This paper explores the stabilization control problem of nonlinear systems with time-varying input delay in the presence of unknown sensor sensitivity. Different from existing methods, we consider the effects of large sensor sensitivity and input delay on the system, in which the sensor sensitivity can be described as an unknown time-varying function with an arbitrarily large bound. To address this, we propose a truncated predictive output feedback control method based on the classification of unknown sensor sensitivity. Firstly, by designing the truncation constant, a truncation classification method of sensor sensitivity is established, dividing the sensitivity into two categories: small sensor sensitivity and large sensor sensitivity. Secondly, a novel Lyapunov-Krasovskii functional is constructed to deal with the impact of truncated prediction and large sensor sensitivity classification on system stability under time-varying input delay. Then, based on the constructed observer, a truncated predictive controller is designed. Within the framework of the new Lyapunov-Krasovskii functional analysis, and with the aid of Lyapunov stability theory, it is strictly proved that under the action of the output feedback law, all states of the system can converge to zero regardless of the sensor sensitivity. Finally, the effectiveness of the proposed method is verified by a simulation example.

Rotating machinery fault diagnosis is essential for ensuring the reliability of industrial assets in IIoT-enabled manufacturing environments, where the high variability of operating conditions has driven the widespread adoption of multi-sensor fusion (MSF) to extract discriminative fault features. However, harsh IIoT environments frequently cause partial sensor failures or communication interruptions, under which conventional multi-sensor fusion systems often suffer significant performance degradation or even complete fusion failure. To address this issue, a Multi-task Multi-input Multi-scale Mixture-of-Experts (M4oE) framework is proposed, in which an independent diagnosis task is constructed for each sensor signal, ensuring that any individual sensor stream, representing the most extreme case of single-sensor availability, can independently perform diagnostic inference. First, a multi-scale mixture-of-experts feature extraction scheme is proposed, in which both the task-specific experts and each shared expert are implemented using the proposed Omni-scale Dilated Convolution Neural Network (OSD-CNN) architecture. Subsequently, task-specific features and shared features are integrated through multi-level feature fusion and fed into task-specific decoders to generate diagnostic results. Since the diagnostic outputs obtained from each sensor have the same identification framework and independent evidence sources, M4oE further employs Dempster-Shafer (D-S) decision-level fusion to enhance the reliability of the overall diagnostic system. Finally, comprehensive evaluations are conducted on three different types of rotating machinery datasets, including pumps, rolling mills, and bogies, to verify the accuracy, robustness, and scalability of the proposed M4oE framework.

The communication links of multiagent systems (MASs) could be compromised by stealthy cyber attacks. To solve the problem, this paper proposes distributed nodes detection and event-triggered links isolation of stealthy attacks in MASs by topology-oriented watermarking (TOW). Firstly, the limitation of traditional MASs against distributed pole-dynamics attacks (dPDAs) is revealed. Secondly, different from existing water-marking methods for single-agent systems, a novel TOW method with four optional parameters selection schemes is proposed for distributed nodes detection in MASs, where it is proved that only one of optional parameters selection schemes can simultaneously possesses complexity and resistance to collusive dPDAs. Moreover, TOW combines the advantages of multiplicative and additive watermarking to achieve dPDAs detection. Furthermore, the positive relationship between dPDAs detection performance and the coupling factor of attack topology, attack signal and TOW parameters is quantified. Thirdly, a new TOW-based event-triggered links isolation algorithm incorporating multiple link-deletion observers is proposed, where TOW is leveraged to trigger running of link-deletion observers. It is proved that the compromised links can be correctly isolated. Finally, the simulation results verify our proposed scheme.

Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of-distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject-independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology.

A novel event-triggered adaptive finite-time control strategy is developed for a class of uncertain fractional-order stochastic nonlinear systems (SNSs). The fractional orders of the systems can be represented by the ratio of arbitrary two positive odd integers, so that the target systems could contain both highorder SNSs and low-order SNSs. A new more general and flexible asymmetric barrier Lyapunov function (BLF) is established to fulfill the requirement of asymmetric dynamic output constraints. By adoptingadding a power integraltechnique and backstepping method, a finite-time controller with the adaptive laws is codesigned. The ubiquitous system uncertainty is directly estimated by adaptive method, and no extra approximation errors are introduced. An event-triggered mechanism is presented to reduce the update frequency of the controller, overcoming the difficulties caused by the system’s stochastic characteristics and fractional power of the control input. It is strictly proved that the trivial solution of the system is finite-time attractive and stable in probability, meanwhile the system output is constrained within the prescribed asymmetric time-varying boundaries. Simulation experiments demonstrated the effectiveness of the main result.

This paper delves a robust neurooptimal shared control tactic for teleoperation systems affected by model uncertainties and external disturbances. A novel shared-control structure is established with an explicitly defined and dynamically adjustable weights mechanism, enabling smooth authority transitions across control modes and consistent task execution. A neural near-optimal adaptive dynamic programming (ADP) framework firstly applied to uncertain teleoperation eschews the conventional dependence on precisely known dynamics to deliver near-optimal control. Moreover, a prescribed-time exponential disturbance observer (PPTEDO) featuring a rigorously designed continuous switching function, thereby overcomes the inherent instability and chattering pitfalls associated with discontinuous PT schemes. Under the impedance control layer, a reset regularized exponentiation sliding mode (RRESM) surface is designed to ensure fast transient-state response while elevating system transparency and improve human-robot interaction fluency. Additionally, the absolute stability of the closed-loop teleoperation system is rigorously proved via establishment of Lyapunov candidate function. Finally, experiment results are presented to demonstrate the practical availability of the designed algorithm.

State constraints are crucial for preventing system hazards by enforcing safety boundaries, yet ensuring state constraints for underactuated mechanical systems (UMSs) operating in complex environments remains unsolved due to their limited workspace and inherent underactuation. To address this critical safety issue involving unactuated states, this paper proposes a novel unified adaptive state-constraints control (UASCC) scheme based on sliding mode control (SMC) for a class of multi-input multi-output (MIMO) UMSs that do not satisfy the strict-feedback form. First, an innovative auxiliary term, integrating a nonlinear state-dependent function (NSDF) and the sliding mode variable signal, is designed to rigorously maintain all state variables within prescribed irregular time-varying constraints. Simultaneously, by designing a new time-dependent shifting function (TDSF), the proposed scheme allows for responses to irregular time-varying constraints under various scenarios without the need to modify the controller structure. Furthermore, this work eliminates the requirement for known uncertainty upper bounds in the SMC of UMSs. By introducing an adaptive time-varying control gain, the resulting continuous control scheme effectively mitigates controller gain overestimation and chattering, thereby enhancing the system’s transient performance. The system’s stability is rigorously proven through Lyapunov theory. Finally, the effectiveness of the proposed control method is experimentally validated on both a laboratory bridge crane platform and a four-degree-of-freedom (4-DOF) tower crane platform.

This article addresses the problem of prescribed performance tracking control for uncertain differential-drive mobile robots equipped with current sensorless electric motors. An improved line-of-sight distance control method with a desired heading angle switching strategy is proposed, which eliminates the singularity problem encountered in previous works when the line-of-sight distance approaches zero. In addition, an open issue of mobile robots control under unavailable motor current (or torque) and completely unknown motor parameters is tackled by combining differential homeomorphism transformations with a low-complexity prescribed performance control approach. Furthermore, the limitations imposed by initial high-order state errors in low-complexity control methods are overcome by introducing a time and space dependent amplifier. The proposed method guarantees the convergence of the position tracking error to the desired precision within the prescribed time, with rigorous proof provided. The effectiveness of the method is demonstrated through simulations and experiment comparisons.

This paper studies the global finite-time control problem for uncertain high-order nonlinear systems (HNSs) with event-triggered input and deferred output constraint. The bounds of control coefficients are not required to be known and odd rational powers are allowed in the system. In this case, unlike the existing adaptive estimation control results can only achieve bounded or asymptotic stability, the proposed control strategy focuses on ensuring finite-time stability (FTS). Specially, two sets of distinct power-type parameters are introduced in control process to reconstruct the adaptive law and integral-type candidate Lyapunov functions respectively, such that the residual terms containing uncertainties can be dominated by the stabilizing terms. To reduce communication burden, an event-triggered mechanism is developed with a state-dependent function instead of a constant, enabling a timely update for controller as all state variables reach zero. Based on Lyapunov analysis and FTS theory, it is proven that the deferred output constraint can be guaranteed and all state variables reach the origin in a finite time under the designed controller. Two simulation examples are illustrated to verify the validity of theoretical results.

Multiagent systems (MASs) are increasingly vulnerable to cyber attacks that exploit limited system resource to evade detection while causing disruptions. This paper investigates the resource-sensitive cyber attacks for MASs. Firstly, distributed replay attack (dRAs) and distributed pole-dynamics attack (dPDAs) are proposed, where dRAs utilize available resources of agents historical measurements, and dPDAs utilize available resources of agent parameter. Secondly, the relationship between available resources and stealthiness of cyber attack are revealed, where both dRAs and dPDAs can be with 0-stealthiness under different available resources. Thirdly, the relationship between available resources and destructiveness of cyber attack are revealed, where dRAs is not with ck-s-destructiveness (i.e., cyber attacks can achieve state trajectory destructiveness without disrupting consensus), because the state trajectory can only be driven to the historical one due to requiring historical measurements. Oppositely, dPDAs can be with ck-s-destructiveness, where the state consistency trajectory can be driven to any one designed by the attacker due to requiring agent parameter. Finally, numerical examples are provided to verify the effectiveness of the proposed attacks.

To alleviate the annotation burden in supervised learning, various weakly-supervised learning (WSL) configurations have emerged-such as pairwise, triplet, and N-tuple based supervision-that leverage instance-level relationships to reduce labeling costs. While these methods have demonstrated strong performance across diverse tasks, most existing methods are limited to homogeneous tuples and do not support learning from mixed-class tuples, particularly when the tuple size exceeds three. To address this gap, this paper introduces a framework called Mixed-class Triplet and Unlabeled Data (MTU) learning, which enables classification from triplets containing instances from different classes, combined with unlabeled data. We further extend this framework to a more general setting, Mixed-class N-tuple and Unlabeled (MNU) learning, which handles arbitrary-length tuples ( N$\geq$3 ) with mixed-class composition. Both MTU and MNU leverage unlabeled instances to enhance supervisory signals and improve classification performance. We formulate tailored empirical risk minimization (ERM) objectives for both learning settings and derive generalization error bounds to provide theoretical guarantees. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed methods under various weak supervision scenarios.

This paper introduces a robot active task cognition framework for Situation-Aware Task Planning (SATP), leveraging visual scene understanding to generate action sequences. By integrating object knowledge, user preferences, and Large Language Models (LLMs), SATP interprets the robot’s current visual perception, and creates procedural actions that align with what the robot “sees”. Diverging from conventional methods requiring explicit verbal commands, our SATP framework autonomously performs task cognition, actively formulating robot-executable action sequences directly from visual input. Initially, a novel approach for describing the visual scene is presented, enabling the robot to grasp detailed object-level properties and inter-object relationships based on its observations. Building on this, a knowledge base for active task cognition is constructed using ontology technology. Furthermore, we develop a two-stage dual-feedback task planner, ReProg+, powered by LLMs, specifically designed for situation-aware task planning grounded in visual data. The efficacy, reliability, and advantages of our solution are thoroughly validated in real-world visual scenarios. Additionally, SATP has been tested with a real robot, with results confirming the feasibility and effectiveness of our approach.

This article proposes a finite-time prescribed performance control (FTPPC) method for interconnected nonlinear systems with input saturation. By adding nonnegative auxiliary signals to the setting time of finite-time prescribed performance functions (FTPPFs), we present saturation-tolerant FTPPFs, which are easy to observe the convergence time. Compared with traditional FTPPFs, saturation-tolerant FTPPFs are able to expand from or restore to the expect constraint boundaries based on the input saturation error as well as whether the system enters the collision avoidance regions. Thus, the potential conflicts between input saturation and FTPPFs are resolved. Combined with saturation-tolerant FTPPFs, a low-complexity control algorithm is proposed, which omits the need for a function to estimate the unknown terms and reduces the computation. With the designed control scheme, the boundedness of all closed-loop signals is strictly proved when the feasibility condition is satisfied. Ultimately, simulations are presented to show the capability of the designed controller.

For stochastic sampled-data systems characterized by unknown nonlinear dynamics (SSDUNSs), it is a great challenge to design an appropriate controller to achieve stable tracking control. In this article, a perceptron-based adaptive model predictive control (PAMPC) scheme is developed for SSDUNSs with multiple discrete stochastic sampling intervals. The activation frequency of each sampling interval can be statistically obtained, which can be described by the categorical distribution. First, a PAMPC structure is developed for the tracking control of SSDUNS. A perceptron with a cost function is designed to incorporate the exploration of the environmental state, encompassing the sampling interval, predictive error, and tracking error. Second, an adaptive predictive horizon (APH) is incorporated into the predictive model to provide the necessary predicting information for the controller. APH is adjusted based on the activation frequency of stochastic sampling intervals. Third, an optimal control problem (OCP) combined with the penalty of the perceptron is designed to stabilize SSDUNS. Then, the control law can be computed to achieve the stable tracking control of SSDUNSs. Finally, the stability of the proposed method is analyzed theoretically to ensure its reliability and robustness. In addition, the effectiveness of the designed method is verified by numerical simulations and real-world applications in the context of wastewater treatment processes (WWTPs).

This article investigates the fully distributed output feedback consensus-tracking control problem for multiple wheeled mobile robots (multi-WMRs) subject to dual-actuator faults (DAFs) under the directed graph. First, a fully distributed estimator is designed to asymptotically estimate the leader's states with nonzero input under event-triggered communication. Next, novel filters are introduced to compensate for unmeasured velocity information in the presence of DAF, and output feedback fault-tolerant controllers are developed to ensure asymptotic consensus tracking despite the faults of actuators. Each robot in the considered multi-WMR system is equipped with two controllers that are co-designed in a unified framework. The final control laws are obtained by solving a set of equations, for which the existence of a unique solution is rigorously established. The proposed scheme not only enables output feedback control under partial loss-of-effectiveness (PLOE) faults in dual actuators, but also ensures fully distributed consensus tracking with event-triggered communication. The simulation results validate the effectiveness of the proposed approach.

This article focuses on the prescribed-time full-state consensus control of feedforward multiagent systems (MASs), and a new framework and analysis are presented. First, to deal with the obstacle arising from inherent feedforward nonlinearity, a crucial aspect of the design is to creatively construct the coordinate transformation at each step and the prescribed-time function as a scaling factor. Subsequently, a novel prescribed-time homogeneous domination framework for feedforward MASs is developed. The significant advantage is that this framework combines the low complexity of homogeneous domination control method design with the simplicity of stability analysis for state-scale schemes. Then, based on the recursive techniques, a distributed prescribed-time full-state consensus controller is designed, which drives the consensus errors to reach equilibrium at any prescribed time and ensures the stability of the entire time interval. Finally, the proposed algorithm is validated through the liquid-level control resonant circuit (LLCRC) system.

This article investigates the optimal tracking control problem for high-order uncertain nonlinear systems by developing a simplified reinforcement learning (RL) framework with minimal neural networks (NNs). In contrast to conventional RL-based schemes that rely on recursive backstepping and require $3n$ NNs (where $n$ is the system order), the proposed method leverages high-order fully actuated (HOFA) system theory to reformulate the dynamics into a compact normal form. This enables a unified, nonrecursive controller design that requires only three NNs regardless of the system order, thereby significantly reducing computational complexity and facilitating practical implementation. Furthermore, this work overcomes a critical theoretical deficiency in existing simplified RL strategies, where the vanishing minimum eigenvalue of the NN basis function correlation matrix often leads to invalid Lyapunov stability analysis. A novel critic-actor weight update law is designed to bypass this problematic matrix, rigorously guaranteeing the semiglobal uniform ultimate boundedness of the closed-loop system without requiring persistent excitation (PE) conditions. Simulation results on a representative example demonstrate the effectiveness and computational efficiency of the proposed approach compared with existing methods.

The problem of command-filter-based adaptive fixed-time tracking control is investigated for nonlinear systems with time-varying uncertain parameters and disturbances in this article. Existing fixed-time control strategies via an adaptive approach are primarily bounded-error, trajectory tracking-oriented. Different from previous results, we propose a new fixed-time stability lemma utilizing an exponential decay function. Then, by leveraging the proposed lemma and command filtered backstepping technique, a novel adaptive fixed-time control scheme is constructed, which can reduce the computational complexity and completely counteract uncertain parameters. We demonstrate that the tracking error enters a neighborhood near zero within a fixed-time and ultimately converges to zero. Furthermore, through the incorporation of a piecewise function into both the filter error compensation system and virtual control laws, the second-order derivability of virtual control laws is guaranteed, thereby ensuring the validity of the command filter. Finally, the proposed strategy's effectiveness is confirmed through simulation results.

This article investigates the stealthy distributed pole-dynamics attacks (dPDAs) detection for multiagent systems (MASs) by distributed additive watermarking (DAW). First, the limitation of traditional MASs for dPDAs is revealed, where dPDAs cannot be detected. Second, unlike the well-established single-agent additive watermarking, to eliminate the side effect of watermarking signal on system state and enable dPDAs detection, the proposed DAW adds watermarking to the control signal of any agent for transmission and removes watermarking of the control signal after receiving it. Meanwhile, the covariance of the watermarking signal in DAW for all agents is different from each other to enable compromised links isolation. Furthermore, the relationship between the dPDAs detection performance and DAW is quantified in the sense of expectation, where the dPDAs detection performance is directly proportional to the sum of the watermarking covariance of the compromised links. Third, leveraging the relationship between dPDAs detection performance and DAW, a DAW-based link isolation scheme is proposed to accurately isolate the compromised links by comparing with the detection function and its approximation, where the approximation of the detection function is iteratively calculated on all possible attack links combination for the compromised agent. As a result, the adverse impacts of dPDAs on MASs are mitigated. Finally, simulation results are conducted to validate the theoretical results.

In this article, the high-order fully actuated (HOFA) system approach is applied to study the matrix threshold strategy dynamic event-triggered control problem of a single-link flexible joint robot system (SFJRS). First, the SFJRS is transformed into an HOFA system model by using the recursive "ascending dimension and descending order" method. On this basis, different from the traditional method based on the state-space model, a novel matrix threshold strategy dynamic event-triggered control scheme based on the HOFA system approach is proposed, which not only simplifies the control design but also greatly saves communication resources. It is proved that the closed-loop systems are asymptotically stable under the proposed control strategy. Finally, the superiority of the HOFA system approach and the matrix threshold strategy dynamic event-triggered control method is demonstrated through two different simulation experiments.

This work studies the problem of data-driven event-triggered control of continuous-time multiagent systems (MASs) with communication delays. A hybrid system approach is proposed to address this problem, hybrid dynamic event-triggering mechanism (DETM) is utilized to ensure strong Zeno-freeness and estimated system matrix is introduced to design time-varying state estimator. By using several auxiliary variables, the closed-loop MAS is described into hybrid system form, that can completely describe the flow dynamics and jump dynamics of the MAS in the presence of communication delays. Then, data-based controller gain matrix design is developed and an estimator is designed to estimate unknown system matrix. After that, based on derived model-based stability conditions and combined with data-driven representation of MAS, data-based stability analysis and event-triggering mechanism (ETM) design results are obtained. The main advantages of the proposed approach in contrast with existing works are that accurate system model is not needed and strong Zeno-freeness is ensured under the scenario with communication delays. Finally, the effectiveness of the proposed method is verified by simulation example.

This article proposes a long-term dynamic object relocalization (L-DOR) solution to address the challenge of mobile robots efficiently relocalize task-related objects in human–robot coexisting environments over extended periods. Existing methods mainly focus on one-time object localization, neglecting long-term relocalization amidst dynamic changes caused by human activities. To tackle this issue, a probabilistic model of object distribution based on spatio-temporal patterns is first established. Then, a robot-object perception interaction is introduced to achieve dynamic object category discrimination, enabling the robot to handle sporadic events caused by human activities. Besides, a cost-expectation balance-based object matching method is presented to determine the suitable task object and gradually infer its potential locations with probabilistic model. On this basis, we suggest a hierarchical global planning and local decision-making to prioritize search efforts to improve localization efficiency. Extensive comparisons and long-term experiments in real-world scenarios with a Fetch robot demonstrate the efficacy of L-DOR in terms of search performance, adaptability to different scenarios, and long-term effectiveness.

Video Corpus Moment Retrieval (VCMR) is pivotal to multimodal understanding. However, existing methods rely heavily on large-scale annotated data, which limits their generalization and scalability. To address this issue, we propose a training-free VCMR framework, termed Synergistic Collaboration and Adaptive Calibration (SCAC), enabling effective semantic parsing and precise temporal localization without parameter updates. SCAC introduces a Query Event Chain Generation module that leverages large language models to transform complex textual queries into structured event chains, while a Video Event Chain Generation module represents videos as semantically coherent event chains through subtitle segmentation and keyframe aggregation. Built on these structured representations, SCAC performs Event-Chain-Based Cross-Modal Retrieval with mean-variance joint scoring to suppress local mismatches and reinforce global consistency. During localization, a Synergy-Calibration Mechanism dynamically refines temporal boundaries via profit-setback feedback. Extensive experiments show that SCAC achieves comparable or superior results to supervised counterparts under training-free conditions, demonstrating strong cross-modal generalization and adaptive capability. The code of our method is available at https://github.com/cyanlll/SCAC.

Object instance segmentation is a key prerequisite for service robots to perform daily chores in unstructured environments. Traditional supervised learning-based segmentation solutions rely on massive annotated datasets, which are impractical for the wide variety of objects in real-world scenarios. To this end, we propose a novel zero-shot instance segmentation approach (TransZSIS) that enables precise instance segmentation without relying on external semantic embeddings or auxiliary information to address the unseen object instance segmentation (UOIS) problem. First, the RGB and depth images are segmented into irregular patches based on a super-pixel segmentation algorithm to generate a unified segmentation map, and then the comprehensive feature vectors of each patch is extracted and paired. Further, a Transformer-based architecture is introduced to capture the correlation between different patch-pair and the intrinsic characteristics of each patch-pair. To predict patch-pair relationships, TransZSIS uses a four-layer fully connected neural network (FCNN) to classify the transformer-encoded features and refine them with a graph-based processing tactic to achieve object instance segmentation. Extensive evaluations on both synthetic and real datasets demonstrate that TransZSIS achieves superior performance compared with state-of-the-art baseline methods. Also, we implement real experiments to verify that our solution can achieve robot grasping by segmenting unseen objects.

IN THE above article [1], an incorrect abstract appeared alongside the article. The correct abstract is provided below.

This article addresses the distributed global prescribed-performance control problem for uncertain Lagrangian dynamics, with a particular emphasis on minimizing steady-state error oscillations. A novel global distributed prescribed-performance control framework is proposed based on a dynamic funnel function and neural network design. Specifically, by integrating funnel barrier properties and derivative information, a new neural network learning law is developed. Furthermore, a projection operator is incorporated into the learning law to guarantee the boundedness of the weight estimates in the stability proof, ultimately avoiding potential constraint incompatibility problems caused by neural network integration. The established control framework ensures that the trajectory consensus error of robotic manipulators under distributed control satisfies global arbitrary convergence rates and steady-state error bounds while leveraging neural network approximation to mitigate the inherent uncertainties of controllers that do not require precise mathematical model, thereby effectively suppressing steady-state error oscillations. Unlike existing literature, this work pioneers the incorporation of neural networks into distributed funnel control, achieving global prescribed performance while significantly reducing steady-state error oscillations. Finally, simulation results validate the effectiveness of the proposed method.

Robust flight in complex and windy environments is critical for both single and multiple quadrotors. Existing methods either learn disturbance model at high computational cost or use error-based adaptive control with a speed-stability trade-off that makes tuning difficult. To address these issues, this paper proposes a future-trend-aware filter-based PD-MRAC (Proportional-Derivative Model Reference Adaptive Control) for single quadrotor and a distributed PD-MRAC for multiple quadrotor formation. By embedding a trend-aware derivative term in the adaptive update laws, the controller obtains anticipatory information about the error evolution, enabling rapid adaptation while mitigating oscillations. For more disturbance-sensitive multi-quadrotors, we design a robust distributed protocol under a directed graph, improving resilience to disturbances. The approach maintains low computational cost and supports fast adaptive updates. Extensive simulations and real-world experiments validate improvement. For single quadrotor, RMSE reduced by around 57% versus the baselines and by around 12% versus the DJI Mavic 2. For multi-quadrotors, formation results show enhanced robustness in simulation and effective real-world indoor/outdoor experiments under strong winds. Our project page is athttps://xiongtao-shi.github.io/PD-MRAC/.

This article investigates the task-space adaptive control problem of robot manipulators under both uncertain dynamics and kinematics. The proposed algorithm employs dual observers for the dynamic and kinematic loops to estimate joint-space and task-space velocity signals, thereby eliminating numerical differentiation, mitigating sensor-noise effects, and avoiding reliance on high-precision velocity sensors, which collectively improve control accuracy. Moreover, the controller achieves the desired separation between the dynamic and kinematic loops even in the presence of time-varying control coefficients. This separation simplifies controller synthesis and stability analysis and reduces conservatism in controller-gain selection caused by loop coupling. Finally, the asymptotic convergence of the task-space tracking error is established via the Lyapunov stability theory. The effectiveness of the proposed scheme is demonstrated through comparative simulations and experiments.

This article addresses the problem of prescribed-time stabilization of nonlinear systems with the features of unknown control directions and time-varying uncertain parameters based on a time-transformation method. The basic ideology relies on a newly established time-transformation method that incorporates the classical adaptive technique, converting the prescribed-time stable control problem of the original system into an asymptotically stable problem of its time-transformed stretched form. Unlike the existing literature, the time transformation method in this article directly gives the adaptive laws before and after the time transformation, which greatly reduces the complexity of designing the adaptive prescribed-time controller due to the fact that the design of the adaptive law in the stretched time domain only needs to satisfy the asymptotic stability criterion. Finally, the proposed methodology is validated by a simulation example.

For uncertain link-type underactuated mechanical systems (LUMSs), existing fully actuated system (FAS)-based approaches are often difficult to apply because the required structural conditions are not satisfied. This article develops an adaptive prescribed-time controller for a class of LUMSs by combining FAS theory with singular perturbation (SP). First, the underactuated dynamics are decomposed into slow and fast subsystems, and the reduced slow subsystem is transformed into a lower-order FAS without approximating the key nonlinear terms. Second, a novel prescribed-time gain (NPTG) is incorporated to improve the early transient response while alleviating the sharp-braking effect and reducing the risk of actuator saturation associated with conventional prescribed-time gains. In addition, a prescribed-time adaptive law is introduced without requiring a priori disturbance bounds. Lyapunov analysis establishes prescribed-time convergence of the reduced slow subsystem, while standard SP arguments justify practical regulation of the full-order closed-loop system. Experiments on an overhead crane verify the effectiveness and robustness of the proposed method.

This paper investigates a resilient model predictive reset control strategy for cyber-physical systems (CPSs) with state and input constraints under hybrid attacks. Jamming attacks are modeled using Stackelberg game theory and result in packet loss . A malicious false data injection attacker randomly injects fault signals to manipulate the control inputs. A channel reset strategy is proposed to ensure that the auxiliary controller can safely transmit the prediction signals to the buffer for storage. The primary controller exerts its utility in the absence of a jamming attack, whereas once the false data attack is detected, compensation and computation are performed using predictive values computed by the auxiliary controller, which can improve the system control performance. The predictive signals computed by the auxiliary controller are only used when an attack is detected in the MPC strategy proposed in this article. Furthermore, the feasibility of the proposed reset control strategy is discussed. Finally, the effectiveness and benefits of the model predictive reset control strategy proposed in this paper are verified through a numerical example.

This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems (MASs) under output event-triggered communication. A novel distributed discontinuous backstepping control strategy is presented, which utilizes information exclusively from intermittent output instants. First, a distributed adaptive event-triggered mechanism (ETM), along with a continuous-discrete time compensator and observer are designed jointly to compensate for consensus errors and reconstruct the system state, where all parameters designed are flexibly chosen. The triggering sampling instants are asynchronous and aperiodic, eliminating the necessity for continuous neighbor information monitoring. Second, we introduce a dynamic variable into the coordinate transformation to overcome the challenge of intermittent output in backstepping design, which is the key to address input errors caused by triggering mechanisms. Compared with the widely used backstepping method using the first-order filter to address intermittent signals, the proposed scheme does not require additional triggering design for the filter and achieves a full-state consensus in the global sense. Theoretical analysis shows that the system is asymptotically stable, all consensus errors converge to zero, and Zeno behavior is excluded. Finally, simulation examples are presented to demonstrate the effectiveness of the theoretical result.

This paper presents an output feedback prescribed performance control method for a class of high-order nonlinear uncertain multiagent systems. General prescribed performance consensus control for multiagent systems requires that the initial consensus error is constraining within a boundary value. Unlike existing works, the conservative condition of prescribed performance consensus control for multiagent systems is relaxed as the consensus error is independent of the initial state. In this condition, we employ a control scheme with improved prescribed performance to constraint the transient behavior of the system. By designing reduce order dynamic gain K-filter, the state variables of the systems are reconstructed. Based on the baskstepping method and dynamic surface control technology, we introduce an innovative prescribed performance event-triggered approach aimed at ensuring prescribed performance levels for both transient and steady-state aspects of consensus control and reducing the communication bandwidth resource. Furthermore, we rigorously demonstrated through the Lyapunov function that all agents can achieve consensus with the leader driven by the controller. The simulation results have demonstrated the effective tracking performance of the developed control approach. Finally, the effectiveness and reliability of the proposed control strategy are verified through the successful execution of multi-QUAVs formation encirclement control.Note to Practitioners—This paper considers output feedback prescribed performance consensus control problem of multiagent systems, which can be applied to some practical systems, e.g., low-altitude formation flight of autonomous aerial vehicle with specified transient performance, multi underwater robot for oil field line-cruising, etc. Furthermore, in harsh working environments, the states of agent devices are difficult to measure or cannot be measured. Hence the control objective in these applications can be transformed into output feedback prescribed performance consensus control problem of multiagent systems. Besides, in military applications, high-speed aircraft need to fly at a certain height above the ground in order to avoid radar detection, which poses strong limitations on the position of the aircraft. This challenging issue can be well addressed through prescribed performance control. Compared with the existing results, this paper proposed a novel prescribed performance control strategy for multiagent systems with unrestricted initial state. It should be noted that in practical applications, communication bandwidth resources are extremely limited, so we propose event triggered control to effectively alleviate the communication pressure of multiagent system cooperative control. The nonlinear high-order system model studied in this article can be converted into a Lagrangian system, multi-complexity manipulator system and an autonomous aerial vehicle system, which has strong engineering practical significance.

The existing shared control schemes and automatic control schemes are almost designed separately. Based on the designed constrained function and shared-constrained function, this paper proposes a universal control framework for high-order strict-feedback systems that can achieve safe tracking control with very low algorithm complexity regardless of whether the reference signal exceeds safety constraints, combining the flexibility of manual operating with the persistence and security of automatic control. A universal safe tracking control theorem is presented to prove the security of system operation. Furthermore, based on the proposed universal safe tracking control method, a novel time-independent path following control method with lane constraints is proposed for nonholonomic mobile robots by converting lane constraints into heading angle constraints, thus achieving automatic/auxiliary adaptive cruise control and lane keeping. Simulation and experimental results are provided to demonstrate the practical applicability of this method. Note to Practitioners—This work addresses the issue of the reference signal exceeding the safety constraint due to planner errors (in autonomous control) or human operator mistakes (in shared control). Compared to existing control methods, the proposed architecture is not only applicable to safe tracking control in both autonomous and shared control scenarios, but also features extremely low algorithmic complexity, making it highly suitable for practical deployment. Unlike traditional shared control systems, where human operators directly manipulate system inputs, the proposed method allows operators to control the system by adjusting the reference signal. This ensures that feedback control is always present, thereby reducing the difficulty of manipulation. Moreover, in contrast to common trajectory tracking methods, the proposed time-independent path following approach holds greater practical value, ultimately achieving adaptive cruise control and lane-keeping for mobile robots. To ensure repeatability, our codes are open sourced on github:https://github.com/Mudianrui/USC-and-app-in-NMRs.git. Experimental videos can be accessed throughhttps://youtu.be/VrsISKkJOCA

The issue of fixed-time tracking control of nonlinear systems subject to time-varying uncertain parameters is investigated in this article. In contrast to previous adaptive approach-based fixed-time control results which focus on driving the tracking error to a bounded region, it is technically challenging yet highly desired to achieve zero-error trajectory tracking. The primary difficulty lies in how to construct and analyze adaptive estimation schemes to completely compensate for uncertain parameters within the fixed-time convergence setting. Furthermore, the presence of time-varying uncertainties renders the systems fundamentally different from those in existing works. To tackle this challenge, a new fixed-time stability lemma utilizing an exponential decay function is proposed. Then, we develop an adaptive fixed-time controller design framework, it is demonstrated that the tracking error ultimately converges to zero after converging to a small neighborhood around zero within a fixed time, with all the closed-loop signals remaining bounded. Besides, the singularity problem in fixed-time control is circumvented. Finally, simulation results substantiate the effectiveness of the proposed strategy.

This paper focuses on the rapid jib and trolley positioning and payload sway suppression of the 4-degree of freedom (4-DOF) tower crane systems under uncertain system dynamics, external disturbances and control input constraints. A new adaptive proportional-integral-derivative sliding mode control (PID-SMC) method is proposed, which is model-free, does not need to linearize the system, and dispense with the uncertainty upper bound information required by traditional sliding mode control (SMC). In particular, a new time-varying scale function is used to constrain the system error to ensure the given-performance, that is, the actual transient-state and steady-state control performance of the system can be predetermined according to practical application requirements, and the quantified steady-state and transient-state properties of the 4-DOF tower crane system are obtained for the first time. In addition, although the actuator saturation upper bound is completely unknown the above mentioned given-performance can also be guaranteed by designing new parameter adaptive law. Finally, the effectiveness and superior performance of the control method are verified through rigorous theoretical analysis and experiments conducted on a 4-DOF tower crane platform. Note to Practitioners—The primary objective of this study is to tackle the challenges associated with rapid target positioning and effective suppression of swing in 4-DOF tower crane systems, amid presence of multiple practical problems. Traditional SMC strategies typically presume the prior knowledge of upper bounds for uncertainties—a condition that is seldom met in actual operations. Furthermore, their disregard for transient performance criteria further curtails their applicability in real-life scenarios. To overcome these limitations, we integrate an adaptive law for the estimation of previously indeterminable uncertainty boundaries. The incorporation of a performance function also allows us to impose stringent controls over both transient-state and steady-state performance, thereby enhancing operational efficiency and guaranteeing safety. Moreover, to address the issue of actuator saturation, this paper utilizes a parameter adaptive strategy that maximizes the utilization of the actuator’s potential without resorting to overly cautious presumptions regarding controller limits. In our future work, we plan to apply and validate this control methodology in actual tower crane applications.

This paper addresses the problem of prescribed-time optimal formation control for nonlinear multi-agent systems (MAS) via reinforcement learning (RL). First, a new finite horizon performance cost function is constructed, which incorporates specified time and terminal constraints, thereby it is useful for embedding the prescribed convergence time performance into optimal control framework. Based on the cost function, RL-based optimal formation controller is subsequently devised under actor-critic structure, by incorporating terminal constraint errors into the critic update law, it ensures that the optimal value function can be approximated while satisfying terminal constraints. Then, the prescribed-time optimal formation controller is designed by integrating an adjustment function with the devised optimal formation controller, with which, the formation tracking errors are ensured to converge to the origin within specified time. The proposed approach provides some advantages such as system model information is not required, optimal performance is achieved with satisfied terminal constraints, and asymptotic rather than bounded prescribed-time formation control is achieved. Finally, the effectiveness of the proposed strategy is validated through simulation examples.

This article investigates the problem of global full-state prescribed performance control (GFSPPC) for uncertain nonlinear systems with dead-zone and event-triggered input. By incorporating a unique time-varying funnel function and embedding it into the state transformation function of each step, we present a coordinate transformation. Then, based on the low-complexity methodology, a novel prescribed performance control (PPC) algorithm is developed, which guarantees predefined transient and steady-state performance for both the tracking error and system states in a global sense. Moreover, with our proposed 1-bit-triggered mechanism, only one bit signal (either 0 or 1) is transmitted on the controller-actuator channel from beginning to end, which reduces the bit of data transmission while saving communication resources. The designed control scheme is inherently robust against model uncertainties, external disturbances and dead-zone nonlinearity without the use of the adaptive technique, filters and approximators. Besides, the strictly increasing functions outside the dead-band in existing works are extended to a non-differentiable and locally decreasing form in the considered dead-zone model. Finally, the proposed approach’s effectiveness is confirmed through simulations of the resistance-inductance-capacitance (RLC) circuit system and the robotic manipulator system, respectively.

This paper delves into the distributed consensus control problem of nonlinear multiagent systems under the influence of actuator deception attacks based on a fixed directed topology. Diverging from the existing research, we develop a new actuator deception attack model, where attack signals are generated by an unmodeled system satisfying the input-to-state stable condition, and the unmodeled system utilizes the output consensus error of the agents and its delayed error information as the system input. In this condition, we put forward a novel distributed output feedback consensus control approach. First, we design the distributed controller with a compensator for the follower by the use of the relevant outputs of the agents, which is independent of the time delay and the states of the unmodeled system. Then, by constructing a new Lyapunov function with an adjustable power parameter, we can regulate the range of the functions describing the false data injected into the actuator. Additionally, through the combination of the exchange supply function method, we establish a strict proof that all agents can achieve exponential leader-following full-state consensus driven by the given controller. Finally, a simulation example is presented to demonstrate the effectiveness of the developed approach.

This paper focuses on output feedback prescribed-time control problem of nonlinear system subject to bounded disturbance. Firstly, in order to observe and deal with unmeasurable states and disturbances, the prescribed-time observer and filter are constructed. The prescribed-time state stabilization problem of the original system is translated into how to design the controller to make the state of filter stable in a predetermined time. Then, to overcome the effect of unknown disturbance during the design process, a novel switched prescribed-time high-order sliding mode (HOSM) is constructed. The output feedback prescribed-time control scheme is presented in conjunction with the proposed prescribed-time HOSM to guarantee the prescribed-time stability of system. Finally, the effectiveness of the main result is demonstrated through two numerical simulations, a third order nonlinear system and the single-link robot system.

In this paper, a trajectory tracking control problem is investigated for a wheeled mobile robot (WMR) on uneven road. A mapping relationship is established between the velocity and position to describe the motion of the robot on uneven road. An adaptive kinematic controller (AKC) is designed to improve the trajectory tracking accuracy, where robot parameters and control gains are both estimated. Then, a fixed-time generalized variable gain extended state observer (VGESO) is proposed to estimate the states and disturbance caused by uneven road, where a balance is maintained between disturbance rejection and noise suppression. Next, a dynamic controller is put forward by combining fully actuated system (FAS) approach with practical prescribed time (PPT) control. It is analyzed that the velocity tracking error system is PPT stable. Finally, experimental results demonstrate effectiveness and superiority of the proposed method.

This paper proposes a novel filter-based model-free reinforcement learning (RL) event-triggered control (ETC) method for the fully distributed robust leaderless cooperative output regulation (COR) of unknown heterogeneous multi-agent systems (MASs) with external disturbances over directed graphs. First, the fully distributed event-triggered observers are designed to generate an autonomous system for the robust leaderless COR, in which the frequency of signal transmission and computational burden are significantly reduced, and the Zeno behavior is strictly ruled out. Then, a filter-based model-free RL algorithm without integration operation is developed to obtain the solution of the internal model-based augmented algebraic Riccati equation (AARE) and to release the requirement of recording complete and continuous data. Moreover, with some adaptive parameters, the robust leaderless COR is solved in a fully distributed manner without involving any global information of directed MASs. Finally, simulation results on RLC circuits are illustrated to show the feasibility and effectiveness of the proposed control scheme.

In this paper, the stabilization control problem of uncertain nonlinear systems with multiple unknown control directions is investigated. Firstly, through the coordinate transformation, the original uncertain nonlinear system with multiple unknown time-varying control coefficients is transformed into a simplified system without unknown control coefficients. Subsequently, a dynamic high-gain observer is employed to compensate for the uncertainties in the transformed system. Notably, the proposed integrated switching controller incorporates a candidate set for estimating the control direction, which only contains two elements, greatly shortening the switching process compared to traditional switching controllers. Through the integrated switching scheme, output feedback stabilization of the original system can be achieved despite the multiple unknown control directions. Ultimately, several numerical simulations of a DC motor electrical model are conducted to show the effectiveness of the presented integrated switching control strategy.

This article investigates fixed-time prescribed tracking control based on a command filter for a class of nonlinear systems with unknown control coefficients. A novel switching control mechanism is proposed to address the challenge of unknown control coefficients and introduce a dual-parameter switching strategy with online parameter adjustment based on the designed conditions. To address the limitation in existing research, where prescribed performance functions depend on the initial conditions of systems, this work designs a new class of prescribed performance functions that eliminates this dependency. A command-filter-based backstepping approach effectively avoids the computational complexity of high-order derivatives in traditional backstepping methods. In addition, the issue of the nondifferentiability of the virtual controller at switching moments in existing switching control methods has been resolved. Ultimately, the boundedness of all signals in the closed-loop system is ensured. Moreover, a simulation example of a second-order system verifies the effectiveness of the algorithm in this article.

The global asymptotic stabilization control algorithm is proposed for interconnected nonlinear systems utilizing intermittent output feedback. A dynamic double side event-triggered mechanism (ETM) is designed to make the available output intermittent, reducing the frequency of signal updates. In this case, we relax some restrictive conditions from related studies. The considered system features unknown time-varying parameters, mismatched uncertainties, and uncertain functions that satisfy nonlinear growth conditions. These complexities render the standard backstepping recursive design scheme inapplicable, as the derivative of the virtual controller does not exist. To address the intermittent output feedback problem, we introduce a novel dynamic backstepping control method. First, we establish a dynamic gain observer using the triggered output signals to reconstruct the unmeasurable state variables. Next, the concept of dynamic gain is introduced through a coordinate transformation, with its derivative employed to offset discontinuous terms, which solves the challenges in recursive backstepping design caused by intermittent output and regulates that the state variable converges asymptotically to the origin in the global sense. Final, the simulation example is proposed to show the validity of the developed algorithm.

This article explores the distributed leader-following full-state consensus control problem for feedforward nonlinear stochastic multiagent system with actuator deception attacks under a fixed directed topology. Unlike the existing works, we establish a novel actuator deception attack model, the attacks have higher stealthiness in model. The false injection information of the attack is generated by a stochastic inverse dynamics system satisfying the stochastic input-to-state stable condition, and the input of the system depends on the relevant output information of neighboring agents, rather than all state information. In this case, we develop a novel distributed output feedback consensus control algorithm to overcome the impact of attacks and stochastic disturbances. First, we design the distributed linear controller with a compensator for each follower based on the relevant outputs, and provide sufficient conditions for ensuring its effectiveness. Then, we construct a new candidate Lyapunov function with a power constant that is used to relax the constraints of the attack model. Subsequently, by means of the exchanging supply function approach, we strictly prove that all agents can achieve leader-following full-state bounded consensus in probability. Finally, an illustrative simulation is provided to showcase the efficacy of our methodology.

This article considers the design of prescribed-time sensor fault estimators (PSFEs) and unknown input compensation for linear systems, i.e., estimators that estimate the sensor fault and controllers that stabilize the state of the system both at a prescribed time. With this intention, a filter is introduced to eliminate the limitation of the fault being differentiable. Then, the task of designing PSFEs can be converted into the task of designing prescribed-time unknown input observers for the augmented systems. Next, the generalized inverse is employed to convert the augmented systems into a form suitable for observer design. Then, the PSFEs are developed by exploiting periodic delayed output. Both full- and reduced-order PSFEs are taken into account. In addition, based on the established state observer and unknown input estimation, periodic delayed controllers are designed to completely compensate for the unknown input so that the closed system is T-prescribed-time stable (T-PS). Finally, an example is provided to demonstrate the efficacy of the proposed methods.

The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) decoding models, especially those based on deep learning, predominantly focus on global spatial features, neglecting valuable local information, potentially leading to suboptimal performance. Therefore, this study proposed a dynamic hierarchical convolutional attention network (DH-CAN) that comprehensively learned discriminative information from both global and local spatial domains, as well as from time-frequency domains in EEG signals. Specifically, a multiscale convolutional block was designed to dynamically capture time-frequency information. The channels of EEG signals were mapped to different brain regions based on motor imagery neural activity patterns. The spatial features, both global and local, were then hierarchically extracted to fully exploit the discriminative information. Furthermore, regional connectivity was established using a graph attention network, incorporating it into the local spatial features. Particularly, this study shared network parameters between symmetrical brain regions to better capture asymmetrical motor imagery patterns. Finally, the learned multilevel features were integrated through a high-level fusion layer. Extensive experimental results on two datasets demonstrated that the proposed model performed excellently across multiple evaluation metrics, exceeding existing benchmark methods. These findings suggested that the proposed model offered a novel perspective for EEG decoding research.

In this article, an adaptive prescribed-time neural controller is developed for the tracking problem of a class of high-order nonlinear systems with full-state constraints. First, a prescribed-time bounded stability criterion is designed. Then, to handle the "explosion of complexity" problem of the backstepping method, an adaptive prescribed-time filter is constructed, in which the filter error is prescribed-time stable. Compared with existing methods, the newly designed transformation approach can accommodate a broader range of state constraint types. Then, the unknown nonlinear function is handled by radial basis function neural networks (RBFNNs). The adaptive prescribed-time neural control scheme is developed based on above. It can guarantee that the closed-loop system achieves the prescribed-time stability, and all states do not transgress the constraints. To demonstrate the effectiveness of the control strategy, comparative simulations are provided at the end.

The adaptive event-triggered control problem for a class of uncertain high-order pure feedback nonlinear systems (HOPFNSs) is considered. Different from the traditional backstepping method, a new high-order backstepping method is proposed based on the high-order fully actuated (HOFA) system approaches to design the adaptive event-triggered control law, which has the significant advantages of simple structure, high degree of freedom, and easy to realize. The high-order backstepping method does not need to transform the HOPFNSs into the first-order systems, which is more efficient and significantly reduces design complexity. It is proved that the adaptive event-triggered controller makes all the signals of the system bounded and save the energy in signal transmission. A simulation example is performed to verify the effectiveness of the control strategy.

This article investigates the problem of unified output constraints for a class of uncertain interconnected nonlinear systems, where the measurement of system states is affected by unknown drifts in the powers of the measurement functions. Compared to previous works on output constraints, the main challenge addressed in this article is the unavailability of the true system states during the controller design process and the nondifferentiability of the sensor's output functions. To achieve the control objectives, the following control scheme is proposed in this study. First, a novel barrier Lyapunov function is introduced, which is specifically designed to handle systems with unknown measurement drifts. This function can be uniformly applied to satisfy both scenarios of systems with or without output constraints. Second, the adding a power integrator (AAPI) technique and dynamic surface control (DSC) techniques are enhanced to effectively handle the unknown measurement drifts and avoid singularity problems in the controller design. The decentralized controller proposed in this article can realize that the outputs are strictly constrained within predefined boundaries and guarantees convergence of all system states to an arbitrarily small neighborhood. Finally, we provide two simulation examples to validate the effectiveness of our proposed control strategy.

In this article, we address the improved error-constrained control problem for unknown, strongly interconnected time-delay nonlinear systems with input saturation and conflicted output constraints. The further challenge we face is that the presence of discontinuous reference signals poses greater difficulties for control design. To tackle these issues, a mechanism for generating smooth, safe reference signals is first devised. Additionally, we propose a novel approach that utilizes improved prescribed performance functions to confine tracking errors within predetermined constant bounds in finite time, while avoiding potential singularity issues arising from abrupt changes in the reference signal. Furthermore, a decentralized adaptive learning error-constrained control strategy is proposed, employing neural networks to approximate complex uncertainties with an asymptotic dynamic surface control method. Stability analysis confirms that the proposed control scheme guarantees the asymptotic stability of the system and ensures safe tracking within conflicted irregular output constraints, even in the presence of input saturation. Finally, simulation results demonstrate the efficacy of the presented control strategy.

In this paper, a novel prescribed-time composite learning-enhanced fuzzy (PrTCLF) cooperative control approach is proposed for the multiple-master/multiple-slave(MMMS) teleoperation systems in presence of system model uncertainties and external interferences. Firstly, compared with traditional MMMS systems, a unifying virtual master-slave teleoperation control framework, notably applicative for more general situations where the number of master and slave robots is not the same, is constructed by introducing scheduled control authority for different operators, especially in cooperative control mode. A significant feature of this paper is that the first result of a novel time-dependent function integrated PrTCLF learning law rendering all the synchronization errors of uncertain MMMS teleoperation system to zero is creatively derived, by which the effect of traditional fuzzy learning error on the precision of system convergence is essentially solved. Meanwhile, in order to ensure the high efficiency and robustness of cooperative work, a new class of nonsingular prescribed-time terminal sliding mode (PrTTSM) surface is designed without any switching behavior. Besides, the sufficient conditions for maintaining the prescribed-time stability of the MMMS teleoperation system are provided through systematic Lyapunov stability analysis. Finally, the effectiveness of the control structure and algorithm is verified by a large number of simulation and experimental results.

Underwater three-dimensional (3D) reconstruction is of great significance for underwater structure detection. However, ocean current disturbance and color distortion pose significant challenges in achieving reliable 3D reconstruction of underwater scenes. This paper investigates 3D reconstruction based on movable stereo camera via an underwater teleoperator. Specifically, a vision system based on ORB-SLAM3 is constructed to calculate the depth maps and perform dense 3D reconstruction through real-time video frames. An improved Multi-Scale Retinex with Color Restoration-Color Correction (MSRCR-CC) algorithm is proposed to address color distortion in underwater image enhancement. A redundant point removal algorithm is designed for the point cloud mapping process, effectively reducing the system's computational burden. Furthermore, to ensure stable scanning and obtain a high-quality original video stream in disturbed underwater environments, a disturbance observer-based prescribed performance control scheme is proposed for the underwater teleoperation system. The proposed control strategy guarantees that the master- slave synchronization error is bounded within predefined boundaries, which can enhance the fast fourier transform response value of the original image, and thus improve the 3D reconstruction quality. A global prescribed performance function is applied, which removes the initial value restriction on synchronization errors. Finally, simulation and experimental results are provided to illustrate the effectiveness of the proposed methods.

Control design for nonstrict feedback nonlinear systems (NSFNS) with nonlower triangular structure may encounter algebraic loop problem, which is a significant, challenging, yet complicate issue to develop a controller with unknown control directions, especially in the presence of external disturbances and time-varying parameters. To solve these issues, unlike existing studies on prescribed-time stability under unknown control directions, a novel zero-error prescribed-time tracking control scheme is proposed for NSFNS based on the fuzzy logic approximation and adaptive technique. Moreover, a new switching event-triggered mechanism that focuses on triggering strategies and conditions to minimize resource wastage from continuous controller sampling is constructed. This approach effectively reduces the frequency of sampling and energy loss within the controller. In addition, the boundedness of the Lyapunov function is analyzed using invariant set theory. It demonstrates that the tracking error converges to zero within a user-defined time, which simultaneously ensures all signals of the closed-loop system be bounded and Zeno-free phenomenon. Finally, the efficacy of the proposed control algorithm is validated through numerical simulation results.

This article investigates the hybrid dynamic trajectory tracking control problem of the mobile robot via intermittent sampled-data (ISD) communication. In the dual-channel transmission of robot position signals and control signals, asynchronous and synchronous ISD communications are adopted to reduce network bandwidth usage and controller calculations while achieving satisfactory tracking performance, under which a hybrid dynamic tracking error model is established, including the jump and the flow domain. To achieve tracking control, a dynamic controller is employed with a dynamic parameter describing whether the signal is transmitted. Furthermore, the uniformly globally asymptotically stability (UGAS) condition under ISD communication is established for the hybrid dynamic tracking error system. Moreover, if the asynchronous ISD communication is degenerated into the synchronous ISD communication, a simplified dynamic controller and the simpler UGAS conditions are also given. Finally, practical experiments are provided for tracking control of the robot, which illustrate the validity for the theoretical results.

Multiplicative watermarking can enhance cyber attacks detection capacity of quantization-involved networked control system (QNCSs). This article presents an iteration-form solution to conventional summation-form multiplicative watermarking (SMW). First, limitation of conventional SMW for QNCSs is revealed, where there exists high design complexity whilst decreasing measurement perturbation from SMW and achieving successful replay attacks (RAs) detection. Second, a new iteration-form multiplicative watermarking (IMW) is designed by leveraging a keys transformation to reconstruct SMW with multiple coupled keys, where design complexity is greatly reduced and there are only dual decoupled keys to be designed. Furthermore, a positive correlation between value of one key and measurement perturbation from IMW is provided. Third, a positive correlation between ratio of the other key at different instants and RAs detection performance of IMW is explored by using residual covariance analysis. Finally, experimental results from networked inverted pendulum systems confirm the feasibility and effectiveness of new IMW.

This paper studies the Lyapunov-based adaptive neural network optimized tracking control problem for a class of unmanned fire fighting robots. Firstly, by reasonably simplifying the unmanned fire fighting robot (UFFR) and combining it with its actual working scene, a novel system model is created that takes into consideration both system uncertainties and external disturbances, including unknown friction factors and drag force. Then, the optimized tracking control scheme for the UFFR is devised by integrating both adaptive neural networks and the backstepping technique. The objective of introducing adaptive neural network technique is to overcome the challenge posed by solving the Hamilton-Jacobi-Bellman (HJB) equation. Based on Lyapunov stability theory, it is demonstrated that all signals in the closed-loop system are semi-globally ultimately bounded and the output variables follow the reference signals to the desired accuracy. In the end, to validate the effectiveness of our designed control scheme, numerical simulations and practical platform experiments have been conducted. To ensure repeatability, our codes are open sourced on Github: https://github.com/JiannanChen/RL-based-OBC-of-UFFR.git

This paper investigates the lateral and longitudinal platoon control problem under user-specified lane and inter-vehicle spacing constraints. By modeling in the Frenét Frame, the lateral and longitudinal movements of the vehicles are decomposed. A novel lateral control strategy is proposed to strictly enforce a preset lane departure accuracy by transforming lane constraints into heading angle constraints. In the longitudinal control, considering the presence of a non-ideal leading vehicle, a prescribed performance controller is designed to regulate its velocity. Additionally, a longitudinal control strategy based on a double-ended smooth transition function is proposed to mitigate the effects of non-zero initial errors and restore the standard constant time headway policy after a preset time. To guarantee practical fixed-time stability, a continuous variable exponent coefficient fixed-time sliding surface is constructed, and adaptive sliding mode controllers are designed. The effectiveness of the proposed method is validated through both simulations and experiments. The source code is available on GitHub (https://github.com/Mudianrui/FSC-VPuLLC.git) to support further research on lateral-longitudinal platoon control.

This article studies the issue of stability in memristor-based neural network (MNN) systems with time-varying delays. First, a novel matrix-separation Legendre inequality is proposed to achieve a tight hierarchical bound on augmented-type integral terms. To derive implementable inequality conditions, several delay-dependent matrices are introduced to eliminate the reciprocal terms associated with time-varying delay. Furthermore, a new Lyapunov-Krasovskii (L-K) functional is proposed by incorporating augmented-type double integrals and delay-product terms. A series of free-weighting matrices are introduced into the L-K functional, leveraging the zero-sum equations and the S-procedure pertaining to both the delay and its derivative. Based on the proposed matrix-separation Legendre inequality and L-K functional, the derived stability conditions exhibit reduced conservatism, as validated by three numerical cases and simulation results.

Service robots operating in unstructured environments must effectively recognize and segment unknown objects to enhance their functionality. Traditional supervised learning-based segmentation techniques require extensive annotated datasets, which are impractical for the diversity of objects encountered in real-world scenarios. Unseen object instance segmentation (UOIS) methods aim to address this by training models on synthetic data to generalize to novel objects, but they often suffer from the simulation-to-reality gap. This article proposes a novel approach (ZISVFM) for solving UOIS by leveraging the powerful zero-shot capability of the segment anything model (SAM) and explicit visual representations from a self-supervised vision transformer (ViT). The proposed framework operates in the following three stages: generating object-agnostic mask proposals from colorized depth images using SAM, refining these proposals using attention-based features from the self-supervised ViT to filter nonobject masks, and applying K-Medoids clustering to generate point prompts that guide SAM toward precise object segmentation. Experimental validation on two benchmark datasets and a self-collected dataset demonstrates the superior performance of ZISVFM in complex environments, including hierarchical settings such as cabinets, drawers, and handheld objects.

This article focuses on the fully distributed dual-event triggered leader-following consensus problem of heterogeneous multiagent systems (MASs) with unknown leader input and actuator fault. A hierarchical triggered control framework is developed for the MASs. First, in the upper network layer, event-triggered observers are designed to reconstruct the leader’s information by using the states exchanged intermittently among the neighboring observers. Then, in the lower physical layer, an adaptive fault-tolerant controller is designed, whose update instant can be directly computed based on the received upper layer information. This not only has the potential to further reduce the update frequency of controller, but also avoid the continuous monitoring on measurement errors. Besides, utilizing this control framework can prevent the faults from being propagated along the communication network. Finally, a numerical example is provided to verify the effectiveness of theoretical results.

This article addresses event-based adaptive control problems of a class of high-order nonlinear systems subject to unknown actuator failures. The number of failures and the specific failure modes are unknown. In order to save communication resources from the controller to the actuator, its event-trigger mechanism that ruled out Zeno phenomenon is proposed. Since the direction of system control is unknown, we develop a novel logic switching mechanism. Therefore, an adaptive switching controller is proposed, and the switching parameters in the controller can make timely updates based on the designed switching condition judgment. At the same time, to solve the measurement error caused by actuator failure and event-trigger mechanism, the control protocol is proposed, which ensures that the state of the system eventually converges to the origin and the global asymptotically stability can be achieved. Finally, the effectiveness of the proposed control design is verified by simulation of a simple system.

In complex and dynamic environments, achieving autonomous decision-making and control of agent remains a challenging task. Traditional reinforcement learning algorithms often struggle to effectively learn optimal policies when faced with high-dimensional state spaces, sparse rewards, and dynamic obstacles. This article proposes an enhanced deep deterministic policy gradient (DDPG) algorithm. First, we employ an artificial potential field method to pretrain the policy network, providing the reinforcement learning model with a safe initialization capability. This approach significantly reduces early-stage exploration risks and accelerates the convergence process. Furthermore, we integrate control barrier functions (CBFs) into the policy optimization to enhance the ability of dynamic obstacle avoidance, ensuring safety in complex environments. Additionally, we adopt staged rewards, potential-based rewards, and auxiliary rewards to overcome the sparse reward problem, providing the agent with richer and more effective learning signals. In the end, to validate the effectiveness of our designed control scheme, robot operating system Gazebo simulations and practical platform experiments have been conducted. To ensure repeatability, our codes are open sourced on the Github: https://github.com/zhn-ya/DRL.

This article concentrates on the event-triggered control problem for interconnected nonlinear systems under hybrid stochastic faults. Unlike existing results, our work considers both, sensor stochastic faults and process stochastic faults, which are referred to as hybrid stochastic faults. First, under hybrid stochastic faults, an observer is constructed to estimate all states, which converts the process stochastic faults into a processable form. Second, an event-triggered mechanism is proposed for the estimated states to reduce their update frequency. To deal with the problem that the triggered estimated states are nondifferentiable, a continuous controller with continuous estimated states is developed, and the event-triggered controller with triggered estimated states is established accordingly. By the use of Lyapunov stability theory, it can be rigorously shown that all signals of the closed-loop systems are bounded in probability. The proposed approach is illustrated with the simulation of two interconnected pendulums.

The incorporation of matrix polynomial terms in the Lyapunov-Krasovskii (L-K) functional presents new challenges for the delayed discrete-time systems. This article proposes a novel delay-cubed-dependent L-K functional to alleviate the conservatism of the stability criteria for the discrete-time systems with time-varying delay. A novel matrix convex method is proposed to cope with the nonaffine terms introduced by the delay-cubed-dependent functional. Furthermore, a generalized matrix convex method is developed to address the L-K functional with varying orders of delay. Then, the executable stability and stabilization constraints with less conservatism are derived-based the proposed delay-cubed-dependent L-K functional and the matrix convex method. Finally, three case studies are given to elucidate the efficacy of the obtained stability and stabilization method.

This article investigates the practical fixed-time optimal consensus problem for multiagent systems (MASs). Under the detailed balanced digraph, a controller with tangent function is constructed so that the MASs can reach consensus within a fixed time and then gradually converges to the global optimal solution on the basis of reaching stability. Besides, an important lemma about hyperbolic tangent function is derived to contribute to the stability analysis. In the process, an adaptive dynamic event-triggered mechanism (ADETM), including anti-cotangent function, is constructed to achieve the number of controller updates is significantly reduced. Eventually, the validity of the consensus control method proposed in this article is verified by a numerical simulation and a power system simulation.

To address the classification problem of short time-window steady-state visual evoked potentials (SSVEP), a novel deep-convolutional neural network (CNN) fused with residual squeeze and excitation blocks (RSEs) and multiscale convolutions is proposed. Given the difficulty in distinguishing frequency domain features of short time-window signals, AttentCNN-Multiscale begins with a filter bank (FB)-based time-domain feature extraction module. The FB comprises several sixth-order Butterworth filters with varying bandpass ranges. Then the feature tensors extracted by these filters are aggregated using a CNN with RSEs. For further feature learning, four 2-D CNNs and a multiscale convolution module are employed, with the final output generated through an adaptive fully connected layer. To demonstrate the effectiveness and superiority of AttentCNN-Multiscale, extensive experiments and comparisons are conducted on two large public datasets and our dataset. Additionally, a novel bionic intelligent soft gripper is designed and integrated with the proposed AttentCNN-Multiscale network to form a closed-loop system, enabling different grasping functionalities for various objects and demonstrating the application potential of the network in medical rehabilitation. To ensure reproducibility, the source code for AttentCNN-Multiscale is available on Github: https://github.com/raow923/AttentCNN-Multiscale.

This article explores the issue of event-triggered prescribed-time tracking control for uncertain nonlinear systems with unknown parameters and external disturbances. A novel adaptive event-triggered proportional–integral (PI) funnel control protocol is proposed, where the switching threshold event-triggered strategy is adopted to conserve communication resources while guaranteeing the expected control performance. To obtain the global results, a new method combining adaptive techniques with the design of prescribed functions with infinite initial values is presented. Compared with the existing schemes, a more concise PI-funnel control method is provided to realize the global prescribed-time tracking control performance, which avoids the external disturbance estimation and calculation of derivatives at each step of the traditional backstepping method. Finally, the effectiveness of the proposed method is illustrated through two simulation demonstrations.

The high-order fully actuated (HOFA) system approaches are utilized in this article to solve the synthesis problem of a class of generalized pseudo pure-feedback nonlinear control systems (PPFNCSs). Contrary to the traditional state space method, a recursive design approach is proposed to transform the generalized PPFNCSs into HOFA systems by adopting the idea of “ascending order and descending dimension.” In this framework, a class of generalized first-order, second-order, and mixed-order PPFNCSs are transformed into HOFA models based on the newly proposed generalized inverse function lemma, which overcomes the problem that traditional backstepping design methods are not applicable to such systems without any restrictions. Based on this, the linear time-invariant systems with the desired characteristic structure are derived by designing control strategies for the transformed HOFA systems, which not only achieves the desired control performance but also successfully avoids the problem of “explosion of complexity.” To demonstrate the effect of the approach, two examples are performed at final.

This paper focuses on the cooperative control problem of multi-agent systems subject to limited energy supply, unreliable communication and malicious attacks on devices. As the advancement of network technology, Denial-of-Services attacks targeting multi-agent systems are prevalent. To detect the valid Denial-of-Services attacks, this paper designs a novel detection mechanism, which based on the voltage levels of the transceiver and receiver. Due to the state information may be more difficult to measure, the reduced order dynamic gain k-filters are designed to reconstruct the states based on only the system outputs. Then, based on the detection mechanism and estimated states, event-triggered resilient consensus controller and switched control strategy are designed, such that the multi-agent systems achieve leader-following consensus with less consumption of energy and nice resilience to Denial-of-Services attacks. Finally, a simulation example is presented to demonstrate the effectiveness of the obtained theoretical results.

The teleoperation system of mobile manipulator has been widely applied for space exploration, medical assistance, and other fields. To improve operational efficiency, this paper presents a novel prescribed performance synchronization control scheme without applying any approximation functions for a class of teleoperation system of the mobile manipulator with time-varying delays and nonholonomic constraints. Compared with the general manipulator system, the mobile manipulator has a larger workspace and stronger operability. Yet, the heterogeneity of master-slave robots and the nonholonomic constraint of slave robot bring many difficulties to the control of the system. Firstly, a prescribed performance function (PPF) is designed to guarantee that the position synchronization errors are remained within the predetermined boundary and converge to a small preset area within a preset time. Then, the direct force feedback information is introduced into the controller to improve the transparency of the teleoperation. In addition, there are no approximation functions applied in the proposed controller, which can significantly reduce the complexity and computation of the system. Finally, simulation and experimental results are made to illustrate the availability of this method for practical application.Note to Practitioners—The motivation of this paper is to develop an algorithm to improve the steady-state and transient-state performance of mobile manipulator teleoperation system without increasing the complexity of the controller. Compared with the existing works, in this paper, the control algorithm without any approximators is proposed and a time-varying prescribed performance function is introduced, which promotes the steady-state and transient-state performance of the system essentially. At the same time, the introduction of force feedback information improves the transparency of the teleoperation system. The feasibility and effectiveness of the algorithm are verified on UR5e-Husky experimental platform. In the future, we will strive to further improve the transparency of the mobile manipulator teleoperation system in order to achieve the immersive effect of operator.

This paper investigates the dynamic event/self triggered full-state bipartite containment control for high-order nonlinear multi-agent systems (MASs) under switching topologies. Unlike existing related results, the developed scheme not only solves the full-state bipartite containment control problem, but also tolerates graph switching and even allows disconnection at certain time intervals. First, to estimate the state of the corresponding virtual leader, the high-gain compensator and observer are developed under the switching topology. Subsequently, by designing the auxiliary dynamic variables, a zeno-free dynamic event-triggered (DET) controller is designed to reduce the communication burden. Further, zeno-free dynamic self-triggered (DST) controllers are proposed to avoid continuously monitoring the states. It is proven that the controller under the DET and DST mechanisms can ensure the followers converge into the corresponding convex hull. Finally, the effectiveness of the proposed approaches is verified through the simulation of single-link manipulators and RLC circuits.

This work examines the distributed leader-following consensus problem of feedforward nonlinear delayed multiagent systems involving directed switching topologies. In contrast to the existing studies, we focus on time delays acting on the outputs of feedforward nonlinear systems, and we permit that the partial topology dissatisfy the directed spanning tree condition. In the cases, we present a novel output feedback-based general switched cascade compensation control method that addresses the above-mentioned problem. First, we put forward a distributed switched cascade compensator by introducing multiple equations, and we design the delay-dependent distributed output feedback controller with the compensator. Subsequently, when the control parameters-dependent linear matrix inequality is met and the switching signal of the topologies obeys a general switching law, we prove that the established controller can render that the follower's state asymptotically tracks the leader's state by employing an appropriate Lyapunov-Krasovskii functional. The given algorithm allows output delays to be arbitrarily large and increases the switching frequency of the topologies. A numerical simulation is presented to demonstrate the practicability of our proposed strategy.

This article focuses on the problem of adaptive event-triggered output feedback control for a class of uncertain nonlinear systems under the output constraint. Different from the existing works, the time-varying parameters and the global output constraint are taken into account. First, by means of multifilters, the unmeasurable state variables are reconstructed, under which the unknown time-varying parameters and sensor sensitivity are transformed into the estimation problem of unknown parameters. Second, based on a barrier function, a novel constraint algorithm is established to make the output enter into asymmetric time-varying constraint boundaries, which is independent of the initial value of the output. To avoid continuous sampling of the controller, an event-triggered mechanism is proposed without the Zeno phenomenon. By means of the Lyapunov stability theory, it is strictly proved that the output enters into the pregiven asymmetric constraint boundaries, and never exceeds. Finally, the validity of our proposed control algorithm is illustrated by a numerical simulation.

The fixed-time control problem is investigated for a class of uncertain nonlinear systems subjected to multiple unknown control directions. The control coefficients of nonlinear systems under consideration are time varying and their signs are not required to be identical. To tackle this challenge, a switching mechanism along with a novel dynamic boundary function is proposed. Utilizing the devised dynamic boundary function, adaptive parameters are introduced into the controller to effectively handle system uncertainties. It is proved that the system output converges to a small neighborhood of the origin in fixed time and the boundedness of all system signals is maintained. Finally, two simulation examples are used to show the validity of the presented switching control strategy.

This article is devoted to the fixed-time synchronous control for a class of uncertain flexible telerobotic systems. The presence of unknown joint flexible coupling, time-varying system uncertainties, and external disturbances makes the system different from those in the related works. First, the lumped system dynamics uncertainties and external disturbances are estimated successfully by designing a new composite adaptive neural networks (CANNs) learning law skillfully. Moreover, the fast-transient, satisfactory robustness, and high-precision position/force synchronization are also realized by design of fixed-time impedance control strategies. Furthermore, the "complexity explosion" issue triggered by traditional backstepping technology is averted efficiently via a novel fixed-time command filter and filter compensation signals. And then, sufficient conditions of system controller parameters and fixed-time stability are theoretically given by establishing the Lyapunov stability theorem. Besides, the absolute stability of the two-port networked system under complex transmission time delays is rigorously proved. Finally, simulations are performed with 2-link flexible telerobotic systems under two cases, results are presented to realistically verify the proposed control algorithm available.

This article investigates the problem of dynamic memory event-triggered (DMET) fixed-time tracking control within time-varying asymmetric constraints for nonaffine nonstrict-feedback uncertain nonlinear systems with unmodeled dynamics and unknown disturbances. The existing dynamic event-triggered control methods cannot handle the nonlinear systems with unmodeled dynamics and nonaffine inputs, which greatly limits the applicability of the strategy. To this end, a novel DMET adaptive fuzzy fixed-time control protocol is constructed based on the idea of command filtered backstepping, in which a new dynamic signal function is established to deal with the unmodeled dynamics and an improved DMET mechanism (DMETM) is designed to solve the problem of nonaffine inputs. It is proved that the newly DMET control strategy ensures the tracking error converges to an arbitrarily small compact set in a fixed time and all the signals of the closed-loop systems are bounded. The effectiveness of the proposed approach is demonstrated by two simulation examples.

This article studies the event-triggered cooperative output regulation problem of heterogeneous multiagent systems with external disturbances and unreliable communication link (i.e., packet losses occur intermittently). A novel hybrid event-triggering mechanism (ETM) is proposed, which imposes a strictly positive lower bound for triggering intervals, and an internal variable with jump dynamics is introduced to design triggering condition. A hybrid model is constructed to describe the closed-loop system with both flow and jump dynamics. Then, based on the hybrid model, Lyapunov-based consensus analysis, hybrid ETM design, and robust performance analysis results are developed. Compared with the existing results, the minimum triggering interval (MTI) can be prespecified, and Zeno behavior is ensured to be excluded no matter there exist disturbances or not, which is useful for control implementation. Besides, the packet losses are allowed to be nonidentical, that covers identical packet losses as a special case. Moreover, the tradeoff between MTI and the number of maximum-allowable successive packet losses is explicitly given. Finally, simulation results are provided to show the effectiveness of the proposed method.

This paper develops a novel hybrid system approach to address the event-triggered stabilization problem of largescale T-S fuzzy systems subject to variable long transmission delays. Various changing behaviours of deviation errors caused by transmission delays, as well as the changes of some auxiliary variables are modeled by a hybrid dynamical system with flow dynamics and jump dynamics. Then, under the hybrid control framework, an observer based anti-disturbance controller and a delay-independent hybrid event-triggering mechanism (HETM) are proposed to stabilize the closed-loop system, while guaranteeing desired performance in disturbance attenuation and delay tolerance. Compared with the case that small transmission delays could naturally lead to delay-independent HETM, the long transmission delays pose many difficulties. Given this, we propose a more robust triggering condition with only using several memorized previous states to guarantee the HETM to be delay-independent even in case with long transmission delays. Moreover, the obtained stability conditions avoid involving lower and upper bounds of transmission delays in matrix inequalities, thus can be delay-independent and significantly simplified. Relationships between system stability and maximally allowable transmission delay are transformed to designing proper auxiliary functions offline. Finally, the effectiveness of proposed control protocol is demonstrated by a numerical example.

The issue of event-based asymmetric predetermined performance control (PPC) has been addressed for$p$-normal nonlinear systems with time-varying unknown control coefficients. A set of Nussbaum functions is introduced, capable of addressing both single and multiple unknown control coefficients. To achieve asymmetric PPC, a switching constraint scheme is proposed. Subsequently, an adaptive fuzzy controller based on dynamic events is developed. Improved techniques for approximating the unknowns of a system using fuzzy logic systems, while dynamic events are employed to reduce the frequency of controller updates. Using Lyapunov stability theory, it is proven that all signals in the closed-loop system remain bounded, and the tracking error is confined within the specified asymmetric boundaries. Eventually, the effectiveness of the present scheme is demonstrated by the simulation of three examples.

This article presents an adaptive fuzzy output-feedback control method for a class of nonlinear time-delay systems with prescribed tracking performance. Based on proper tuning functions and a pair of monotone tube boundary functions, a novel error transformation is proposed, which can constrain the tracking error within predefined monotone tube boundaries provided that the transferred variable is bounded. To this end, an adaptive output-feedback controller is designed to stabilize the transferred variable. We use fuzzy logic systems and adaptive control techniques to approximate the unknown nonlinear functions, and a reduced-order observer with less dynamics is proposed to estimate the unmeasurable state. In order to reduce the computation burden, we adopt command-filters method, which can avoid the complexity explosion problem in backstepping controller design procedure. Our article presents a way to constrain the tracking error with improved performance indices, such as overshoot, and convergence time. In addition, the method is independent of the initial values. Numerical simulations are given to verify the effectiveness of the proposed approach.

This article devotes to propose a novel synchronization control algorithm for networked nonlinear master–slave system under a scheduled shared-control structure to skillfully solve the problems of easy fatigue caused for continuous operation on human operators in the existing immobilized teleoperation system. Specially, a novel composite fuzzy learning control (CFLC) law is innovatively designed by incorporating the current and past values of the learning data to more effectively compensate what the system uncertainties cause. Then, a new practical preassigned fixed-time nonsingular terminal sliding-mode (PFNTSM) control algorithm with the shared-control framework is subsequently established, which is beneficial to sustain the continuity, nonsingularity, and higher robustness to system uncertainties. A significant feature of this study is that a complete force-position synchronous tracking and transparency-lifting human–robot interaction are realized. Moreover, the absolute stability of the two-port networked system under complex transmission time delays is rigorously proved. Finally, compared experimental results are presented to realistically verify the proposed control theory available.

In this article, the problem of improved full-error constrained control for unknown interconnected nonlinear time-delay systems with input saturation is investigated. To address this issue, auxiliary systems and a filter are constructed to generate modified signals, which are added to the constraint boundaries to prevent full dynamic errors (tracking error and virtual errors) from violating flexible boundaries and causing singularity problems during input saturation or drastic changes in the reference signal. To enhance the dynamic performance of all system errors, the error-driven nonlinear feedback technique is applied to all transform errors, enabling them to possess self-tuning characteristics. Furthermore, a decentralized adaptive learning constrained control strategy is proposed by employing fuzzy logic systems with an incremental adaptive mechanism to approximate complex uncertainties. The stability analysis demonstrates that the proposed control scheme guarantees the semi-global uniform ultimate boundedness of all signals in the closed-loop system, and the full dynamic errors are constrained within self-adjustable performance boundaries in the presence of input saturation. Finally, simulation results demonstrate the efficacy of the presented control strategy.

In this article, an asynchronous piecewise continuous hybrid dynamic event-triggered mechanism (DETM) is proposed for wheeled mobile robots (WMRs) to cope with nonideal network environments. Unlike the previous DETM, the asynchronous piecewise continuous DETM depends on a piecewise continuous intervals related to the previous triggered durations, while maintaining the required control performance and improving communication efficiency. A hybrid dynamic model evolving from the WMR system is characterized to describe the triggered and un-triggered states, and a hybrid controller is designed with a dynamic parameter that indicate in real-time the data transfer situation to achieve the trajectory tracking control of the WMR. In addition, the uniformly globally asymptotically stability condition is obtained for the hybrid dynamic model under the proposed asynchronous piecewise continuous DETM. Finally, practical experiments including comparison results are provided for illustration of the provided methodology.

In this article, the predefined-performance (PP) trajectory tracking control problem is investigated for a class of nonholonomic mobile manipulator systems subject to modeling errors, system uncertainties, and external disturbances. Extended frompredefined-time stability,PP stabilityis developed for the first time. In the proposed PP control framework, a predefined-time segmented time-varying sliding mode (SM) without reaching time is constructed to ensure faster zero-error tracking at the predefined time. Then, a performance function related with the symbol of error is designed to assure that the system tracking errors converge to the predetermined boundary within the preset time without evident overshoot. In this way, thePP stabilityis realized. A significant feature of this article is that thePP stabilityis guaranteed theoretically and practically in the presence of system uncertainties without utilizing any approximation function. Finally, simulation and experiment comparisons among the proposed PP control and existing conventional finite/fixed-time control are conducted to demonstrate the superior performance of the proposed control scheme.

This article proposes the design and verification of a novel barrier function (BF)-based adaptive composite sliding mode control method dedicated to the multiple-inputs–multiple-outputs underactuated systems. The proposed scheme is constructed by a composite sliding mode surface along with dual adaptive parameters. The main advantage of this scheme is that the actuated and underactuated state errors are integrated on the same proportional-integral-differential sliding mode surface, which not only enhances the coupling between the actuated and underactuated states, but also introduces an integral term in the sliding mode surface to improve performance of the system. In addition, a novel adaptive scheme of BF is proposed, which ensures that the system is bounded to converge and the range of the boundary can be set artificially even though the upper bounds of the external disturbances and the nonlinear phenomenon of the actuator are unknown. Rigorous stability analysis, based on the Lyapunov function, demonstrates the convergence of the composite sliding mode surface as well as all error states within the system. Finally, the proposed control algorithm is validated for its effectiveness and practicality through experimental results obtained from a four-degrees of freedom tower crane system.

This paper considers the tracking control problem for unmanned fire fighting robots subject to both full-state constraints and input saturation. First, a system model is developed that incorporates system internal uncertainties, external disturbances, input saturation, and actuator faults. Then, to address the full-state constraint problem, the original constrained system is transformed into an equivalent unconstrained one by using a new state-dependent transformation function. In addition, to solve asymmetric time-varying constraints on the control input, another new transformation function is also designed. In the end, based on the transformed system, a novel adaptive control scheme is proposed utilizing the backstepping recursive method and first-order filters. It is demonstrated that all signals in the closed-loop system are semi-globally ultimately bounded, and the output variables accurately track the reference signals while satisfying both full-state constraints and input saturation. To validate the effectiveness of our designed control scheme, numerical simulations have been conducted. To ensure repeatability, our codes are open sourced on github: https://github.com/JiannanChen/ATControlUFFR-FullStateConstraintInputSaturation.git.

Due to energy constraint and reliability consideration, autonomous underwater vehicles (AUVs) have fewer independent actuators than their degrees of freedom (DOFs). Additionally, the position and velocity of AUV are sometimes limited due to physical constraints. The current solutions, such as Barrier Lyapunov Function (BLF) and Nonlinear State Dependent Function (NSDF), depend on the upper bounds of virtual controllers and dynamic surface control (DSC) technique. This paper develops a new trajectory tracking controller for underactuated AUV systems with state constraints. A quasi-linear relationship is established between the independent and dependent variables of the transformation function. The Nussbaum functions are employed to address algebraic loop problem, which avoids using the DSC and any approximator. Moreover, an auxiliary controller is introduced to deal with underactuation problem. The Lyapunov theory proves that the proposed controller can guarantee asymptotic tracking of desired trajectory while keeping the position and velocity of the AUV within their constrained bounds. The method can be extended to the nth-order parametric-strict-feedback nonlinear systems. Finally, both simulation and experimental results reveal that tracking performance can be guaranteed by the proposed control scheme.

The synchronization for a class of switched uncertain neural networks (NNs) with frequent asynchronism based on event-triggered control is researched in this article. Compared with existing works that require one switching during an inter-event interval, frequent switching is allowed in this article. By employing controller-mode-dependent Lyapunov-Krasovskii functionals (LKFs), we devise the control strategy to guarantee that the switched NNs can be synchronized. The proposed LKFs can make full use of system information. Using an improved integral inequality, some sufficient stability conditions formed by linear matrix inequalities (LMIs) are derived for the synchronization of switched uncertain NNs. Average dwell time (ADT) is obtained in the form of inequality that includes the maximum inter-event interval. In addition, the existence of lower bound of inter-event interval is discussed to avoid Zeno behavior. At last, the feasibility of the proposed method is proven by a numerical example.

This article addresses the synchronization issue for inertial neural networks (INNs) with heterogeneous time-varying delays and unbounded distributed delays, in which the state quantization is considered. First, by fully considering the delay and sampling time point information, a modified looped-functional is proposed for the synchronization error system. Compared with the existing Lyapunov–Krasovskii functional (LKF), the proposed functional contains the sawtooth structure term$\mathcal{V}_8(t)$and the time-varying terms$e_\mathit{x}(t-\beta\hbar(t))$and$e_\mathit{y}({t-\beta\hbar(t))}$. Then, the obtained constraints may be further relaxed. Based on the functional and integral inequality, less conservative synchronization criteria are derived as the basis of controller design. In addition, the required quantized sampled-data controller is proposed by solving a set of linear matrix inequalities. Finally, two numerical examples are given to show the effectiveness and superiority of the proposed scheme in this article.

This article investigates the distributed leader-following consensus for a class of nonlinear stochastic multiagent systems (MASs) under directed communication topology. In order to estimate unmeasured system states, a dynamic gain filter is designed for each control input with reduced filtering variables. Then, a novel reference generator is proposed, which plays a key role in relaxing the restriction on communication topology. Based on the reference generators and filters, a distributed output feedback consensus protocol is proposed by a recursive control design approach, which incorporates adaptive radial basis function (RBF) neural networks to approximate the unknown parameters and functions. Compared with existing works on stochastic MASs, the proposed approach can significantly reduce the number of dynamic variables in filters. Furthermore, the agents considered in this article are quite general with multiple uncertain/unmatched inputs and stochastic disturbance. Finally, a simulation example is given to demonstrate the effectiveness of our results.

This article addresses the security problem of tracking control for nonlinear multiagent systems against jamming attacks. It is assumed that the communication networks among agents are unreliable due to the existence of jamming attacks, and a Stackelberg game is introduced to depict the interaction process between multiagent systems and malicious jammer. First, the dynamic linearization model of the system is established by applying a pseudo-partial derivative method. Then, a novel model-free security adaptive control strategy is proposed, so that the multiagent systems can achieve bounded tracking control in the mathematical expectation sense in spite of jamming attacks. Furthermore, a fixed threshold event-triggered scheme is utilized to reduce communication cost. It is worth noting that the proposed methods only require the input and output information of the agents. Finally, the validity of the proposed methods is illustrated through two simulation examples.

This article studies the modeling and tracking control problems for a class of towed unmanned fire fighting robots. Considering that no similar modeling results exist, we take the lead in building a novel system model that takes into consideration both system uncertainties and external disturbances, including unknown friction factors and drag force. Then, to compensate for the adverse effects of system uncertainties and external disturbances, a novel robust control algorithm is proposed, which utilizes adaptive control and scaling techniques. Moreover, innovative predefined performance functions are designed to ensure that tracking processes meet predefined transient and steady-state requirements. Unlike most of the existing works, our predefined time performance function has the advantage that the convergence time and convergence accuracy can be arbitrarily changed. In the end, a novel robust adaptive control scheme with predefined time and precision tracking is designed using the backstepping recursive method. Based on Lyapunov stability theory, it is demonstrated that all signals in the closed-loop system are ultimately bounded, and both predefined transient and steady-state processes are never violated. To validate the effectiveness of this proposed control scheme, numerical simulations and practical platform experiments have been conducted. To ensure repeatability, our codes are open sourced on Github: https://github.com/JiannanChen/Modeling-and-Robust-Control-UFFR.git.

This article addresses the fully distributed (FD) output feedback full states consensus tracking problem for high-order multiagent systems with general nonlinearities under the directed graph. In each agent, a novel FD estimator is established to estimate the leader’s states, which only needs three variables’ information from each parent agent without considering their orders. Meanwhile, it is independent of the graph’s scale and does not rely on any global information. Then, instead of the backstepping method and based on the constructed compensator, an output-dependent dynamic gain method is used to design the FD output feedback protocol avoiding the repeated derivatives of the nonlinearities. Based on a new lemma, it is proved that using the proposed FD protocol, global full states consensus stability can be guaranteed and the consensus error can converge to zero asymptotically. The proposed method can not only achieve the consensus in FD fashion but also extremely relax the conditions on nonlinearities which satisfy the local Lipschitz condition with a more general incremental rate containing output and compensator states information. Finally, a numerical example is given to verify the effectiveness of the proposed method.

In this article, the adaptive backstepping control problem is investigated for a class of mismatched uncertain nonlinear systems with input and state quantization. All available states are generated by the static bounded quantizers, which can cause the failure of the recursive backstepping design. Previous results are based on linear-like virtual controllers to ensure that the partial derivatives of virtual controllers are constants, therefore, the systems are required to be an integral form or to meet matched conditions. Based on a dynamic gain approach, this article presents a new compensation mechanism to solve the difficulty of recursive backstepping design caused by discontinuous states, the control problem is transformed into a design problem of the dynamic variable. First, the dynamic variable is introduced based on a coordinate transformation, its derivative is used to compensate for discontinuous mismatched nonlinear terms. Then, with the help of the Lyapunov stability theorem, it is strictly proved that all signals of the closed-loop system are globally uniformly bounded. Finally, numerical simulations are provided to validate the effectiveness of the developed algorithm.

This article considers the prescribed performance tracking problem for a class of nonlinear interconnected time-delay systems, in which system states are unavailable and each subsystem is subject to unmodeled dynamics. Based on a novel performance function, the effect of initial values is eliminated in the prescribed performance method and the dynamic surface control method is employed to avoids the complexity explosion problem. With the reduced-order observer and fuzzy logic system, decentralized adaptive fuzzy output feedback control approach is proposed and the output feedback control strategy is further explored concerning global tracking performance constraint provided that the nonlinear time-delay functions are known or satisfy other mild assumptions. A numerical simulation is provided to substantiate our method.

A switching control scheme is presented for a class of nonlinear interconnected systems subjected to multiple unknown control directions. The unknown control coefficients considered in this article are time varying and have nonidentical signs, which makes the system model more generalizable. The switching controller proposed in this article is adopted to address the problem of unknown control directions, which solves the defects of the controller based on the Nussbaum gain technique. With the help of the proposed switching controller combined with dynamic gains, the boundedness of the system state and other signals is ensured for all time. Additionally, the system state can be regulated to zero by the proposed switching controller when the system is not affected by uncertainties and external disturbances. Finally, the effectiveness of the proposed switching control method is verified by simulation results.

This article concentrates on the problem of prescribed-time control for switched uncertain nonlinear systems with time-varying powers under arbitrary switching. The challenge is to drive all signals of the closed-loop system to the origin at any preselected time under the influence of time-varying unknown powers and unknown parameters. To deal with this problem, a novel adaptive prescribed-time stable criterion and selection scheme of the prescribed-time function are first proposed, which generalize the existing results and present the relationship with the time scale scaling method. On this basis, the common adaptive prescribed-time controller is constructed step by step for a class of switched nonlinear systems with time-varying powers and uncertainty, and the stability of closed-loop systems is subsequently analyzed. Finally, we provide two simulation examples to illustrate the validness of the control algorithm.

This article investigates decentralized adaptive control for a class of unknown parameter-varying interconnected time-delay nonlinear systems subject to asymmetric full-state constraints. By innovatively constructing an exponential scaling function on system states and an asymmetric integral barrier Lyapunov function (AIBLF), the states of the system can be directly constrained to predesigned bounds of exponential rate convergence. Based on the congelation of variables method, different unknown time-varying parameters of interconnected time-delay nonlinear systems with a mild requirement are well addressed, and the asymptotic stability of the whole system is proved with our proposed control strategy. Furthermore, the proposed decentralized adaptive controller guarantees that all system states can converge to zero with the exponential rate while maintaining within the asymmetric constraints. Finally, the validity of the presented control scheme is demonstrated by the simulation results.

This work studies event-triggered bipartite consensus problem of multi-agent systems (MAS) with structurally balanced signed graph. A hybrid system approach is proposed to deal with the leaderless and leader-following bipartite consensus problems in a unified framework, and a novel hybrid event-triggering mechanism (ETM) is proposed to reduce the usage of communication resources. By appropriately defining closed-loop states, a unified hybrid model is constructed for both leaderless and leader-following MAS. Then, stability analysis and ETM design results are given under hybrid systems framework. It is worth noting that the designed hybrid ETM is in decentralized form rather than distributed form. Moreover, the lower bound of triggering intervals can be pre-selected, i.e., Zeno-freeness is guaranteed, that is important for control implementation. The proposed method is applied to satellite formation system in simulation results to show the effectiveness.Note to Practitioners—This paper considers bipartite consensus control problem of MAS, which can be applied to some practical systems, e.g., bidirectional formation of unmanned air vehicles, since the control objective in these applications can be transformed into bipartite consensus control problem of MAS. Compared with the existing results, a hybrid system approach is proposed for consensus analysis of MAS with or without leader, and a novel event-triggering mechanism is proposed, which is more easier to be implemented, so, the proposed approach is more friendly for control engineers. The obtained results are applied to satellite formation control problem and the effectiveness is verified, and it is expected that the proposed approach can be extended to MAS with more realistic network-induced constraints and applied to more practical engineering systems.

This article studies the event-triggered output feedback consensus problem for a class of high-order uncertain nonlinear multiagent systems with totally unknown non-identical control directions. Firstly, the reduced-order filters are constructed by utilizing the dynamic gain technique to compensate the system uncertainties and unmeasurable states of the followers. Based on the filter states and output information, the asymptotic output consensus result is guaranteed with the developed distributed adaptive output feedback control protocol, while the inherent issue “complexity explosion” in backstepping design is avoided. Then, to solve the interaction of multiple Nussbaum functions in a single inequality, the Nussbaum functions with different frequencies are adopted, with which the unknown directions of multiagent system are no longer limited in identical. In addition, we propose a dynamic triggering strategy with time-varying threshold parameters to reduce the updates of controller without Zeno phenomenon. Finally, simulation results are provided to illustrate the effectiveness of theoretical algorithm.

In order to improve the efficiency of data transmission and save communication resources, the problems of double event-triggered control are investigated for a class of high-order nonlinear random systems. Under more general system conditions, in addition to overcoming the difficulty of recursive design caused by signal discontinuity, the effects of high-order nonlinearity and random disturbances also need to be addressed. Based on the adding power integral technique, a practical finite-time stable result is established for the nonlinear random systems under a double event-triggered mechanism (ETM) and proved that there is no Zeno phenomenon. Compared with the existing results, the update frequency of the signals is effectively reduced, and the upper bound of the stable error is independent of trigger parameters, thus can be made sufficiently small by tuning design parameters. Furthermore, the result is expanded to finite-time stabilization, state variables converge to the origin in a finite time. Finally, numerical simulations verify the effectiveness of the proposed algorithm.

This paper focuses on the adaptive output feedback control problem for nonlinear stochastic systems with unknown measurement sensitivity based on dynamic event-triggered mechanism (ETM). Different from the existing works, a novel adaptive output feedback control algorithm is proposed for unknown measurement sensitivity (its sign and bounds are unknown) by means of Nussbaum-type function. First, a reduce-order dynamic gain K-filter is proposed to reconstruct the unmeasurable state variable. Second, a tangent-type barrier Lyapunov function with a predefined-time performance function is established to constrain system output into the given region in a predefined time. Third, a dynamic ETM is put forward to reduce trigger times, and then the controller is designed accordingly. Based on the Lyapunov stability theory, it is proved that system state variables converge to zero in probability and other signals of the closed-loop system are bounded in probability. Finally, the validity of the proposed algorithm is demonstrated by the numerical simulation on a single-link manipulator.

In this paper, the distributed bipartite containment control problem for high-order nonlinear multi-agent systems (MASs) with time-varying powers is investigated under signed communication topology. Unlike existing bipartite containment control problems, the powers of the systems are considered to be unknown and time-varying, and the novel bipartite containment controller is proposed. First, the dynamic gain compensator and bipartite containment observer, which could estimate the corresponding virtual leader trajectory belonging to the convex hull spanned by multiple leaders and their symmetric ones, are developed for each follower. Subsequently, with the help of observer, a novel distributed bipartite containment controller is designed by using the power integrator technique and the backstepping method to ensure that the followers converge in the convex hull spanned by multiple leaders and their symmetric ones. Finally, the effectiveness of the bipartite containment scheme is verified by a simulation example.

Object recognition, one of the main goals of robot vision, is a vital prerequisite for service robots to perform domestic tasks. Thanks to the rich sense of information provided by RGB-D sensors, RGB-D-based object recognition has received increasing attention. However, the existing works focus on collaborative RGB and depth data for object recognition, while ignoring the influence of depth image quality on recognition performance. Moreover, in real-world scenarios, there are many objects with strong similarity from certain observation angles, which poses a challenge for the service robot to recognize objects accurately. In this paper, we propose CNN-TransNet, a novel end-to-end Transformer-based architecture with convolutional neural networks (CNNs) for RGB-D object recognition. In order to deal with the effect of high inter-class similarity, discriminative multi-modal feature representations are generated by learning and relating multi-modal features at multiple levels. Besides, we employ a multi-modal fusion and projection (MMFP) module to reweight the contribution of each modality to address the problem of poor-quality depth image. Our proposed approach achieves state-of-the-art performance on three datasets (including Washington RGB-D Object Dataset, JHUIT-50, and Object Clutter Indoor Dataset), with accuracy of 95.4%, 98.1%, and 94.7%, respectively. The results demonstrate the effectiveness and superiority of the proposed model in RGB-D object recognition task.

In this article, the iterative learning averaging consensus problem is studied for multiagent systems with system uncertainties, actuator faults, and binary-valued communications. Considering only binary-valued measurement information with stochastic noise can be received from its neighbors for each agent, a new two-iteration-scale framework that alternates estimation and control is designed. Under the proposed framework, each agent estimates the neighbors' states based on the empirical measurement method during a dwell iteration interval, during which each agent's states will keep constant along the iteration axis. Further, in view of the impacts of system uncertainties and actuator faults, a novel adaptive iterative learning fault-tolerant averaging consensus control scheme is designed based on its own states and the estimated neighbors' states. Finally, the resulting closed-loop system is rigorously proved to be stable, and numerical simulations are conducted to demonstrate the effectiveness of the developed control strategy.

This article studies the global prescribed-time stabilization problem for a class of time-delay nonlinear systems with uncertain parameters. First, we design two time-varying gains with special properties, in which one is introduced into virtual controllers to achieve prescribed-time convergence and the other one is used to construct the Lyapunov-Krasovskii (L-K) functional and Lyapunov function to handle the nonlinear time-delay term and unknown parameters, respectively. Then, by utilizing double time-varying gains and the scaling-free backstepping design approach, a dynamic state feedback controller is constructed, which guarantees that all state variables reach zero within a prescribed time, and the prescribed time can be specified in advance. Then, based on new functionals and regular differential inequality, we figure out the explicit expression for the upper bound of all variables, which plays an important role in proving the boundedness of all system variables. Final, a simulation example is given to demonstrate the effectiveness of the proposed method.

This article studies the distributed adaptive failures compensation output-feedback consensus for a class of nonlinear multiagent systems (MASs) with multiactuator failures allowing unmatched redundancy under directed switching graphs. With estimated information of neighbors, a novel distributed reference generator is designed. To compensate the unmeasured state variables of each agent, a reduced-order dynamic gain filter is constructed. Based on the generator and filter, and using the recursive design method, a distributed adaptive protocol is designed, where the adaptive technique is used to compensate the actuator failures. The proposed scheme can significantly relax conditions on the communication graph, which allows the graph to be disconnected at any time instant. The number of introduced variables in the filter and its dimension is greatly reduced and, thus, reduces the numerical challenge. The output-feedback consensus for nonlinear MASs with actuator failures and possible unmatched actuator redundancy is addressed for the first time. The consensus error can converge to an arbitrarily small set not affected by actuator failures, and the resulting closed-loop system is semiglobally stable. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

In this article, we consider the input-to-state stability (ISS) problem for a class of time-delay systems with intermittent large delays, which may cause the invalidation of traditional delay-dependent stability criteria. The topic of this article features that it proposes a novel kind of stability criterion for time-delay systems, which is delay dependent if the time delay is smaller than a prescribed allowable size. While if the time delay is larger than the allowable size, the ISS can be preserved as well provided that the large-delay periods satisfy the kind of duration condition. Different from existing results on similar topics, we present the main result based on a unified Lyapunov-Krasovskii function (LKF). In this way, the frequency restriction can be removed and the analysis complexity can be simplified. A numerical example is provided to verify the proposed results.

In this article, we consider the load frequency control problem for a class of power systems based on the dynamic event-triggered control (ETC) approach. The transmission networks are unreliable in the sense that malicious denial-of-service (DoS) attacks may arise in the power system. First, a model-based feedback controller is designed, which utilizes estimated states, and thus can compensate the error between plant states and the feedback data. Then, a dynamic event-triggered mechanism (DETM) is proposed by introducing an internal dynamic variable and a timer variable with jump dynamics. The proposed (DETM) can exclude Zeno behavior by regularizing a prescribed strictly positive triggering interval. Incorporated in the ETC scheme, a novel hybrid model is established to describe the flow and jump dynamics of the power system in the presence of DoS attacks. Based on the hybrid dynamic ETC scheme, the power system stability can be preserved if the attacks frequency and duration sustain within an explicit range. In addition, the explicit range is further maximized based on the measurement trigger-resetting property. Finally, a numerical example is presented to show the effectiveness of our results.

In this article, the output-feedback tracking control problem is considered for a class of nonlinear time-delay systems in a strict-feedback form. Based on a state observer with reduced order, a novel output-feedback control scheme is proposed using the backstepping approach, which is able to guarantee the system transient and steady-state performance within a prescribed region. Different from existing works on prescribed performance control (PPC), the present method can relax the restriction that the initial value must be given within a predefined region, say, PPC semiglobally. In the case that the upper bound functions for nonlinear time-delay functions are unknown, based on the approximate capacity of fuzzy-logic systems, an adaptive fuzzy approximation control strategy is proposed. When the upper bound functions are known in prior, or in a product form with unknown parameters and known functions, an output-feedback tracking controller is designed, under which the closed-loop signals are globally ultimately uniformly bounded, and tracking control with global prescribed performance can be achieved. Simulation results are given to substantiate our method.

The stability of Takagi–Sugeno (T–S) fuzzy systems with time-varying delay is investigated in this article. First of all, a novel Lyapunov–Krasovskii functional (LKF) is proposed by fully utilizing single integral polynomial-delay-product terms and membership-function-dependent matrices, where more delay information is considered. Second, by introducing negative integral estimation inequalities and polynomial inequality, the estimation gap of derivatives is further decreased. As consequence, the criterion with less conservatism is presented. Finally, the examples are utilized for verifying the validity of the stability approach.

This article investigates the stability and stabilization issues of Takagi–Sugeno (T–S) fuzzy systems with time-varying delay via a generalized reciprocally convex inequality (GRCI). The enhanced stability criteria with less conservatism are achieved by employing a novel GRCI and establishing an asymmetric Lyapunov–Krasovskii (L–K). The proposed reciprocally convex method encompasses some previous methods as exceptional examples by introducing a matrix-valued polynomial. Moreover, an asymmetric L–K functional with delay-product terms is proposed for the stability of the T–S fuzzy systems. Then, a stabilization method is produced based on the parallel distributed compensation. Finally, case studies are carried out to indicate the effectiveness and advantages of the stability and stabilization criteria.

This article studies the admissibility stability issue of Takagi–Sugeno fuzzy singular (TFS) systems with time-varying delay. First, negative-definiteness constraints for$n$th-order matrix-valued polynomials are established based on the Finsler's Lemma. A new reciprocally convex inequality (RCI) is developed according to the proposed negative-definiteness method. Then, an improved state decomposition Lyapunov–Krasovskii (L–K) functional is constructed based on the decomposed state method. The improved L–K functional is augmented by considering more information on TFS system states and the delay-dependent matrix. Based on the novel RCI and the L–K functional, a less conservative admissibility criterion is derived. Finally, a well-studied numerical example is performed to reveal the superiority and validity of the proposed admissibility criteria.

This article addresses the leader-following consensus problem of feedforward stochastic nonlinear multiagent systems with switching topologies. Output information for all agents, except for state information, can be acquired based on sensor measurement. Moreover, the stochastic disturbances from external unpredictable environments are considered on all agent systems with a feedforward structure. In these conditions, we propose a novel consensus scheme with a simple design procedure. First, for each follower, we construct a dynamic gain-based switched compensator using its output and its neighbor agents' outputs to provide feedback control signals. Then, for each follower, we develop a compensator-based distributed controller that is not directly associated with the topology switching signal such that it has a first derivative and antishake. Thereafter, by means of the Lyapunov stability theory, we verify that the leader-following consensus can be acquired asymptotically in probability under the controllers' action if the topology switching signal fulfills an average dwell time condition. Finally, the feasibility of the control algorithm is checked via numerical simulation.

In this article, the problem of adaptive decentralized control is investigated for a class of interconnected time-delay uncertain nonlinear systems with different unknown control directions and deferred asymmetric time-varying (DTV) full-state constraints. By constructing the novel time-varying asymmetric integral barrier Lyapunov function (TVAIBLF), the conservative limitation of constant integral barrier Lyapunov function (IBLF) or symmetric IBLF is reduced and the need on the prior knowledge of control gains is also avoided, while the deferred constraints directly imposed on the states of system are achieved by introducing the shifting function into the controller design. Furthermore, based on the Nussbaum-type functions, a new adaptive decentralized control strategy for interconnected time-delay nonlinear systems with subsystems having different control directions is proposed via backstepping method. And it is proven that the proposed control method can guarantee that all signals in closed-loop system are bounded and the transform errors asymptotically converge to zero. Finally, the effectiveness of the proposed control strategy is illustrated through the simulation results.

This article proposes a hybrid systems approach to address the sampled-data leaderless and leader-following bipartite consensus problems of multiagent systems (MAS) with communication delays. First, distributed asynchronous sampled-data bipartite consensus protocols are proposed based on estimators. Then, by introducing appropriate intermediate variables and internal auxiliary variables, a unified hybrid model, consisting of flow dynamics and jump dynamics, is constructed to describe the closed-loop dynamics of both leaderless and leader-following MAS. Based on this model, the leaderless and leader-following bipartite consensus is equivalent to stability of a hybrid system, and Lyapunov-based stability results are then developed under hybrid systems framework. With the proposed method, explicit upper bounds of sampling periods and communication delays can be calculated. Finally, simulation examples are given to show the effectiveness.

This article focuses on hybrid dynamic event-triggered security tracking control of wheeled mobile robots (WMRs) against round-trip stochastic denial of service (DoS) attacks. To characterize the random properties, DoS attacks are modulated as Markov switched processes. A hybrid dynamic model is characterized and a hybrid dynamic event-triggering mechanism (ETM) is proposed for the WMR, which achieve larger triggering intervals than the static one. To actively defend against the DoS attacks and achieve the trajectory tracking control, a hybrid dynamic controller is designed with a dynamic parameter that reflects the DoS attacks in real time. The uniformly globally asymptotically stability (UGAS) condition of the addressed hybrid dynamic WMR system is obtained. Practical experiments are provided for illustration of the provided theoretical results.

This article considers the tracking control problem for an uncertain feedback nonlinear system with asymmetric time-varying full-state constraints and unknown control directions. We propose a new low-computation full-state constrained robust control algorithm, that removes the feasibility conditions of virtual controllers and solves the unknown control direction problem without using the Nussbaum gain technique. By introducing nonlinear transformation functions, the original constrained systems are converted into new unconstrained tracking error systems, and the new systems eliminate the limitation of the initial conditions. Then, to seek the correct control directions, an orientation function with error conversion is constructed, which avoids introducing Nussbaum-type functions and logic switching rules. The proposed method possesses inherent robustness against model uncertainties and disturbances, and guarantees that the full-state constraints and the tracking error of systems enter into a prescribed set in a fixed time. Finally, simulation examples are presented to demonstrate the superiority and effectiveness of the developed control algorithm.

An exponential stable control scheme based on state transformation and Lyapunov functions is presented for a class of large-scale nonlinear time-delay interconnected systems subjected to unknown control directions and unmodeled dynamics. By regulating the transformed state, the original system state is regulated to the origin at an exponential rate. By introducing a class of type-B Nussbaum functions, the obstacle caused by multiple unknown control coefficients is successfully overcome. Dynamic gains technique and changing supply function idea are established to tackle time-delay terms and unmodeled dynamics in the system. It turns out that the system state converges to the origin at a prescribed speed, and all the closed-loop signals remain bounded. Finally, simulation results show the effectiveness of the proposed methodology.

This article addresses the distributed consensus tracking problem for high-order nonlinear multiagent systems (MASs) with nonidentical output constraints under directed switching graphs. Such a practical and important issue has only been taken into limited consideration by the existing literature. In this article, the heterogeneous followers are considered to suffer from nonidentical output constraints, which take precedence over consensus tracking. To this end, a series of intermediate dynamic variables are introduced for each agent to isolate the effect of topologies switching and estimate the output of the leader. Then, distributed adaptive protocols are designed based on a novel constraint transformation. By using an improved mode-dependent average dwell-time lemma, sufficient conditions on switching graphs are given to achieve consensus tracking. Compared with existing results, the proposed scheme can not only guarantee output constraints but also significantly reduce the communication burden and tolerate a certain degree of graph switching. Finally, the proposed method is verified by the inverted pendulum simulation example.

This article investigates the distributed dynamic output-feedback bipartite consensus for a class of stochastic nonlinear multiagent systems (MASs) with time delays and actuators faults under directed switching graphs. First, a distributed extended state compensator is constructed for each agent to compensate for the consensus errors and actuators’ faults only using the output information of neighbors. Then, based on the compensator, a linear memoryless output-feedback controller is designed. Using a technical lemma and the stochastic Lyapunov stability theory, it is proved that combined with the given constraint conditions on graphs switching the 2nd-moment asymptotic bipartite consensus for the MASs can be achieved. Different from existing results, the proposed compensator can not only save the network bandwidth but also compensate for the actuators’ faults. The proposed scheme also significantly relaxes the conditions on the communication network to directed switching graphs and even allows the graph to be disconnected over some time intervals. The common design parameters under any graphs can avoid the detection of graph switching, which will need global topology information. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed method.

In this article, we investigate the consensus problem of multiagent systems (MASs) based on the event-triggered control (ETC) method. The communication between agents is transmitted through unreliable networks with denial-of-service (DoS) attacks. By equipping the MAS with estimation capabilities so as to estimate the lost measurements caused by DoS attacks, we present a hybrid dynamic event-triggered strategy (HDETS) for the consensus of MAS subject to unreliable networks. Based on the proposed HDETS, we can preserve the consensus of MASs provided that the DoS attacks satisfy a condition on its frequency and duration. In addition, it can guarantee the Zeno-freeness with a designed strictly positive minimum event-triggered interval as well. Furthermore, we revisit the estimator with the trigger-resetting property, and present a finite time resetting condition for the estimation error, which illustrates the maximal DoS attacks intensity that the MASs can tolerate. Finally, the developed results are verified by a spacecraft formation simulation example.

This article concerns with the adaptive fixed-time control problem for classes of nonlinear cascade systems with parametric uncertainty. The majority of existing finite/fixed-time control strategies for uncertain nonlinear systems can only make system states approach to the neighborhood of origin over a finite/fixed time. Different from these results, we put forward a time-varying gain-based control algorithm to ensure that all system states return to the origin in a fixed time. With the help of the backstepping method, a time-varying adaptive controller is designed for a high-order nonlinear systems subject to uncertain parameters. Through constructing appropriate dynamic gain-based Lyapunov functions and utilizing our proposed stability criterion, it is proved that all states of the uncertain system can be adjusted to the origin in a fixed-time interval. Moreover, the settling time is not depend on the initial conditions of system and can be preset according to the actual requirements. Finally, the simulation results are provided to illustrate the effectiveness of the developed dynamic time-varying feedback control algorithm.

It is arduous to determine the stability for amplidyne electrical systems (AESs) with time-varying delay. In this article, a novel approach with two independent tuning parameters is established to obtain the stability conditions for AESs with time-varying delay. The new method could optimize negative determination conditions by tuning two tuning parameters without producing extra decision variables. Some existing lemmas commonly employed in the literature are contained in the proposed method as exceptional cases. Then, an improved Lyapunov–Krasovskii (L–K) functional is proposed in which more delay and state information are employed. Based on the novel quadratic function negative-definiteness method and the proposed L–K functional, new delay-dependent stability and stabilization criteria are achieved for AESs with time-varying delay. Finally, numerical examples and simulation results are conducted to show the advantages of the proposed approach.

In this article, a fast fixed-time filter (FFTF)-based-backstepping control system is proposed for a class of uncertain high-order nonlinear systems with full-state constraints. To effectively tackle the lumped uncertainty, a dynamic gain fixed-time disturbance observer (DGFDO) is constructed to estimate the uncertainty, where the observer parameters are dynamically tuned and the estimation error converges to zero in fixed time. In order to solve the full-state constraints problem, the state transformation technique is employed with avoiding “feasibility conditions.” For overcoming the “explosion of complexity” problem of the backstepping method, an FFTF is constructed, where the filter error can be ensured to achieve fixed-time stability. In the light of Lyapunov theory, it is proved that all signals of the closed-loop system are bounded, all states are kept in their constraints, and output error converges to an arbitrarily small neighborhood around zero in fixed time. Finally, simulation results are demonstrated to verify the effectiveness of the theoretical results.

This article addresses the event-triggered consensus problem of multiagent systems (MASs) with packet losses. The packet losses are not restricted to be identical, and a hybrid systems framework is proposed to handle the considered problem with or without leader in a unified framework. Distributed control law is constructed based on an estimator, whose dynamics are related to whether packet losses occur. A novel hybrid event-triggering mechanism (ETM) is proposed, and a lower bound of the interevent times is enforced to guarantee Zeno-freeness. For stability analysis, a unified hybrid model is constructed to describe the dynamics of MAS with or without leader. In accordance to this model, how to design the hybrid ETM and consensus analysis are formally developed, and the maximum allowable successive packet losses can be explicitly computed. Moreover, even if there exist nonlinearities and disturbances in system dynamics, and the leader has nonzero input, the proposed method can still be applied. Finally, examples are provided to illustrate the superiority of the proposed method.

In this paper, global event-triggered adaptive prescribed tracking output feedback control is investigated for a class of nonlinear uncertain systems with unknown control direction. Different from the existing works, the design of the tracking error-dependent normalized function and prescribed boundary is based on our uniformly defined blow-up function rather than a specific function, and the asymmetric constraint requirements on tracking error can be achieved by appropriately selecting the blow-up function. By utilizing the Kalman filter ($K$-filter) and dynamic gain technique, a new reduce-order observer is proposed, which can make the estimated error exponentially converge to zero. In addition, by introducing a dynamic signal into the trigger condition, a novel event-triggered mechanism is proposed, which can extend the trigger time interval and completely counteract the event-triggered errors in terms of asymptotic stability by sacrificing part of the system transient performance. Furthermore, an adaptive controller is designed based on the backstepping method, which ensures the boundedness of the state of the closed-loop system, while regulating the tracking error meets the global prescribed performance requirements and approaches to zero asymptotically. Two examples are given to illustrate the effectiveness of the proposed control scheme.

This article studies the distributed average tracking (DAT) problem pertaining to a discrete-time linear time-invariant multiagent network, which is subject to, concurrently, input delays, random packet drops, and reference noise. The problem amounts to an integrated design of delay and a packet-drop-tolerant algorithm and determining the ultimate upper bound of the tracking error between agents' states and the average of the reference signals. The investigation is driven by the goal of devising a practically more attainable average tracking algorithm, thereby extending the existing work in the literature, which largely ignored the aforementioned uncertainties. For this purpose, a blend of techniques from Kalman filtering, multistage consensus filtering, and predictive control is employed, which gives rise to a simple yet comepelling DAT algorithm that is robust to the initialization error and allows the tradeoff between communication/computation cost and stationary-state tracking error. Due to the inherent coupling among different control components, convergence analysis is significantly challenging. Nevertheless, it is revealed that the allowable values of the algorithm parameters rely upon the maximal degree of an expected network, while the convergence speed depends upon the second smallest eigenvalue of the same network's topology. The effectiveness of the theoretical results is verified by a numerical example.

In this article, the problem of tracking control is considered for a class of uncertain strict-feedback nonlinear systems with deferred asymmetric time-varying full-state constraints. A novel adaptive robust full-state-constrained control scheme is developed. First, by introducing a novel shifting function, the original constrained system with any initial values is modified to a new constrained system, and the initial values of the modified constrained system remain 0. Then, to remove the feasibility condition caused by the barrier Lyapunov functions, the modified constrained system is further transformed into a new unconstrained system by a brand new nonlinear transformation. Furthermore, the tracking error system of the unconstrained system is constructed by using a new coordinate transformation, and a novel adaptive full-state-constrained control scheme is designed based on this error system through the backstepping recursion method and first-order filters. Finally, the resulting closed-loop system proves to be stable and numerical simulations are conducted to demonstrate the effectiveness of the developed control strategy.

This article is concerned with the fixed-time prescribed tracking control problem for the uncertain stochastic nonlinear systems subject to input quantization and unknown measurement sensitivity. Different from existing results, the sensitivity on the sensor for measuring the system state is considered as an unknown parameter instead of the known one. Due to unknown measurement sensitivity on the sensor, the real system state cannot be obtained by measurement; hence, we put forward a new feedback control algorithm by the use of the unreal measured value of the system state. Moreover, the fixed-time prescribed performance on the output tracking error is investigated by developing a novel performance function. By means of the backstepping method, an adaptive quantized controller is designed for the system. Based on the Lyapunov stability theory, it is proved that the controller can render the output tracking error that satisfies the fixed-time prescribed performance and all signals of the resulting closed-loop system are bounded in probability. Finally, simulation results are provided to illustrate the effectiveness of the proposed control algorithm.

The stability analysis problem is considered for multiarea load frequency control (LFC) systems with electric vehicles (EVs) and time delays. The novel linear operator inequality approach is proposed and a less conservative delay-dependent stability condition is achieved. First, the model of the multiarea LFC system with EVs and delays is expressed by the partial integral equation (PIE) at the first time. Then, a complete quadratic Lyapunov-Krasovskii functional is built in the form of an inner product of the linear partial integral (PI) operator. The novel stability criteria with less conservatism are proposed in the form of linear operator inequality. Moreover, the relationships between the delay margins and the controller parameters are shown. Finally, the simulations are conducted on both one-area and two-area LFC systems to show the effectiveness of the proposed approach.

This article considers the distributed output-feedback consensus control problem for nonlinear multiagent systems subject to input delays. Different from the existing related works, the input delay of each agent is described as an unknown time-varying function and is different from each other in this article. To deal with this problem, for each follower, we first construct a novel distributed observer based on the relative output information to asymptotically estimate the state information of the leader, then we introduce a classical observer to asymptotically estimate the state information of the follower based on its output information. By means of two observers, the leader-following consensus problem is transformed into the stability problem of the nonlinear system with unknown input delays. Subsequently, the distributed controller independent of delays is proposed for each follower by the use of the truncated prediction method under some conditions. Based on the Lyapunov stability theory, it is strictly proved that the distributed controller can render all agents achieving consensus. Finally, the effectiveness of the theoretical results is illustrated on the basis of numerical simulations on a group of single-link manipulators.

This article investigates the stabilization control and stabilizing data-rate condition problems for networked control systems, which transmit signals from the sensor to the controller over the communication network with denial-of-service (DoS) attacks. Considering a class of DoS attacks that only constrain its frequency and duration, we aim to explore the constraint condition for stabilization and minimum stabilizing data rate of the networked control systems. The framework consists of two main parts. The first part considers the stabilizing control by the state-feedback approach under ideal bandwidth capacity. While the second part characterizes the average stabilizing data rate in terms of the eigenvalues of system matrix and DoS constraint functions to explicitly reveal the relationship between the attacks and the network bandwidth capacity. The stabilizing result is novel in the sense that the DoS-attack intensity, which is characterized by its frequency and duration, can vary for different time intervals. With this feature, the minimum average data-rate condition can vary for different time intervals according to the intensity of DoS attacks.

This article studies the guaranteed performance synchronization control problem for networked bilateral teleoperation systems with system uncertainties. The communication channel connecting the master and the slave is subject to asymmetrical varying time delays with unknown upper bounds. The first result on prescribed performance synchronization control for the bilateral teleoperation system under such a weak assumption on the communication time delays is provided. Moreover, a novel composite adaptive control algorithm is proposed under a much weaker interval-excitation (IE) condition. More specifically, parameter adaptive estimation accuracy and speed are quantificationally ensured by employing a composite technique. Therefore, both steady-state performance and transient-state performance are achieved for the position synchronization and parameter estimation with the proposed control strategy. The Lyapunov function and the multidimensional small-gain framework are utilized to derive system stability criteria. It demonstrates that the allowable maximal derivatives of the transmission delays can be easily computed with the given parameters of the control algorithm and the nonlinear performance functions. Finally, both simulation and experimental results are provided to demonstrate the feasibility and superiority of the proposed composite adaptive strategy.

This article develops a novel hybrid switching control (HSC) approach to the consensus of multiagent systems (MASs), and the objective is to further improve the transient consensus performance compared with a reset control approach in recent results. First, a hybrid switched controller is introduced, which consists of two working modes: 1) reset control mode and 2) gain mode. Through appropriate switching mechanism design, the controller output signal can be guaranteed to be continuous, and the input-output of the switched controller always has the same sign. Then, HSC is applied to the consensus of MAS, and a consensus protocol consisting of proportional control part and HSC part is proposed. By introducing binary-value variables, an appropriate closed-loop model is constructed, and the Lyapunov-based consensus analysis results are obtained. Finally, an example is provided to show the superiority of the proposed approach.

In this article, we investigate the observer-based event-triggered consensus problem of multiagent systems with disturbances. A reset observer consisting of a linear observer and reset element is proposed, the reset element endows the reset observer the ability to improve transient estimation performance compared with traditional linear observers. A hybrid dynamic event-triggering mechanism (ETM) is proposed, in which an internal timer variable is introduced to enforce a lower bound for the triggering intervals such that Zeno-free triggering can be guaranteed even in the presence of disturbances. Then, in order to describe the closed-loop system with both flow dynamics and jump dynamics, a hybrid model is constructed, based on which the Lyapunov-based consensus analysis and dynamic ETM design results are presented. In contrast with linear observer-based consensus protocols and the existing dynamic ETMs, the system performance can be improved and continuous communication between neighboring agents is not needed. Finally, a simulation example is provided to show the effectiveness of the proposed methods.

To reduce the influence of controller uncertainties and lower the computational burden of the system, a novel nonfragile sampled-data control scheme is proposed in this article for the Tagaki–Sugeno fuzzy system with time delay. First, an improved Lyapunov–Krasovskii functional is established with adjusted looped functional, which contains not only the current states, but the delayed states. Then, utilizing the Wirtinger inequality and improved single integral inequality, the new stability criteria are proposed with less conservativeness. By calculating the solution of linear matrix inequalities, the nonfragile controller parameters can be acquired. Furthermore, the sampled-data fuzzy controller leads to a good stable performance for the system, even under the possible gain fluctuations. Finally, three examples are given to verify the effectiveness and superiority of the proposed control scheme.

This article focuses on the problem of adaptive finite-time tracking control for nonlinear stochastic systems under asymmetric constraints based on dynamic event-triggering control. Different from the existing works, a novel adaptive tracking control algorithm is proposed with asymmetric time-varying constraints and dynamic event-triggering mechanism. First, to constrain the state variable within given time-varying boundaries, a novel predefined-time performance function is constructed. Second, a novel barrier function related to state variable is constructed, by means of which the state variable is directly constrained within the asymmetric time-varying boundaries without the virtual controller. In addition, by establishing a novel dynamic function, we propose a dynamic event-triggering mechanism, and then design controller accordingly, which can reduce computation burdens and save the network resources. By the aid of the Lyapunov stability theory, it is proved that the system tracking error converges to an adjustable bounded set in probability in a finite time and all state variables are successfully constrained into the asymmetric time-varying boundaries. Finally, the effectiveness of the proposed control algorithm is verified by a simulation example.

This article focuses on the leader-following consensus problem for a class of high-order uncertain nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks and actuator faults under the directed topology. To reduce the network communication bandwidth resource from the controller to the actuator and mitigate the adverse effect from DoS attacks, a novel event-triggered control strategy is proposed based on the reliable attack detection mechanism. The general attack detection mechanisms depend on the residuals between the values from the systems and observers. Different from the general attack detection mechanisms, a novel attack detection mechanism is proposed based on the logic relationship of voltage level signals which from the outputs of system components. Besides, the controller is designed based on the backstepping method and the controller can guarantee that the Zeno behavior is excluded. Furthermore, by using the Lyapunov stability theory, it proves that the controllers make the MASs achieve consensus. Eventually, a simulation example is presented to demonstrate the effectiveness of the proposed theoretical results.

The main method of modern ironmaking is blast furnace ironmaking, which is a very complex nonlinear dynamic process with complex physical-chemical coupling. The hot metal is the final product of blast furnace, and its silicon content not only reflects the quality of hot metal but also characterizes the operation status of the blast furnace, so its accurate prediction is very important for the operation of the blast furnace. Given the bottleneck problem in the application of the existing prediction model of hot metal silicon content in the blast furnace, this article proposed a novel data-driven modeling method. First, a nonlinear Takagi–Sugeno (T–S) fuzzy model is constructed for the hot metal silicon content to completely capture the nonlinear dynamics of the blast furnace process. Then, considering the doubts of blast furnace operators about the predicted results of the model, the Bayesian method is used to identify the consequent parameters of the fuzzy model to obtain the probability output, to present the credibility of the predicted results. Furthermore, to improve the robustness of the fuzzy model to the initial fuzzy rules, the sparse priori is adopted to construct a compact fuzzy model with strong generalization performance, in which the key fuzzy rules were screened out. In addition, two optimization methods are derived for each of the above models. Finally, the validity of the proposed methods is verified by the test of actual blast furnace data.

This article studies the distributed adaptive leader-following control for high-order time-varying nonlinear multiagent systems (MASs) with uncertain parameters. The state feedback protocol and output feedback protocol are proposed, respectively, to render all states consensus errors to converge to zero asymptotically. First, the hierarchical decomposition algorithm is used to construct a refreshed communication graph to address the mutual dependence problem of controllers. Then, by introducing a local neighborhood consensus errors-based transformation, the leader-following consensus problem is converted into the stabilization problem for the consensus error system. Using the backstepping method and tuning function technique, the distributed adaptive state feedback controller is designed to render all followers’ states to track the leader’s ones. Further, by constructing the reduced-order dynamic gain k-filter to estimate unmeasured states, a distributed adaptive output feedback controller is designed. In both controller design methods, the traditional Lipschitz condition need not be satisfied any more for all time-varying nonlinear functions, and different from most of the existing results on the high-order nonlinear MASs, full states consensus can be obtained. Finally, a general numerical example is given to illustrate the effectiveness of the proposed methods.

This article studies the joint localization and tracking issue for the autonomous underwater vehicle (AUV), with the constraints of asynchronous time clock in cyberchannels and model uncertainty in physical channels. More specifically, we develop a reinforcement learning (RL)-based asynchronous localization algorithm to localize the position of AUV, where the time clock of AUV is not required to be well synchronized with the real time. Based on the estimated position, a scalable sampling strategy called multivariate probabilistic collocation method with orthogonal fractional factorial design (M-PCM-OFFD) is employed to evaluate the time-varying uncertain model parameters of AUV. After that, an RL-based tracking controller is designed to drive AUV to the desired target point. Besides that, the performance analyses for the integration solution are also presented. Of note, the advantages of our solution are highlighted as: 1) the RL-based localization algorithm can avoid local optimal in traditional least-square methods; 2) the M-PCM-OFFD-based sampling strategy can address the model uncertainty and reduce the computational cost; and 3) the integration design of localization and tracking can reduce the communication energy consumption. Finally, simulation and experiment demonstrate that the proposed localization algorithm can effectively eliminate the impact of asynchronous clock, and more importantly, the integration of M-PCM-OFFD in the RL-based tracking controller can find accurate optimization solutions with limited computational costs.

In this article, we investigate the adaptive visual tracking problem for uncertain robotic system with time-varying depth, in the case of no image-space velocity information. Existing works dealing with this problem either assume the estimated time-varying depth is invertible or require the priori information of the system kinematics. This case potentially leads to the singularity of the control scheme and requirement for a high gain feedback. In this brief, a new image-space observer which does not involve the inversion of the estimated depth is proposed, upon which, we design an adaptive visual tracking controller without using the image-space velocity information. More importantly, the observer employs a passive image Jacobian matrix-based constant feedback instead of high gain feedback and the priori information is no longer demanded. Additionally, the proposed control scheme enjoys a desirable separation property for the robotic dynamics and kinematics which makes it applicable to most industrial/commercial robots having a closed torque control loop and only permitting the joint velocity command. By using the Lyapunov stability analysis, the asymptotical convergence of both image-space tracking errors and observation errors are obtained. Numerical simulations and implementation issues concerning the application to industrial/commercial robots are presented to verify the efficacy of the control scheme.

This paper concerns the predefined-time bipartite consensus control for uncertain nonlinear multiagent systems under a signed directed topology. All agents have high-order uncertain nonlinear dynamic characteristics satisfying a time-varying Lipschitz growth condition, and their partial state information is not available for measurement. In this case, we put forward a novel output-feedback-based predefined-time leader-following bipartite consensus control strategy. A predefined-time compensator for each follower is firstly constructed with a time-varying gain by utilizing its relative output information. Then, a novel linear-like output feedback predefined-time distributed control protocol is developed for each follower by means of the compensator. By making two artful state transitions, the bipartite consensus problem is reduced to a stabilization one of nonlinear systems. By means of the Lyapunov stability theorem, we strictly prove that the designed controllers can ensure that all agents realize bipartite consensus in predefined time. Finally, two simulation examples are given to validate the viability of the developed theoretical algorithm.

The dissipative stability problem for a class of Takagi-Sugeno (T-S) fuzzy systems with variable sampling control is the focus of this paper. The controller signals are assumed to transmit with a constant delay. Our aim is to design the sampled-data controller such that the T-S fuzzy system is globally asymptotically stable with a (Q,S,R) - γ -dissipative performance index. The stability is analyzed by using a novel piecewise Lyapunov-Krasovskii functional (LKF) together with a looped-functional and free-matrix-based (FMB) inequality method. First, several useful linear matrix inequality (LMI) conditions are derived to verify the dissipative stability of the T-S fuzzy system and then the controller gains matrices are expressed by resorting the LMI approach with the maximal-allowable upper bound (MAUB) of sampling periods. The proposed LMI conditions can be easily solved by using the MATLAB tool box. Finally, the numerical example of a truck-trailer system is considered and analyzed by the proposed scheme to illustrate the benefit and superiority.

This paper addresses the output feedback distributed containment control problem for a class of nonlinear stochastic multiagent systems under a fixed directed graph. Existing works usually design the containment control protocol using backstepping design method based on a conservative Lipschitz condition on nonlinear functions, which has a tedious control design procedure. In this paper, a new output feedback distributed containment control algorithm is proposed based on a novel dynamic compensator, which can not only simplify the control design procedure but also relax the condition on nonlinear terms. The proposed distributed containment protocol for each agent depends only on the agent output and the relative outputs of its neighbor agents, and can reduce the communication burden between the agents. Based on the Lyapunov stability theory, it is proved that the outputs of the followers are driven into the convex hull spanned by the outputs of the leaders with the proposed linear controller. Finally, the effectiveness of the theoretical results is illustrated by simulation examples.

This article studies the finite-time observer-based leader-following consensus problem for a class of nonlinear multiagent systems with nonuniform time-varying input delays. Existing works usually assume that input delays are the same constants and the input of the leader is available for each follower. In this article, we propose a new distributed consensus algorithm to relax the conservative condition. A novel finite-time distributed observer is designed for each follower, which can accurately estimate the state information of the leader in a setting time. By means of the observer, the distributed controller is proposed for each follower, and it depends only on the state information of the follower and the estimated-state information of its neighbor agents. Based on the Lyapunov stability theory, it is strictly proved that all agents can achieve a consensus. Finally, the effectiveness of the theoretical results is verified by numerical simulation on a group of single-link manipulators.

In this article, the event-triggered and self-triggered leader-following output-feedback control problems are investigated for a class of high-order stochastic nonlinear multiagent systems (MASs) under an undirected graph. First, using the high-gain method, the observer is designed to estimate the unmeasured state variables of the given nonlinear system. Then, by introducing an internal dynamic variable, a distributed Zeno-free dynamic event-triggered controller is constructed. Compared with the static event-triggering results, the interevent time of the proposed dynamic event-triggering mechanism is shown to be prolonged and, thus, the advantages of the event-triggered control approaches can be enhanced. Further, to avoid continuously monitoring the states, a Zeno-free self-triggering mechanism is given. It is shown that the expectations of the output tracking errors converge to an arbitrarily small set if the diffusion terms are different for all agents, and that the expectations of all state tracking errors converge to an arbitrarily small set if the diffusion terms are the same for all agents. Finally, simulation studies are given to illustrate the effectiveness of the proposed methods.

In the existing literature, reset control has been shown to have great potential to improve transient performance of linear systems, but reset-induced jump dynamics bring difficulties for stability analysis, especially under the network environment. In this article, we apply reset control to the consensus of multiagent systems (MASs) with event-triggered communication, and a novel reset mechanism (RM) and a hybrid event-triggering mechanism (ETM) are proposed with guaranteed Zeno-freeness. The state space is decomposed into multiple flow sets and jump sets according to the RM and ETM, and a hybrid model of the MASs is constructed such that the reset induced and event-trigger induced jump dynamics can be handled in a unified framework. Based on the hybrid model, a novel hybrid systems framework is presented for consensus analysis and co-design of RM and ETM. Finally, the proposed design is verified with a simulation example.

For complex and difficult-to-control blast furnace systems with hour-level delay, accurate prediction of molten iron quality plays a very important role in guaranteeing the stable and smooth operation. Recently, some data-driven multi-input multi-output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature, silicon content ([Si]), phosphorus content ([P]), and sulfur content ([S]). However, those data-driven MIMO models ignore the interindicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. Moreover, the above methods do not pay attention to the molten iron quality indicators missing issue, which often occurs on blast furnace. To address the above two issues, this article proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model by utilizing an output transfer matrix. In the novel method, the interindicator correlation was explicitly modeled by a low-rank learning of the correlation matrix that overcame the great challenge of jointly determining the fuzzy rules of the MIMO T-S model and the interindicator correlation. Moreover, a new complete complementary matrix can be obtained by the output transfer from the original incomplete matrix resulting from molten iron quality indicators missing issues. For the corresponding optimization problem, an effective alternating optimization algorithm is presented, and the convergence of the optimization algorithm is also rigorously proved. The validity of the proposed method is verified by comparison with some related methods on real blast furnace data.

This article investigates the dynamic event-triggeredH∞control problem for nonlinear networked control systems with unreliable communication channel, variable communication delays, and limited bandwidth. The nonlinear plant is represented by discrete-time polynomial fuzzy model. First, a decentralized dynamic event-triggered mechanism is proposed to determine whether the measured data are transmitted or not, and in order to exclude data collision caused by limited bandwidth, novel try-once-discard and flexible round-robin scheduling protocols are proposed to assign communication channel to certain sensor node. Then, Bernoulli distribution is employed to model the unreliable communication channel, and a new random sequence is developed to model the received data sequence under the effect of data losses and scheduling protocols. Furthermore, a discrete-time stochastic system model with both state and error delays is constructed, and sufficient conditions in the form of sum-of-squares are developed for the design ofH∞controllers such that the closed-loop system is stochastically stable and preserves guaranteedH∞performance. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed results.

In this article, we study the tracking problem for networked nonlinear discrete systems with actuator faults and dual-direction data dropouts. A novel adaptive fault-tolerant iterative learning model-free control strategy is designed. First, by utilizing the method called compact form dynamic linearization, the original nonlinear system model is transformed into an equivalent data-driven model, and the data model contains only one unknown parameter. Both the actuator fault and the system dynamics information are included in this parameter. Then, to model the physical processes of data dropout, a new mathematical relationship is constructed. Furthermore, an adaptive fault-tolerant iterative learning tracking control scheme is developed with only randomly received input/output data. Noting that the high learning rate or convergence rate is required in actual applications, a new varying parameter approach is designed to improve such rate. Finally, it is rigorously proved that the closed loop is stable in the sense of uniform ultimate boundedness, and numerical simulation results are conducted to validate the effectiveness of the designed control strategy.

In this paper, we address the consensus tracking problem for the multiagent system (MAS) based on a nonfragile memory sampled-data controller. Considering the effect of controller gain fluctuation and communication delay, a novel sampled-data control scheme with variable sampling interval is designed for each agent. By developing some new terms, an improved piecewise Lyapunov-Krasovskii functional (LKF) is constructed to take full advantage of characteristic about real sampling pattern. Furthermore, some relaxed matrices constructed in the LKF are not necessarily positive definite. Making full use of the LKF and free-matrix-based integral inequality, some sufficient criteria are developed to ensure the consistency of the MAS. Then, by solving a group of linear matrix inequalities with the maximal sampling interval, the desired sampled-data control gain matrix is obtained. Finally, the numerical example of a 5-agent system is given to illustrate the effectiveness of the proposed approach in this paper.

A new event-triggered iterative learning control method is proposed for handling the distributed containment control problem of model-free multiagent systems under a fixed directed graph. The designed controller merely uses the input and output signals, controlled model information is not required. At first, the unknown dynamic is transformed into the linearization model upon the base of pseudo partial derivative. Secondly, the novel distributed containment controller is proposed for each follower by use of iterative learning algorithm. Moreover, a new trigger mechanism is designed to save energy of the systems, such that the updating number of the proposed controller can be reduced greatly. Mathematical deduction shows that the controller can render the outputs of the followers converge to a convex hull formed by the outputs of leaders. Finally, simulation examples are given for verifying the significance of proposed method.

Most studies so far on trajectory tracking control of autonomous underwater vehicle (AUV) have assumed that the Euler angles are exactly known. However, the AUV inevitably suffers from external environmental disturbances which are driven by wind, density, and temperature gradients. The attitude transducers cannot derive accurate attitude information of the AUV. Additionally, the uncertain hydrodynamic parameters affect the stability of the system. Consequently, it is unknown whether tracking performance of the AUV can be guaranteed. In order to overcome these drawbacks, in this paper, a finite-time controller is developed by using the nonsingular fast terminal sliding mode control technique. A robust differentiator is proposed to estimate the external disturbances and uncertain parts. Simulations are performed to show that with the proposed control laws, the AUV converges to the desired trajectory even in the presence of external disturbances and system uncertainty.

This article investigates the consensus problem of multiagent systems (MASs) with the reset control approach. Reset control has been shown to possess great potential to improve transient system performance, but reset-induced state jump dynamics bring difficulties for consensus analysis, especially under the network environment. In this article, a novel reset element is developed, and time regularization is utilized to exclude Zeno behavior. The case with continuous communication is first considered, a distributed proportional integral + reset consensus protocol is proposed, which consists of the proportional-integral controller and the reset mechanism. The results are further generalized to the case with asynchronous sampling, which relaxes the requirement for continuous communication and clock synchronization. A hybrid model of the MAS is constructed to describe the system dynamics, and a hybrid systems approach is proposed to handle the reset- and sampling-induced jump dynamics in a unified framework. Moreover, a novel Lyapunov function is proposed and LMI-based stability conditions are given. Finally, an example is provided to show the effectiveness of the proposed methods.

In this paper, the distributed output feedback leader-following control is investigated for high-order nonlinear multiagent systems (MASs) using the dynamic gain method. The linear-like distributed output feedback controller is designed without using the recursive method to overcome the "explosion of complexity" problem and further relax the conditions on the nonlinear functions of the MASs. First, the distributed reduced order dynamic gain observer is constructed for the i th agent to estimate its unmeasured state variables, in which the output information of its neighbors are used. Second, the linear-like output feedback controller is designed such that the outputs of the followers track the leader's output, and the tracking error could be arbitrarily small. Finally, the simulation examples are given to illustrate the effectiveness of the proposed method.

This paper studies the problem of decentralized adaptive output feedback fault detection and control for a class of uncertain nonlinear interconnected systems. The K-filters are designed to estimate the unmeasured state variables of the system. Moreover, the built-in noise dampening filters are introduced to attenuate the influence caused by the measurement noises. Then the fault detection scheme is proposed by designing the residual and threshold signals. Subsequently, by using the backstepping design method, the decentralized switched control strategies are proposed with the help of the neural network approximation technique. Based on the Lyapunov stability theory, it is proved strictly that all signals of the resulting closed-loop system are bounded. Finally, a simulation example is presented to verify the effectiveness of the theoretical result.

This paper investigates the leader-following consensus problem for multiagent systems (MASs) with communication delays. A novel hybrid event-triggered control scheme is developed and a hybrid system approach is proposed to design the event-triggering condition. Meanwhile, by means of temporal regularization, a strictly positive lower bound on the interevent times can be guaranteed, that is, Zeno-freeness can be guaranteed. The MASs are first described as a closed-loop system with both flow dynamics and jump dynamics, where the jump dynamics is induced from the triggering events and communication delays. Then, a hybrid model of the MASs is constructed under a hybrid systems framework. Based on this hybrid model, how to construct the Lyapunov function is given and the event-triggering condition design is also developed, such that the asymptotic consensus is achieved. Finally, an example is provided to show the effectiveness of the proposed approach.

This paper investigates the sampled-data stabilization problem for a Takagi-Sugeno (T-S) fuzzy system with time delay. By taking the information of states within the intervals from tkto t and t to tk+1into account, a new two-side delay-dependent looped-functional is introduced which can not only relax the monotonic constraint of Lyapunov-Krasovskii functional (LKF), but also make better use of the actual sampling pattern. Furthermore the sampled-data fuzzy controller is designed to contain both the present and delayed state information, thereby enhancing the control performance and design flexibility. Based on the novel augmented LKF and improved bounding technique, less conservative stability criteria are derived in the form of linear matrix inequalities. The superiority of proposed results is shown by two simulation examples.

This paper investigates the problem of the distributed adaptive output feedback leader-following consensus control for a class of high-order nonlinear multiagent systems with unknown parameters and nonlinear terms. First, the reduced order dynamic gain k-filters are built to estimate the unmeasured state variables. The bounds of the unknown parameters are estimated in order to avoid the over-estimation problem. Then, the dynamic surface control technique is used to design the distributed controller, and the computation load of the system is greatly reduced. Finally, a numerical example is shown to verify the effectiveness of the designed controller.

The adaptive neural network tracking control problem is investigated for a class of interconnected switched systems. The considered systems are with unmodeled dynamics, some of which do not satisfy the input-to-state stable (ISS) condition. By utilizing the neural network to approximate the composite unknown nonlinear functions, the corresponding decentralized tracking controller is designed for each subsystem with the help of dynamic surface control method. Some subsystems are stable with the designed controller, while other subsystems may not be stable because of non-ISS unmodeled dynamics, but they have some special properties with the designed controller. Then, a novel switching signal scheme is established such that the interconnected switched system is stable in the sense of semi-global boundedness, and the tracking errors can converge to predefined residual sets with prescribed performance index. Moreover, the switching scheme allows the number of switches to grow faster than traditional average dwell time method. Finally, a numerical example is provided to demonstrate the effectiveness of the presented results.

In this paper, a pricing mechanism is proposed for the electricity supply chain, which is consisting of one generation company (GC), multiple consumers, and competing utility companies (UCs). The UC participates in electricity supply chain management by a revenue sharing contract (RSC). In the electricity supply chain, the electricity real-time balance has an important role in the stable operation of the power system. Therefore, we introduce the demand response into the electricity supply chain to match supply with demand under forecast errors. Hence, we formulate a noncooperative game to characterize the interactions among the multiple competing UCs, which set the retail prices to maximize their profits. Besides, the UCs select their preferred contractual terms offered by the GC to maximize its profits and coordinate the electricity supply chain simultaneously. The existence and uniqueness of the Nash equilibrium (NE) are examined, and an iterative algorithm is developed to obtain the NE. Furthermore, we analyze the RSC that can coordinate the electricity supply chain and align the NE with the cooperative optimum under the RSC. Finally, numerical results demonstrate the superiority of the proposed model and the influence of market demand disruptions on the profits of the UCs, GC, and supply chain.

Most research so far in teleoperation control has assumed that all information is transmitted continuously. Unfortunately, the damaged and electromagnetic interfered line cause communication link failure. In addition, the unreliable link further leads to port data congestion. The data packet will be discarded when the buffer overflows. Consequently, it is unknown whether stability of the teleoperator could be guaranteed in the presence of intermittent communications. In order to overcome these drawbacks, in this paper, we provide a solution to the formation control problem of a single-master-multislave teleoperator in the situation where each robot is allowed to communicate with its neighbors only at some irregular discrete time instants. The relationship among control gains, topology, and maximum-allowable connected interval is presented. Simulations are performed to show the validity of our proposed approach.

This paper investigates the event-based leader-following consensus problem for high-order nonlinear multiagent systems whose dynamics are in strict feedback forms and satisfy Lipschitz condition. By using self-triggered control scheme and dynamic output feedback control method in combination, a new class of distributed self-triggered consensus protocols is proposed based only on the relative output measurements of neighboring agents. It is noted that the proposed protocols only require the output information of neighboring agents to be shared and the designed self-triggered algorithm can avoid continuous communication among neighboring agents, thus the communication cost is reduced significantly. Sufficient conditions in terms of matrix inequalities are derived to guarantee the exponential leader-following consensus. The effectiveness of the theoretical results is illustrated through a simulation example.

This paper investigates the cooperative stabilization problem for a class of linear switched systems under asynchronous switching. Based on a novel class of switching signals which prevail over the traditional average dwell time scheme, a sufficient condition of global asymptotic stability is proposed for the considered system. Unlike the existing results, the provided condition permits the Lyapunov-like function to increase not only in the period of mode-identifying process but also in normal-working period with matched controller. Compensating the increment by an increased decrement, the proposed approach can guarantee the decrease of the Lyapunov-like function from a whole perspective. Moreover, the solvability condition is established as well to obtain the gains for corresponding controllers. Finally, a simulation example is provided to verify the validity of the presented method.

This paper studies the distributed adaptive fuzzy containment tracking control for a class of high-order stochastic pure-feedback nonlinear multiagent systems with multiple dynamic leaders and performance constraint requirement. The control inputs are quantized by hysteresis quantizers. Mean value theorems are used to transfer the nonaffine systems into affine forms and a nonlinear decomposition is employed to solve the quantized input control problem. With a novel structure barrier Lyapunov function, the distributed control strategy is developed. It is strictly proved that the outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders, the containment tracking errors satisfy the performance constraint requirement and the resulting leader-following multiagent system is stable in probability based on Lyapunov stability theory. At last, simulation is provided to show the validity and the advantages of the proposed techniques.

This paper focuses on the problem of decentralized event-based dynamic output feedback adaptive fuzzy control for a class of interconnected stochastic nonlinear systems. In order to relax the Lipschitz condition for the nonlinearity, a novel dynamic gain observer is constructed to estimate the unmeasured state variables. The funnel-like control technique is proposed to ensure that the output of each subsystem satisfies the prescribed performance requirement. To save energy in signal transmission, the controller and its triggered mechanism are codesigned based on backstepping method. By using the approximation theory of fuzzy logic systems, an unknown continuous function is approximated, and the difficulty caused by unmodeled dynamics is removed with the aid of changing supply function idea. By applying the Lyapunov stability theory, it is proved that all the signals of the resulting closed-loop system with the designed controller are bounded in probability. Finally, simulation results are given to verify the effectiveness of the theoretical results.

This paper considers the adaptive fuzzy output feedback tracking control problem for a class of uncertain nonlinear switched systems with time delay and unmodeled dynamics. Based on a kind of switched K-filters, a prescribed performance control scheme is proposed to guarantee the tracking performance and restrain the fluctuation caused by switches between submodes as well. In addition, fuzzy logic systems (FLSs) are use to approximate unknown nonlinear functions and dynamic surface control (DSC) method is employed to eliminate the explosion of complexity problem inherent in traditional backstepping method. The proposed controllers of corresponding subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time (ADT). A numerical simulation is performed to illustrate the effectiveness of the proposed approach.

Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal silicon content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier design for the development tendency of hot metal silicon content based on blast furnace operation data. The cross characteristic of binary classification problem was found via embedding high-dimensional blast furnace data into a two-dimensional space. Then, presented a nonparallel hyperplanes based fuzzy classifier, which conquered the cross classification still holding the interpretability advantage as fuzzy classifier. The proposed method was tested on No.2 blast furnace of Liuzhou Steel in China, that demonstrated the excellent performance compared with some other classifier algorithms.

Reset control has been shown to have advantages over linear time-invariant controllers, especially, for control loops with time-delays. In this paper, in order to adapt reset control to networked control systems (NCS), multi-input multi-output discrete-time reset control model is first established by decomposing the controller output and defining appropriate flow set and jump sets. Then, we apply discrete-time reset controllers to NCS with time-varying delays in both forward and feedback channels, the modeling framework of networked reset control systems is given based on switched linear parameter-varying systems, and stability conditions are derived in terms of linear matrix inequalities. Finally, experimental and simulation examples are presented to illustrate the superior performance of the proposed approaches.

This paper considers the distributed consensus tracking problem for a class of high-order stochastic multiagent systems with uncertain nonlinear functions under a fixed undirected graph. Through the recursive method, the novel nonlinear distributed controllers are designed. By constructing a kind of special form for the virtual controller in the first step of recursive design, we realize that the state variables of every agent are separated except the outputs of the adjacency agents. The designed controller of each agent only depends on its own state variables and the outputs of the adjacent multiagents. With the proposed method, it is not required any more that the orders of the agents are same. This makes the designed controller be easier to be implemented and the proposed method be applicable for a wider class of multiagent systems. The efficiency of the design approach is illustrated by a simulation example.

The leader-following consensus problem is investigated for second-order time-varying nonlinear multiagent systems with unmodeled dynamics and unknown parameters over directed communication topology. Under the assumption that the unknown nonlinearities satisfy Lipschitz conditions with time-varying gains, a local adaptive law is introduced for the design of consensus protocol that enable all followers' state variables to consensus with that of leader asymptotically. The proposed protocols are independent of system parameters and only require the relative state information of its neighbors, and hence they are fully distributed. Simulation examples are given to illustrate the effectiveness of the theoretical results.

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

In this paper, we study the problem of output feedback distributed containment control for a class of high-order nonlinear multiagent systems under a fixed undirected graph and a fixed directed graph, respectively. Only the output signals of the systems can be measured. The novel reduced order dynamic gain observer is constructed to estimate the unmeasured state variables of the system with the less conservative condition on nonlinear terms than traditional Lipschitz one. Via the backstepping method, output feedback distributed nonlinear controllers for the followers are designed. By means of the novel first virtual controllers, we separate the estimated state variables of different agents from each other. Consequently, the designed controllers show independence on the estimated state variables of neighbors except outputs information, and the dynamics of each agent can be greatly different, which make the design method have a wider class of applications. Finally, a numerical simulation is presented to illustrate the effectiveness of the proposed method.

Due to the cognitive limitations of the human operator and lack of complete information about the remote environment, the work performance of such teleoperation systems cannot be guaranteed in most cases. However, some practical tasks conducted by the teleoperation system require high performances, such as tele-surgery needs satisfactory high speed and more precision control results to guarantee patient' health status. To obtain some satisfactory performances, the error constrained control is employed by applying the barrier Lyapunov function (BLF). With the constrained synchronization errors, some high performances, such as, high convergence speed, small overshoot, and an arbitrarily predefined small residual constrained synchronization error can be achieved simultaneously. Nevertheless, like many classical control schemes only the asymptotic/exponential convergence, i.e., the synchronization errors converge to zero as time goes infinity can be achieved with the error constrained control. It is clear that finite time convergence is more desirable. To obtain a finite-time synchronization performance, the terminal sliding mode (TSM)-based finite time control method is developed for teleoperation system with position error constrained in this paper. First, a new nonsingular fast terminal sliding mode (NFTSM) surface with new transformed synchronization errors is proposed. Second, adaptive neural network system is applied for dealing with the system uncertainties and the external disturbances. Third, the BLF is applied to prove the stability and the nonviolation of the synchronization errors constraints. Finally, some comparisons are conducted in simulation and experiment results are also presented to show the effectiveness of the proposed method.

In this correspondence paper, an exact method is developed to guarantee asymptotic stability of a bilateral teleoperation system that is subjected for a time-delayed communication. This extends the prior art of searching for the maximum upper bound of time delay. In order to improve the flexibility in controller design and obtain better performance, a fractional-order PDαcontroller is proposed. The exactly stable regions of delays are explored for both integral-order and fractional-order controllers. Compared with conditions in most previous works which are deduced by the Lyapunov-Krasovskii functional and rely on the solution of some linear matrix inequalities, the stability conditions proposed in this paper are established from the frequency domain point of view, and thus, the results are not only sufficient but also necessary. To illustrate accuracy of the conditions, they are simulated on a delayed teleoperation system composed of a pair of robots.

The asymptotical synchronization problem is investigated for two identical chaotic Lur'e systems with time delays. The sampled-data control method is employed for the system design. A new synchronization condition is proposed in the form of linear matrix inequalities. The error system is shown to be asymptotically stable with the constructed new piecewise differentiable Lyapunov-Krasovskii functional (LKF). Different from the existing work, the new LKF makes full use of the information in the nonlinear part of the system. The obtained stability condition is less conservative than some of the existing ones. A longer sampling period is achieved with the new method. The numerical examples are given and the simulations are performed on Chua's circuit. The results show the superiorities and effectiveness of the proposed control method.

This paper studies the dynamic output feedback tracking control problem for stochastic interconnected time-delay systems with the prescribed performance. The subsystems are in the form of triangular structure. First, we design a reduced-order observer independent of time delay to estimate the unmeasured state variables online instead of the traditional full-order observer. Then, a new state transformation is proposed in consideration of the prescribed performance requirement. Using neural network to approximate the composite unknown nonlinear function, the corresponding decentralized output tracking controller is designed. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of uniformly ultimately boundedness and that both transient-state and steady-state performances are preserved. Finally, a simulation example is given, and the result shows the effectiveness of the proposed control design method.

Parameter estimation problem is considered for a class of dual-rate Wiener systems whose input-output data are measured by two different sampling rate. Firstly, a polynomial transformation technique is used to derive a mathematical model for such dual-rate Wiener systems. Then, directly based on the dual-rate sampled data, a dual-rate Wiener systems stochastic gradient algorithm (DRW-SG) is presented. In order to improve the algorithm convergence rate, a dual-rate Wiener systems stochastic gradient algorithm with a forgetting factor algorithm (DRW-FF-SG) is presented. For making full use of the forgetting factor, a dual-rate Wiener systems stochastic gradient algorithm with an increasing forgetting factor algorithm (DRW-IFF-SG) is presented which performs excellently. Finally, an example is provided to test and illustrate the proposed algorithms.

As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems, such as the modeling and control problems. In view of these reasons, this paper is concerned with developing a Wiener model to predict the silicon content of blast furnace. Unlike traditional Wiener model, this paper avoids the optimization of high number of model parameters. The Wiener model here is composed of a basis filter filter expansion named Laguerre filter and a linear programming support vector regression (LP-SVR). They are used to represent the linear dynamic component and the nonlinear static element. Take the advantages that Laguerre filter can approximate linear systems with a lower model and order and LP-SVR can achieve a sparse solution, the proposed Wiener model not only improves the prediction accuracy but also reduces the computation complexity. Simulation results show that this Wiener model is suitable for the prediction of blast furnace silicon content.

The master-slave control design problem is considered for the networked teleoperation system with friction and external disturbances. A new finite-time synchronization control method is proposed with the help of adaptive fuzzy approximation. We develop a new nonsingular fast terminal sliding mode (NFTSM) to provide faster convergence and higher precision than the linear hyperplane-sliding mode and the classic terminal-sliding mode (TSM). Then, the adaptive fuzzy-logic system is employed to approximate the system uncertainties, and the corresponding adaptive fuzzy NFTSM controller is designed. By constructing Lyapunov function, the stability and finite-time synchronization performance are proved with the new controller in the presence of system uncertainties and external disturbances. Compared with the traditional teleoperation design method, the new control scheme achieves better transient-state performance and steady-state performance. Finally, the simulations are performed and the comparisons are shown among the proposed method, the P+d method, the PD+d method, the DFF method, and the classic TSM FTSM. The simulation results further demonstrate the effectiveness of the proposed method.

This brief is concerned with the problem of asymptotic stability of neural networks with time-varying delays. The activation functions are monotone nondecreasing with known lower and upper bounds. Novel stability criteria are derived by employing new Lyapunov-Krasovskii functional and the integral inequality. The developed stability criteria have delay dependencies and the results are characterized by linear matrix inequalities. New and less conservative solutions to the global stability problem are provided in terms of feasibility testing. Numerical examples are finally given to demonstrate the effectiveness of the proposed method.

This paper addresses the problem of robotic control with a simple PD-like controller without joint velocities. Compared with present works, our main contribution is using an globally exponentially speed observer (I and I speed observer) to estimate the joint velocities, we have proved that with the estimated velocities, the image errors converge to zero. The simulations are performed and the results show the effectiveness of the proposed method.

This paper addresses the robust trajectory tracking problem for an eye-in-hand system. It considers not only the uncertainties and disturbances in robot model, but also the unknown dead-zone input. By combing neural network approximation, sliding model control and adaptive technique, the robot can track the desired trajectory well using information proved by a camera. Finally, stability and robustness are proved by Lyapunov method. Computer simulations are presented to confirm the effectiveness of the proposed visual feedback controller.

The robust control problem is studied for a class of large-scale networked control systems. The subsystems are in the nonlinear form, and they exchange information through the communication networks. The interconnections considered are nonlinear, and not the traditional linear form, which brings a challenging issue for the decentralized control design. We develop a new memoryless control scheme with the use of the decomposition for each subsystem that is based on the input matrix. By Takagi–Sugeno (T–S) fuzzyfication for each subsystem, the interconnected T–S fuzzy subsystems are obtained. When the upper bound functions of uncertain interconnections are known, we design a decentralized memoryless state feedback controller. When the parameters of bound functions are not available, the adaptive method is used, and the decentralized memoryless adaptive controller is developed. By the construction of a new Lyapunov–Krasovskii functional, we prove the stability of the resultant closed-loop system for the both cases. Finally, we apply the theoretic results to the decentralized controller design of networked interconnected chemical reactor systems. The simulations are performed, and the effectiveness of the proposed method is demonstrated.

This paper addresses the stability-analysis problem for teleoperation systems with time delays. Compared with previous work, communication delays are assumed to be both time-varying and asymmetric, which is the case for network-based teleoperation systems. The stability analysis is performed for two classes of controllers: delayed position-error feedback and delayed torque feedback. By choosing Lyapunov-Krasovskii functional, we show that the master-slave teleoperation system is stable under specific linear-matrix-inequality (LMI) conditions. With the given controller-design parameters, the proposed stability criteria can be used to compute the allowable maximal transmission delay. Finally, both simulations and experiments are performed to show the effectiveness of the proposed method.

This paper investigates the stability analysis problem of teleoperation system. Compared with previous work, the communication delays are assumed to be both time-varying and unsymmetric. The stability analysis is performed on two classes of controllers: delayed position error feedback and delayed force feedback. By choosing Lyapunov Krasovskii functional, we show that the master-slave teleoperation system is asymptotically stable under specific LMI conditions. With the given controller design parameters, the proposed stability criteria can be used to compute the allowable maximum delay values. Finally, the simulations are performed to show the effectiveness of the proposed method.

The robust control problem is investigated for a class of uncertain nonlinear time-delay systems. Via the Takagi-Sugeno (T-S) fuzzification, we obtain the T-S fuzzy systems with each local model in the form of time-delay systems with uncertain nonlinear functions. The mismatched nonlinear functions satisfy the Lipschitz condition, while the matched parts are bounded by nonlinear functions with unknown coefficients. Based on the input matrix, the system is decomposed into two cascade subsystems. The virtual controller is designed for the first subsystem, and then, a memoryless adaptive controller is presented. By employing a new Lyapunov Krasovskii functional, we show that the resulting closed-loop system is exponentially stable and the solutions are uniformly ultimately bounded. Finally, simulation examples are given to show the effectiveness of our main results.

In this paper, the robust-control problem is investigated for a class of uncertain nonlinear time-delay systems via dynamic output-feedback approach. The considered system is in the strict-feedback form with unknown control direction. A full-order observer is constructed with the gains computed via linear matrix inequality at first. Then, with the bounds of uncertain functions known, we design the dynamic output-feedback controller such that the closed-loop system is asymptotically stable. Furthermore, when the bound functions of uncertainties are not available, the adaptive fuzzy-logic system is employed to approximate the uncertain function, and the corresponding output-feedback controller is designed. It is shown that the resulting closed-loop system is stable in the sense of semiglobal uniform ultimate boundedness. Finally, simulations are done to verify the feasibility and effectiveness of the obtained theoretical results.

In this paper, dynamic output feedback control problem is investigated for a class of nonlinear interconnected systems with time delays. Decentralized observer independent of the time delays is first designed. Then, we employ the bounds information of uncertain interconnections to construct the decentralized output feedback controller via backstepping design method. Based on Lyapunov stability theory, we show that the designed controller can render the closed-loop system asymptotically stable with the help of the changing supplying function idea. Furthermore, the corresponding decentralized control problem is considered under the case that the bounds of uncertain interconnections are not precisely known. By employing the neural network approximation theory, we construct the neural network output feedback controller with corresponding adaptive law. The resulting closed-loop system is stable in the sense of semiglobal boundedness. The observers and controllers constructed in this paper are independent of the time delays. Finally, simulations are done to verify the effectiveness of the theoretic results obtained.

In this paper, the problem of robust output tracking control for a class of time-delay nonlinear systems is considered. The systems are in the form of triangular structure with unmodeled dynamics. First, we construct an observer whose gain matrix is scheduled via linear matrix inequality approach. For the case that the information of uncertainties bounds is not completely available, we design an observer-based neural network (NN) controller by employing the backstepping method. The resulting closed-loop system is ensured to be stable in the sense of semiglobal boundedness with the help of changing supplying function idea. The observer and the controller designed are both independent of the time delays. Finally, numerical simulations are conducted to verify the effectiveness of the main theoretic results obtained.