. Inspired by the fact that a large enough damping coefficient can improve the robustness of a second-order agent against external disturbance, we overcome the above challenge by studying the impact of the damping coefficient on the rendezvous problem. To theoretically analyze the impact, one type of static rendezvous protocol is first proposed and employed. It is interesting to find that agents can reach rendezvous under the static protocol if the damping coefficient is large enough, even though the duration of the time-delay is uncertain. This fact indicates that a large damping coefficient can make the static protocol be robust to the uncertainty of time-delay, which is consistent with the common sense. Then, we apply this impact to deal with the uncertainty introduced by the nonuniform time-varying information delays. To fully apply this impact, we adopt an adaptive approach to making the damping coefficient automatically adapt with the changes of the time-delay. Hence, two classes of fully distributed and adaptive rendezvous protocols are designed, which do not need the global information of the entire communication graph or the value of these time-delays at all. The difference between two classes of protocols is whether it can realize low-frequency learning or not. Finally, some numerical simulations are performed on multiple robots to illustrate the analytical results.

This paper investigates the event-triggered consensus of linear multiagent systems with periodic data sampling mechanisms, where random packet losses are taken into account. The random packet losses occur in communication links based on a certain probability, and it is subject to the Bernoulli distribution. A novel distributed control protocol is designed based on the combined measurement to achieve the mean square consensus. By using the Riccati inequalities and linear matrix inequalities, an event-triggered condition with fewer parameters is also designed to reduce the information updating number. The interaction among the control gain matrix, sampling interval, and packet losses probability is used to describe the consensus conditions. The maximum sampling interval is presented explicitly. It is shown that the advantages of the proposed event-triggered strategy with the data sampling mechanism can avoid the Zeno behavior of the systems and continuous monitoring of the states. The simulations are provided to verify the proposed control strategy.

This article presents a simple and intuitive solution for multilabel image classification, which achieves the competitive performance on the popular COCO and PASCAL VOC benchmarks. The main idea is to capture how humans perform this task: we recognize both labels (i.e., objects and attributes) and the correlation of labels at the same time. Here, label recognition is performed by a standard ConvNet pipeline, whereas label correlation modeling is done by projecting both labels and image features extracted by the ConvNet to a common latent vector space. Specifically, we carefully design the loss function to ensure that: 1) labels and features that co-appear frequently are close to each other in the latent space and 2) conversely, labels/features that do not appear together are far apart. This information is then combined with the original ConvNet outputs to form the final prediction. The whole model is trained end-to-end, with no additional supervised information other than the image-level supervised information. Experiments show that the proposed method consistently outperforms previous approaches on COCO and PASCAL VOC in terms of mAP, macro/micro precision, recall, and$F$-measure. Further, our model is highly efficient at test time, with only a small number of additional weights compared to the base model for direct label recognition.

This paper presents a complete solution for the hardware design of a memristor-based long short-term memory (MLSTM) network. Throughout the design process, we fully consider the external and internal structures of the long short-term memory (LSTM), both of which are efficiently implemented by memristor crossbars. In the specific design of the internal structure, the parameter sharing mechanism is used between the LSTM cells to minimize the hardware design scale. In particular, we designed a circuit that requires only one memristor crossbar for each unit in the LSTM cell. The activation function, including sigmoid and tanh (hyperbolic tangent function), involved in each unit is approximated by a piecewise function, which is designed with the corresponding hardware. To verify the effectiveness of the system we designed, we test it on IMDB and SemEval datasets. Considering the huge impact of the dimensions of the input data on the scale of the hardware design, we use word2vector instead of one-hot encoding for the input data encoding. With the parameter sharing mechanism, the transformed vectors are input in different periods, so only 65 memristive crossbars are needed in the entire system to complete the sentiment analysis of the input text. The experimental results verify the effectiveness of our proposed MLSTM system.

This paper investigates the problem of quasi-synchronization (QS) for the periodic neural networks (NNs). In order to address more general NNs, the parameter and the period mismatches are both considered, that is, excluding parameters, periods of the target dynamic and the followers are also different. In addition, the constrainted target information is studied, where the logarithmic quantizer is used to overcome the limited communication capacity and Bernoulli processes are employed to model cases of the information loss. A new period is established based on the lowest common multiple period of the target dynamic and the followers to obtain an augmented synchronization error system (ASES). Further, a suboptimal iterative algorithm is proposed to cut down the QS range of the ASES and the corresponding controllers are designed. At last, the controller design method is illustrated by a numerical example.

There are few results concerning consensus of multiagent systems (MASs) based on partial differential equations (PDEs), and the problem of how to act boundary control based on distributed measurement on spatial boundary points of MASs has not been solved. This paper addresses boundary control based on distributed measurement for output consensus of leader-following directed MASs modeled by parabolic PDEs. First, a boundary controller acting on spatial boundary points is designed by considering the delivered information produced by agents communicating with neighborhoods. Without considering input constraint, the Lyapunov's direct method is used to obtain a sufficient condition on the existence of the boundary controller to achieve output consensus. The condition is expressed as a form of the feasibility of LMIs. After that, the whole input constraint for MASs is given. And then, one more condition on control gains is obtained to ensure the existence of the boundary controller with input constraint. Finally, one numerical example with two cases illustrates the theoretical analysis results.