This article focuses on the design and analysis of resilient leaderless consensus for multiagent systems exposed to distributed denial-of-service (DoS) attacks. Unlike most existing works that focus on undirected communication topologies and synchronized attacks affecting all links simultaneously, this work considers a more general scenario with directed communication graphs and distributed DoS attacks, where different communication channels can be disrupted independently. To address this challenge, a dynamic event-triggered communication scheme is incorporated into the consensus protocol, under which asymptotic consensus stability can be preserved despite distributed DoS disruptions. Information exchange is performed only at triggering instants, which effectively suppresses redundant transmissions and enhances communication efficiency. To further reduce computational burden and remove the requirement of continuous monitoring, a self-triggered communication scheme is introduced, in which each agent determines its next triggering instant solely based on the most recently available data. Rigorous analysis verifies that both the event-triggered and self-triggered schemes preclude Zeno behavior. In addition, numerical simulations of a low Earth orbit satellite formation demonstrate the efficacy and advantages of the developed control strategies.

In this article, we consider the problem of equilibrium selection in multiplayer finite potential games with large-size action sets. Traditional learning approaches often require players to traverse the entire action set to evaluate the utility of each action, which can be computationally intensive and inefficient. To overcome this limitation, we leverage the idea of neighborhood search into the game-theoretical learning process for the first time by generating neighborhood candidate action sets for exploration and evaluation. As such, we propose a lightweight log-linear dynamics for efficient equilibrium selection in finite potential games. Asymptotic convergence is proved under both asynchronous and independent revision rules with the help of resistance tree theory. Furthermore, through the multisatellite cooperative task allocation (MSCTA) problem, we elaborate on how to encode the players’ actions and how to generate the neighborhood structure. Simulation results demonstrate that the proposed method significantly outperforms the existing game-theoretic learning methods, notably in terms of solution time.

This article studies distributed resilient consensus for nonlinear multiagent systems (MASs) under sensor and actuator deception attacks. The agents are modeled as high-order strict-feedback dynamics with unknown parameters and external disturbances. Sensor attacks introduce time-varying uncertainties into system dynamics, while actuator attacks may change control magnitudes or even reverse the actual control direction, thereby turning a negative-feedback loop into a destabilizing positive-feedback one. Different from existing results, we consider a more realistic and challenging scenario, where control directions experience an infinite number of switches. To address these challenges, we develop a novel backstepping-based resilient controller. Time-varying uncertainties caused by sensor deception are counteracted by nonlinear damping terms. For actuator malicious sign reversals, each agent generates a direction signal and uses performance-triggered switching to identify the real-time control direction, ensuring the closed-loop system frequently recovers a negative-feedback structure. A two-phase control structure with fixed-time control and barrier Lyapunov functions ensures consensus accuracy. We prove that if the minimum switching frequency of direction signals exceeds twice the maximum switching frequency of actuator attacks, all closed-loop signals are globally bounded and consensus errors converge to an arbitrarily small residual set within a fixed time. Simulations verify the effectiveness.

Learning to optimize (L2O) is a paradigm designed to train a learnable optimizer that can quickly infer optimal solutions with less computation. While supervised learning (SL) and reinforcement learning (RL) are prevalent, SL needs to obtain optimal solutions of training instances beforehand and RL heavily relies on the meticulous design of rewards. More importantly, both paradigms struggle with good generalization across new problem instances. Therefore, this paper pioneers a novel learning framework, named evolutionary multitasking-based unsupervised learning (EMTUL), eliminating dependency on optimal labels and complicated rewards, and maintaining diversified parameter vectors to improve generalization. Taking the traveling salesman problem (TSP) as a case study, a lightweight learnable optimizer named tour generator (TrGen) is devised. During training, each instance is treated as a task, and an EMT algorithm is employed to train the TrGen on multiple tasks simultaneously. Upon termination of the algorithm, an epoch ends, and a set of candidate parameter vectors is preserved for the subsequent epoch based on distribution diversity and generalization performance. This approach gradually directs the training process towards diversified search regions beneficial for generalization. Following training, the set of candidate parameter vectors serves as a knowledge reserve. When encountering new instances, the learnable optimizer can either directly infer optimal solutions via the knowledge reserve or fine-tune its parameters to accommodate the specifics of each new instance. The main benefits of the EMTUL are: 1) it directly uses the objective function as the loss function, dispensing the necessity of optimal solutions and differentiable loss or reward function; 2) it maintains a set of elite parameter vectors to efficiently handle new instances. Finally, experimental results demonstrate that TrGen, with few model parameters and trained under EMTUL, effectively identifies global or local optima for new instances of varying scales, even with relatively few training instances.

Users upload their own WiFi localization request signals to match against the received signal strengths (RSSs) in an offline constructed fingerprint database, thereby obtaining their own geographic location estimates. However, the relationships between the measured signals of users are seldom explored for enhancing the localization accuracy. Furthermore, the tedious site surveys required for building an offline fingerprint database hinder the wider application of this technology. To address the above issues, this article proposes a collaboratively enhanced lightweight WiFi localization algorithm named CBWF+, capable of providing an accurate indoor position with low-overhead fingerprints and collaborative localization. Specifically, we first discretize the area of interest to produce virtual points (VPs), and leverage the concept of circular-boundary-based localization to select a few VPs that share similar signal characteristics with the request signals. Then, important users for collaborative localization are chosen by two new proposed indicators. Next, based on the selection results and the RSS relationships among users, the spatiotemporal characteristics of WiFi signals are used to further filter out VPs that contradict the relationship between RSS and physical location. Finally, the credible VPs are used to obtain a high-accuracy estimate, by a proposed weighting method. The results of the real-world experiments validate the effectiveness of our proposed CBWF+ algorithm, compared to other state-of-the-art approaches (e.g., NN, OCLoc, CBWF, etc.). Specifically, in a 40-m$\times 17$-m real scenario with only 20 reference points (RPs) and 11 access point (APs), our algorithm achieves an average localization accuracy of 2.49 m. Our codes are available at:https://github.com/dadadaray/CBWF2.0.

In this article, we address the shape formation problem for massive robot swarms in environments where external localization systems are unavailable. Achieving this task effectively with solely onboard measurements is still scarcely explored and faces some practical challenges. To solve this challenging problem, we propose the following novel results. Firstly, to estimate the relative positions among neighboring robots, a concurrent-learning based estimator is proposed. It relaxes the persistent excitation condition required in the classical ones such as the least-square estimator. Secondly, we introduce a finite-time agreement protocol to determine the shape location. This is achieved by estimating the relative position between each robot and a randomly assigned seed robot. The initial position of the seed one marks the shape location. Thirdly, based on the theoretical results of the relative localization, a novel behavior-based control strategy is devised. This strategy not only enables the adaptive shape formation of large groups of robots but also enhances the observability of inter-robot relative localization. Numerical simulation results are provided to verify the performance of our proposed strategy compared to the state-of-the-art ones. Additionally, outdoor experiments on real robots further demonstrate the practical effectiveness and robustness of our methods. Note to Practitioners—Shape formation has a broad potential for large groups of robots to execute certain tasks, such as object transport, forest firefighting, and entertainment shows. However, most of the existing approaches rely on external localization infrastructures, rendering them impractical in environments where such systems are not available. To address this issue, this article proposes an integrated strategy that can achieve shape formation for large groups of robots by using local distance and displacement measurements. This strategy consists of three main components. Firstly, a relative localization estimator is introduced to estimate the relative positions among neighboring robots. Secondly, a protocol for reaching a consensus on the desired shape’s position is proposed. Thirdly, a behavior-based controller is developed to achieve massive shape formation and enhance the observability of relative localization. More details of the proposed algorithms and swarm robotic systems are provided in this article.

In this article, robustH∞synchronization problem for a class of networked re-entrant manufacturing systems (RMSs) is investigated by utilizing state feedback control and distributed adaptive state feedback control approaches. Different from the isolated single re-entrant manufacturing line, a networked RMS with three-tiered architecture is presented, which contains the production line, the production workshop and the workshop network. Based on the mass conservation law, the dynamics of the production line and the production workshop are established by a first-order linear hyperbolic PDE and a first-order semi-linear hyperbolic PDE, respectively. On one hand, in view of communication delays and external disturbances that might exist in the system, a delayed state feedback controller is constructed to address robustH∞synchronization of the networked RMSs. On the other hand, considering the uncertainty with coupling gain and the unavailability of global information, a distributed cooperative controller with the edge-dependent adaptive gain is further developed to ensure robustH∞synchronization of the networked RMSs. Numerical simulations validate the effectiveness of both proposed control schemes.

This paper focuses on adaptive prescribed performance control for nonlinear systems with parametric uncertainties. The proposed control scheme incorporates a certainty equivalence controller, a batch least-squares identifier (BaLSI) and a performance triggered condition. The off-line BaLSI, which utilizes all the previously appeared excitation information for parameter updating, is activated as intervals by the performance triggered condition. The effects of the parametric uncertainties are eliminate in finite times of updating, and the closed-loop system can achieve the prescribed performance without suffering from stiff differential equation problem. The simulation results are provided to demonstrate the effectiveness of the proposed control scheme.

Unsupervised domain adaptive person reidentification (UDA re-ID) aims to transfer knowledge learned from from a source domain to a target domain. Prevalent clustering-based self-training methods suffer from label noise, which impedes improvements of model performance. To overcome this issue, mutual networks are introduced to generate reliable soft pseudo-labels for supervising the model training, allowing the suppression of label noise through the complementarity of two networks in the framework. However, existing methods based on this framework face two issues. One issue is that the complementarity between the two networks diminishes as training progresses, reducing the effectiveness of noise suppression. The other issue is that the reliability of pseudo-labels from clustering is not explicitly evaluated, and directly using all pseudo-labels for network optimization may introduce numerous noisy samples. To address these issues, we propose the Data Complementary Training Framework (DCTF) based on mutual networks. This framework comprises two main strategies. Firstly, we introduce the Camera Complementary Training Method (CCTM). CCTM utilizes camera style transfer to generate images of different camera styles for training the two networks, thereby increasing their diversity and enhancing the complementarity of mutual networks. Secondly, we propose the pseudo-labels Reliability Evaluation based on camera Style Transfer (REST). REST assesses the pseudo-label reliability based on the data distribution characteristics, selecting more reliable samples for network optimization. Experimental results on four UDA re-ID tasks demonstrate the effectiveness of our approach, with an mAP gain up to 8.7% compared to previous state-of-the-art methods on the challenging MSMT17 dataset.

As a promising indoor localization technology, WiFi fingerprint-based localization encounters many issues that need to be addressed urgently, such as high-overhead fingerprint map construction, device heterogeneity among either mobile devices or access points (APs), etc. In this article, we present CBWF: a lightweight circular boundary -based WiFi fingerprinting localization system that is able to provide low-overhead, device calibration-free accurate indoor localization. CBWF achieves this by dividing a localization area into multiple subregions, and then leveraging the relation between the received signal strength (RSS) vectors from two different APs as fingerprints for localization. The key idea behind CBWF is that a superior division mechanism is attained to divide the localization area. Specifically, we propose the circle boundary mechanism to better approximate the real boundary of subregions, compared with the widely used linear boundary mechanism, and then sufficiently exploit the theoretical characteristics behind this novel mechanism. Extensive simulation and real-world experiments show that our lightweight system outperforms state-of-the-art approaches. Specifically, in a 40 m$\times 17$m real scenario with only 20 reference points (RPs) and 11 APs, CBWF achieves an average localization accuracy of 2.95 and 4.15 m for two different mobile devices, respectively. Our codes are available at:https://github.com/dadadaray/circular-boundary.

Privacy preservation for distributed optimization in multiagent systems has been widely concerned in recent years. In this article, the accumulated noise privacy-preserving alternating direction method of multipliers (ANPPM) algorithm is proposed to preserve the private information of each agent. The masked states of each agent are sent to its neighbors with a designed noise-adding mechanism, and an accumulated term is introduced to confuse the gradients at each iteration. With ANPPM, all the agents can achieve privacy preservation for the information of real states and subgradients. Moreover, the states of all the agents can be guaranteed to converge to the optimal solution. The convergence rate of is consistent with standard ADMM, hence no adverse effect is induced by the privacy-preserving mechanism. Numerical results are provided to validate the effectiveness of the proposed ANPPM algorithm.

This article investigates the leaderless output consensus control problem for a class of nonlinear multiagent systems with heterogenous system orders and unmatched unknown parameters via output-feedback control. The interaction topology among the agents is undirected and jointly connected. Due to the heterogenous system orders and switching topology among the agents, the classical distributed adaptive backstepping-based control technique cannot be applied to solve the problem considered in this article. To solve this issue, a novel distributed reference system is first proposed for each agent, by using only relative outputs of the neighboring agents. Subsequently, a fully distributed reference system-based adaptive leaderless output consensus control scheme is designed via output-feedback control. A remarkable merit of the proposed control scheme lies in that precisely known nonlinear dynamics, system states, distributed parameter estimates, and the states of virtual reference system are no longer needed to be shared with neighbors. This implies that the communication burden can be effectively alleviated, and even the communication network can be replaced by some perception sensors. Finally, two illustrative examples are provided to verify the effectiveness of the proposed control scheme.

In this article, the decentralized adaptive secure control problem for cyber-physical systems (CPSs) against deception attacks is investigated. The CPSs are formed as a type of nonlinear interconnected strict-feedback systems with uncertain time-varying parameters. The attack affects the information transmission between sensor and actuator in a multiplicative manner. A novel decentralized adaptive backstepping secure control strategy is established by exploiting a particular kind of Nussbaum functions and a flat-zone Lyapunov function analysis approach. It is shown that all of closed-loop signals remain globally bounded, and each output signal eventually converges into a small neighborhood of the origin. Simulation results on an illustrative example are provided to display the effectiveness of the proposed control scheme.

This article focuses on the distributed adaptive consensus control problem for nonlinear systems with unknown parameters and denial-of-service (DoS) attacks. The communication channels among subsystems are directed and DoS attacks are executed on subsystems to jam the communication transmission. Besides, only part of these subsystems can access states of the desired reference system. To actively alleviate attack effects on the consensus performance, a distributed adaptive consensus control scheme with an active-defense mechanism is proposed. The active-defense mechanism consists of an attack-detection algorithm and a switching strategy. The distributed adaptive consensus controller is designed with normalized damping terms in control inputs and parameter update laws. In the presence of DoS attacks, a stability condition is derived based on the designed control scheme, which guarantees that the consensus errors are globally uniformly bounded under arbitrary switching dwell-time. Experimental results are provided to validate the effectiveness of the proposed control scheme with the active-defense mechanism.

In this article, the distributed adaptive secure formation control problem for mobile robots is considered. The communication channels among robots are undirected and suffer from denial-of-service (DoS) attacks. Only part of the robots can access the reference trajectories. To mitigate attack effects on the formation performance, a switching strategy for communication channels is designed. Besides, an adaptive secure control scheme is proposed with a distributed adaptive trajectory estimator and an adaptive tracking controller for each robot. In the estimator, damping and normalizing terms are introduced to alleviate attack effects on the system performance. Moreover, these terms can also remove the constraints on the lower bounded dwell time of switching topologies. By applying the backstepping technique, an adaptive tracking control scheme is proposed for robots with uncertainties to track estimator states. According to the proposed secure control scheme, a stability condition is provided, such that the boundedness of formation errors can be guaranteed under DoS attacks and arbitrary switching dwell time. Experimental results are given to illustrate the effectiveness of the proposed secure control scheme.

This paper investigates the attack detection and reconstruction problem for a class of nonlinear systems with unknown parameters and actuator/state attacks. An adaptive sliding mode observer with online parameter estimation is designed to estimate the states and the convergence of estimation errors is guaranteed. By analyzing the features of adaptive sliding mode observers, an attack detection and reconstruction scheme is proposed. It is shown that the reconstruction signal can approximate the attack with any accuracy under a persistent excitation condition. Finally, simulation results are given to verify the effectiveness of the proposed scheme.

Privacy preservation for distributed optimization algorithms in multi-agent systems has warranted widespread concern in recent years due to the urgent security requirements. In this paper, a novel distributed subgradient privacy-preserving algorithm is proposed, where the weight matrix is row stochastic. The proposed algorithm holds an advanced noise-adding mechanism, where locally balanced noise is adopted to mask the real data that agents transmit to neighbors. It is shown that all the agents converge to an optimal solution of the optimization problem, while the subgradients and cost functions are preserved simultaneously.

This paper addresses the tracking problem of an omnidirectional mobile robot (OMR), which is a second-order electromechanical system with unmatched uncertainties and disturbances. The control inputs are the currents of the motors, and the control objective is the tracking control of the real-time position and orientation for the robot. The main features of this paper are summarized as follows. (1) Both the kinematic and the dynamic models for a standard OMR are presented, which explicitly defines the unknown physical parameters of the system as well as the specific mathematical form of disturbances. (2) A new OMR control scheme is proposed based on adaptive back-stepping techniques. It is shown that the OMR can asymptotically track the desired trajectory with any orientation. (3) In addition to numerical simulations, a real OMR experiment platform is established. The effectiveness of the proposed control scheme has been verified with both simulation and experimental results.

This article addresses the resilient practical cooperative output regulation problem (RPCORP) for multiagent systems subjected to both denial-of-service (DoS) attacks and actuator faults. Fundamentally different from the existing solutions to RPCORPs, the system parameters considered in this article are unknown to each agent, and a novel data-driven control approach is introduced to handle such an issue. The solution starts with developing resilient distributed observers for each follower in the presence of DoS attacks. Then, a resilient communication mechanism and a time-varying sampling period are introduced to, respectively, ensure the neighbor state is available as soon as attacks disappear and to avoid targeted attacks launched by intelligent attackers. Furthermore, a model-based fault-tolerant and resilient controller is designed based on the Lyapunov approach and the output regulation theory. In order to remove the reliance on system parameters, we leverage a new data-driven algorithm to learn controller parameters via the collected data. Rigorous analysis shows that the closed-loop system can resiliently achieve practical cooperative output regulation. Finally, a simulation example is given to illustrate the effectiveness of the achieved results.

In this article, an adaptive finite-time fault-tolerant control (FTC) strategy is proposed for state tracking of uncertain systems subject to actuator faults and input saturation. The actuator faults, which include multiplicative and time-varying additive modes, are allowed to be unknown. To deal with the coupling of the unknown model uncertainties and the actuator faults, an adaptive mechanism is designed based on a wisely chosen Lyapunov function. The proposed finite-time FTC scheme, with adaptive laws of uncertain parameters, guarantees the stability of the closed-loop system, and the practical finite-time state tracking property. Then, the algorithm is applied to a flight control system, simulation and comparison results demonstrate the effectiveness and advantages of the designed control scheme.

Topology identification of multilayer networks is of great significance in the fields of information, engineering, and society. Current research on topology identification of multilayer networks requires the knowledge of the specific number of layers and the corresponding nodes in each layer in advance. However, the information is often unknown in reality. Herein, for a multilayer network with unknown layers, where node dynamic functions are in the quadratic form, we can obtain the specific layers and the topology structure based on compressive sensing. Notably, the number of layers can be obtained in two ways: 1) by the number of diverse node dynamic functions or 2) inner coupling matrices. The effectiveness and robustness of our method are verified through numerical simulations.

In this article, we investigate the distributed resilient observers-based decentralized adaptive control problem for cyber-physical systems (CPSs) with time-varying reference trajectory under denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear multi-input uncertain multiagent systems, which can be used to model an AC microgrid system consisting of distributed generators. When the communication to a subsystem from one of its neighbors is attacked by a DoS attack, the transmitted information is unavailable and the existing distributed adaptive methods used to estimate the bound of the n th-order derivative of the reference trajectory become nonapplicable. To overcome this difficulty, we first design a new distributed estimator for each subsystem to ensure that the magnitude of the state of the estimator is larger than the bound of the n th-order derivative of the reference trajectory after a finite time. By employing the estimator state, a distributed observer with a switching mechanism is proposed. Then, a new block backstepping-based decentralized adaptive controller is developed. Based on the DoS communication duration property, convex design conditions of observer parameters are derived with the Lebesgue integral theory and the average dwell time method. It is proved that the output tracking errors will approach a compact set with the developed method. Finally, the design method is successfully applied to show the effectiveness of the proposed method to solve the power sharing problem for AC microgrids.

In this article, for second-order multiagent systems with uncertain disturbances, the finite-time leader-follower consensus problem has been investigated. First, by considering that the leader's states are only available to part of the followers, a distributed estimator is constructed to estimate the state tracking errors between the leader and each follower. Then, an estimator-based control scheme is proposed under the event-triggered strategy to achieve finite-time leader-follower consensus. Besides, the event-triggered intervals are with a positive lower bound such that the Zeno behavior can be avoided. Note that the system is discontinuous under the event-triggered mechanism; thus, a nonsmooth analysis is performed. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

Industrial Internet of Things data incorporate the fundamental elements of industrial processes, providing novel paradigms of predictive maintenance for complex industrial equipment. Remaining useful life prediction is critical in the predictive maintenance task of product lifecycle management, which has attracted increasing research attention. However, most existing prediction methods cannot effectively extract complex multiscale temporal patterns and cannot meet the real-time requirements of industrial sites. To address these issues, we propose a$T^{2}$-Tensor-aided multiscale transformer for accurate and effective prediction in this article. We defined the$T^{2}$-tensor to represent the multiscale temporal pattern by reconstructing the time series. Besides, a high-order transformer for multiscale feature extraction is proposed. Particularly, the multiscale characteristics can be captured through intertoken and intratoken. In addition, a transformer parameter lightweighting method with tensor ring decomposition is developed. Experiments demonstrate the accuracy and efficiency of the proposed method.

This article investigates the leaderless consensus problem for uncertain high-order nonlinear multiagent systems with event-triggered communication. Under a directed graph condition, a fully distributed adaptive control strategy is presented. The main contributions are summarized as follows: 1) globally Lipschitz condition, as required in many existing literatures, is not needed in this article; 2) for each agent, only one filtered output signal is required to be broadcast to its neighbors, which can effectively relieve network transmission burden; 3) the distributed adaptive controllers and event-triggered conditions are co-designed based on a single Lyapunov function such that continuous monitoring of neighbors’ states is not needed; and 4) by introducing an exponential convergence term to the designed triggering condition, Zeno behavior is avoided while all the agents’ outputs can reach asymptotically leaderless consensus. Moreover, to easily implement the designed triggering condition and further reduce the triggering frequency when the consensus errors converge to the neighborhood of origin, a switching type of triggering condition is presented and bounded consensus can be guaranteed. Simulation results are given to validate the presented distributed consensus control scheme.

A systematic differentiator-based adaptive backstepping control methodology is proposed for a class of sandwich-like nonlinear system with unknown state-dependent deadzone nonlinearity and parametric uncertainties. The novelty of our approach is that a high-order sliding mode differentiator is utilized to estimate the nonstrict feedback coupling term resulting from the sandwiched deadzone, and all the outputs of the differentiator are integrated into the backstepping procedure based on Lyapunov functions with flat zone recursively. By this approach, all the unknown parameters are estimated online, the discontinuity of the virtual input caused by bound estimations is avoided. It is shown that the ultimate boundedness of all the closed-loop signals is achieved and the output tracking error converges to a preset set. Simulation is performed to verify the theoretical findings.

This paper investigates the formation control problem for a group of nonholonomic mobile robots (NMRs) with unknown parameters and deception attacks. The information transmitted among different robots is represented by a directed graph and only a subset of the robots can obtain the full information of the desired trajectory directly. For those robots which cannot access to the reference trajectory directly, distributed estimators are designed to estimate the unknown trajectory information by using only locally available information. Besides, the sensor-to-controller transmitting channels and the communication among connected robots are suffering from deception attacks. Adaptive laws and compensation terms are designed to handle the issue of attack-induced uncertainties. Then, a novel distributed resilient formation control scheme is designed, based on which a sufficient condition is developed to guarantee that the formation errors converge to a compact set and all the closed-loop signals are bounded. Experimental results are given to validate the theoretical studies.

In this article, we investigate the output consensus tracking problem for a class of high-order nonlinear systems with unknown parameters, uncertain external disturbances, and intermittent actuator faults. Under the directed topology conditions, a novel distributed adaptive controller is proposed. The common time-varying trajectory is allowed to be totally unknown by part of subsystems. Therefore, the assumption on the linearly parameterized trajectory signal in most literature is no longer needed. To achieve the relaxation, extra distributed parameter estimators are introduced in all subsystems. Besides, to handle the actuator faults occurring at possibly infinite times, a new adaptive compensation technique is adopted. It is shown that with the proposed scheme, all closed-loop signals are globally uniformly bounded and asymptotically output consensus tracking can be achieved.

This article presents a distributed consensus-based successive convex approximation (DSCA) algorithm to solve nonconvex nondifferentiable economic dispatch (ED) problems. The ED model formulated incorporates generation constraints, valve-point effects, and multiple fuel types. A perturbation technique enables the proposed DSCA to tackle such a nondifferentiable and nonconvex optimization, which paves the way to solving more complicated optimization problems that occur in practical applications. The local generation constraint is taken care by a local surrogate convex optimization directly. The global equality constraint is handled based on a consensus protocol, where the local generation-demand mismatch among all dispatchable generators (DGs) is shared in a distributed manner. As a result, the power distribution of DGs is updated, and the generation cost is minimized. Several case studies show that the proposed DSCA algorithm can achieve superior ED solutions and computational efficiency over existing nonconvex optimization algorithms.

In this article, a distributed adaptive iterative learning control for a group of uncertain autonomous vehicles with a time-varying reference is presented, where the autonomous vehicles are underactuated with parametric uncertainties, the actuators are subject to faults, and the control gains are not fully known. A time-varying reference is adopted, the assumption that the trajectory of the leader is linearly parameterized with some known functions is relaxed, and the control inputs are smooth. To design distributed control scheme for each vehicle, a local compensatory variable is generated based on information collected from its neighbors. The composite energy function is used in stability analysis. It is shown that uniform convergence of consensus errors is guaranteed. An illustrative example is given to demonstrate the effectiveness of the proposed control scheme.

In this paper, the cooperative formation control problem is investigated for multiple two-wheeled mobile robots with unknown parameters. By combining the hierarchical decomposition and prescribed performance bound (PPB) technique, distributed adaptive formation controllers are designed based on dynamic surface control. It is proved in the Lyapunov sense that all the closed-loop signals are semi-globally bounded and the tracking error converges to a compact set, which can be made arbitrarily small by adjusting the design parameters appropriately. Furthermore, formation maintenance, connectivity preservation and collision avoidance can be achieved with the proposed control scheme. Simulations are also given to verify the effectiveness of the theoretical results.

Cyber-physical systems (CPSs) are often complexly nonlinear and uncertain. This paper investigates the adaptive output-feedback control problem for CPSs subject to intermittent denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of uncertain second-order strict-feedback nonlinear systems. When a DoS attack is active, the output signal becomes unavailable. To overcome this diffculty, an adaptive observer is constructed. Based on an average-dwell-time (ADT) method incorporated by frequency and duration properties of DoS attacks, convex design conditions of controller parameters are derived by solving a set of linear matrix inequalities (LMI). The proposed controller guarantees that all closed-loop signals remain globally bounded. An illustrative example is included to validate the theoretical results.

This paper investigates the model reference adaptive control problem for a class of linear networked control systems with unknown parameters and intermittent denial-of-service (DoS) attacks. An output feedback based adaptive resilient controller is designed by adopting the projection technique. A sufficient condition, regarding the frequency and duration constraints for DoS attacks, is provided such that the global uniform boundedness of all the closed-loop signals can be guaranteed despite the occurrence of DoS attacks. Moreover, the error performance is analysed. Simulation results are given to demonstrate the theoretical finding.

This article addresses the leader-following consensus of a class of multi-agent systems (MASs) subjected to saturation. Unlike previous literature, the followers are with heterogeneous dynamics. To solve this problem, we employ the low-gain feedback technique and the parameterized algebraic Riccati equations to design the controllers. For the fixed and switching network topologies, sufficient conditions are put in place to guarantee the semiglobal stability of the consensus error system. Numerical results are also provided to validate the effectiveness of the control design.

In this paper, we investigate the output tracking control problem for a class of uncertain nonlinear systems in parametric strict feedback form with quantized input. A novel backstepping based adaptive quantized control scheme is proposed. Different from the existing results, the true quantization parameters are allowed to be unknown in the design of adaptive controller. It is shown that with the proposed control scheme, the system output can track the desired trajectory asymptotically and all the closed-loop signals are globally uniformly bounded.

In this paper, we investigate the adaptive consensus control for a class of high-order nonlinear systems with different unknown control directions where communications among the agents are represented by a directed graph. Based on backstepping technique, a fully distributed adaptive control approach is proposed without using global information of the topology. Meanwhile, a novel Nussbaum-type function is proposed to address the consensus control with unknown control directions. It is proved that boundedness of all closed-loop signals and asymptotically consensus tracking for all the agents' outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme.

A collaborative security detection method is investigated for the Internet of Things (IoT) systems. Consensus protocol is utilized to implement the information sharing and fusion in a collaborative manner. A game theoretical analysis framework is developed for the collaborative security detection by considering the confrontation between the defender and the attacker. The objective is to achieve the maximum security protection for the entire IoT systems. The existence and uniqueness of the Nash equilibrium of game model with complete consensus are analyzed. Then an iteration learning based calculation method is presented to determine the Nash equilibrium. Quantitative analysis is provided for the relationship between the Nash equilibriums of the game models in the cases of complete and incomplete consensus with infinite and finite number of iterations. Simulation results by considering DDoS attack are also provided to verify our theoretical results.

Correlation filters are special classifiers designed for shift-invariant object recognition, which are robust to pattern distortions. The recent literature shows that combining a set of sub-filters trained based on a single or a small group of images obtains the best performance. The idea is equivalent to estimating variable distribution based on the data sampling (bagging), which can be interpreted as finding solutions (variable distribution approximation) directly from sampled data space. However, this methodology fails to account for the variations existed in the data. In this paper, we introduce an intermediate step-solution sampling-after the data sampling step to form a subspace, in which an optimal solution can be estimated. More specifically, we propose a new method, named latent constrained correlation filters (LCCF), by mapping the correlation filters to a given latent subspace, and develop a new learning framework in the latent subspace that embeds distribution-related constraints into the original problem. To solve the optimization problem, we introduce a subspace-based alternating direction method of multipliers, which is proven to converge at the saddle point. Our approach is successfully applied to three different tasks, including eye localization, car detection, and object tracking. Extensive experiments demonstrate that LCCF outperforms the state-of-the-art methods.11.

This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and backstepping control, a two-step design method is proposed to construct controllers and, with the aid of neural networks, all system nonlinearities are allowed to be unknown. Moreover, a linear time-varying model and a similarity transformation are introduced to circumvent the obstacle brought by quantization, and the controllers need no information about the quantizer parameters. The proposed scheme is able to ensure the boundedness of all closed-loop signals and steer the containment errors into an arbitrarily small residual set. The simulation results illustrate the effectiveness of the scheme.

For quadrotor aircraft, atmospheric turbulence, wind shear, and model uncertainty are important factors which affect the quality of flight, or even lead to a failure of flight. In this paper, we consider the control problem of quadrotor aircraft with linear or nonlinear disturbances including model uncertainty. By employing backstepping technique, adaptive backstepping controllers are proposed which ensure the stability and reliability of the closed-loop system. Simulation results illustrate the effectiveness of proposed scheme.

In this paper, a distributed adaptive control scheme is proposed for nth order multi-agent systems with pure integrator type of subsystem dynamics. It is assumed that the information transmission condition among different subsystems is represented by a fixed, balanced and weakly connected directed graph. The full knowledge of desired trajectory is allowed totally unknown by part of the subsystems, except that its first nth derivatives are bounded. It is shown that the globally uniform boundedness of all closed-loop signals and asymptotically consensus tracking for all the subsystem outputs can be guaranteed.

In this paper, the position control problem of a gear transmission servo system with dead-zone nonlinearity is investigated. All the parameters involved in both system model and dead-zone nonlinearity model are allowed totally unknown. An adaptive back stepping control scheme is presented. The effects of dead-zone nonlinearities are described by mismatched and matched disturbances, which are compensated by introducing additional estimates of their bounds in control laws and robust terms in parameter update laws. It is shown that all the closed-loop signals can be ensured bounded and the output regulation error will converge to a compact set. Simulation results are provided to show the effectiveness of the proposed adaptive control scheme.

In this paper, we address adaptive predictor feedback design for a simplified drilling system in the presence of disturbance and time-delay. The main objective is to stabilize the bottomhole pressure at a critical depth at a desired set-point directly. The stabilization of the dynamic system and the asymptotic tracking are demonstrated by the proposed adaptive control, where the adaptation employs Lyapunov update law design with normalization. The proposed method is evaluated using a high fidelity drilling simulator and cases from a North Sea drilling operation are simulated. The results show that the proposed predictor controller is effective to stabilize the bottom hole pressure within the desired margins and compensate the effects of the delay and disturbance.

In multivariate systems, the causality relationships between any two different data variables and the corresponding path models are often unknown. In this paper, the identification of bidirectional path models of a bivariate system is investigated by extending the augmented UD identification (AUDI) algorithm proposed by Niu(1992) which can simultaneously identify the order and parameters for open-loop systems with unclear physical meanings of the even columns in the data matrix. To extract more information than the AUDI algorithm for identification of bidirectional path models, we develop a novel approach based on construction of the interleave data vector and UD factorization of the data matrix. The odd and even columns of the resulting data matrix correspond to the parameters of the forward and backward path models, respectively. Moreover, the information contained in the data matrix can be evaluated to determine the causality between the two data variables. The ARMAX process with white noise is first considered. The results are then extended to the case with colored noise. Simulation results are presented to show the effectiveness of our proposed methods.

In this paper, we consider the problem of designing distributed adaptive consensus tracking controllers for multiple nonlinear systems with unknown parameters and external disturbances. The desired trajectory is time varying given by the state of a reference system, which is only available to a portion of the group of the systems. Besides, the dynamics of the reference state is bounded but unknown to all of the systems. The communication graph characterizing the interactions among the systems is assumed to have undirected, fixed and connected topology. By introducing distributed estimators for the bound of the reference dynamics, two control schemes are proposed to address the problem. In the first scheme, a sign function is employed and perfect consensus tracking can be achieved. In the second scheme, an alternative control law is developed and the chattering phenomenon caused by the sign function can be reduced. However, new challenge will be triggered which is to compensate for possible destabilizing effects of the coupling elements relating to local parameter estimation errors and the synchronization errors of the neighbors. The overall communication graph is firstly reduced to an undirected spanning tree with single system notified of the reference state. Based on this, new synchronization error for each subsystem is then defined as the weighted distance relative to only one of its neighbors. It is shown that all the synchronization errors will converge to a prescribed bound which can be made as small as desired in this case.

In this paper, the results of stabilizing a large scale nonlinear systems with uncertain dynamic interactions and unmodelled dynamics depending on both subsystem inputs and outputs in [9] and [10] are extended to highly nonlinear systems. Certain modifications on standard adaptive backstepping controllers are proposed to compensate for the effects of interactions from other subsystems in designing local controllers. .

We present several improvements on conventional active shape models (ASM) for face alignment. Despite the accuracy and robustness of ASMs in image alignment, its performance depends heavily on the initial parameters of the shape model, as well as the local texture model for each landmark and the corresponding local matching strategy. In this work, to improve ASMs for face alignment, several measures are taken. First, salient facial features, such as the eyes and the mouth, are localized based on a face detector. These salient features are then utilized to initialize the shape model and provide region constraints on the subsequent iterative shape searching. Secondly, we exploit edge information to construct better local texture models for landmarks on the face contour. The edge intensity at the contour landmark is used as a self-adaptive weight when calculating the Mahalanobis distance between the candidate and reference profile. Thirdly, to avoid unreasonable shift from pre-localized salient features, landmarks around the salient features are adjusted before applying global subspace constraints. Experiments on a database containing 300 labeled face images show that the proposed method performs significantly better than traditional ASMs.