Shape control of deformable linear objects (DLOs) is a major challenge in robotics due to their high-dimensional, nonlinear dynamics and sensitivity to boundary conditions. Existing data-driven and physics-based models either require large datasets or suffer from excessive computational cost for real-time control. This paper presents a Cosserat-based Physics-Informed Neural Network (C-PINN) framework for efficient, real-time DLO modeling and automatic shape control. By embedding Cosserat rod theory directly into the PINN loss, C-PINN achieves accurate, physically consistent predictions of the DLO while dramatically reducing the requirement for large-scale training data and is robust to unseen scenarios. To enhance generalization and training stability, we introduce a curriculum learning strategy and propose an online sim-to-real residual adaptation module to bridge the gap between simulation and real-world deployment. The learned surrogate model is integrated into a gradient-based model predictive controller (MPC), enabling real-time, closed-loop shape control. Extensive experiments demonstrate that our approach generalizes well to various DLO materials and configurations in both 2D and 3D scenarios, and adapts robustly to interactive, human-in-the-loop manipulation. Both simulation and real-world experiments show that our method consistently attains significantly lower RMSE, as confirmed by comprehensive comparisons with various baselines. These results highlight the effectiveness and versatility of C-PINN for practical, high-precision DLO manipulation in diverse robotic scenarios. Compared to traditional analytical solvers, C-PINN achieves up to a 228-fold improvement in computational speed.

This article studies the decentralized impulsive control problem for nonlinear interconnected systems (NISs) based on the dynamic event-triggered mechanism. By the fuzzy logic system (FLS)-based backstepping approach, we first propose a dynamic event-triggered impulsive controller. Unlike traditional event-triggered control (ETC), our impulsive control scheme allows for the instantaneous regulation of system states only at some state-dependent impulse instants, thus avoiding control inputs between two triggering moments. Notably, the resulting closed-loop impulsive systems include hybrid dynamics, general nonlinear characteristics, and prescribed performance constraints simultaneously. Based on the Lyapunov analysis method, we prove that even under discrete impulsive controllers, all closed-loop states remain bounded, and the prescribed tracking performance is achieved, namely, the tracking error can converge to a prescribed bounded region within a desired finite time. Then, the proposed impulsive control approach is further extended to the output-feedback case, under which the impulsive control design is based on the observer states. Finally, we show two simulation examples to validate the effectiveness of impulsive control schemes.

The multiuser haptic-enabled robotic system (M-Hers) facilitates shared control among human operators through task-dependent authority allocation, where interaction relationships are typically dictated by task requirements. However, some of these relationships can be nonpassive, generating excess energy that violates passivity constraints and compromises system stability. To address this, we first introduce the interaction architecture (IA) to formalize how operators influence task execution. Based on this framework, we propose a tank-based two-layer task model that ensures system passivity despite nonpassive IAs. This model comprises a virtual object (VO) layer for task rendering and a virtual system (VS) layer that passively executes nonpassive IA behaviors. The VS layer uses a global energy tank to compensate for IA-induced energy violations and modify the VO model when tank energy is depleted. This structure decouples task rendering from low-level robotic control, enabling seamless integration of an arbitrary number of robots with heterogeneous dynamics and control modes. Simulation and experimental results validate the proposed method’s scalability, flexibility, and effectiveness in preserving passivity while accurately realizing diverse IAs. This approach paves the way for scalable and easy-to-deploy control framework that supports multiuser haptic interaction.

Despite extensive investigations into the multi-user haptic-enabled robotic system (M-Hers), achieving scalable control design in the presence of non-passive human operators remains a key challenge. This is primarily due to the increasing complexity of stability conditions and interaction coupling as the number of operators grows. In this study, we address this challenge in two steps. First, we introduce the individual interaction environment (IIE) to isolate the passivity violations, which facilitates the independent control design for each human-robot subsystem, thereby enhancing the scalability with respect to the number of subsystems. Second, within the IIE framework, we identify passivity-violating components caused by partners' active behaviors and propose a novel augmented tank-based controller (ATBC) to guarantee passive IIE while maintaining high rendering accuracy. Specifically, the ATBC employs an energy-related power regulation strategy to enhance interaction safety and a time-varying control gain to mitigate the negative effects of power regulation on rendering fidelity. We validated the proposed method through collaborative haptic tasks on a customized M-Hers composed of three robots in four different scenarios. Comparative studies demonstrate that our approach effectively ensures IIE passivity in the presence of active human behaviors, while ensuring high reproducibility and achieving a favorable balance between passivity and rendering accuracy.

This paper presents a novel collision avoidance method for general ellipsoids based on control barrier functions (CBFs) and separating hyperplanes. First, collision-free conditions for general ellipsoids are analytically derived using the concept of dual cones. These conditions are incorporated into the CBF framework by extending the system dynamics of controlled objects with separating hyperplanes, enabling efficient and reliable collision avoidance. The validity of the proposed collision-free CBFs is rigorously proven, ensuring their effectiveness in enforcing safety constraints. The proposed method requires only single-level optimization, significantly reducing computational time compared to state-of-the-art methods. Numerical simulations and real-world experiments demonstrate the effectiveness and practicality of the proposed algorithm.

Visual place recognition is a critical component of robust simultaneous localization and mapping systems. Conventional approaches primarily rely on RGB imagery, but their performance degrades significantly in extreme environments, such as those with poor illumination and airborne particulate interference (e.g., smoke or fog), which significantly degrade the performance of RGB-based methods. Furthermore, existing techniques often struggle with cross-scenario generalization. To overcome these limitations, we propose an RGB-thermal multimodal fusion framework for place recognition, specifically designed to enhance robustness in extreme environmental conditions. Our framework incorporates a dynamic RGB-thermal fusion module, coupled with dual fine-tuned vision foundation models as the feature extraction backbone. Experimental results on public datasets and our self-collected dataset demonstrate that our method significantly outperforms state-of-the-art RGB-based approaches, achieving generalizable and robust retrieval capabilities across day and night scenarios. The code is available at https://github.com/HITSZ-NRSL/RGB-Thermal-VPR.

This paper analyzes decentralized/centralized event/ self-triggered schemes respectively according to a large-scale interconnected system with state limitation and uncertain interference, and devises a set of parallel controllers for the control input and the worst disturbance. Firstly, the system with asymmetric state constraints is transformed into an auxiliary system by means of coordinate transformation. And then, the physical and artificial systems are integrated to establish a parallel control structure to improve the performance of the system. Next, an aperiodic sampling robust$H_{\infty}$optimization method to save communication resources is proposed through zero-sum game (ZSG) theory, and the control pair sequence is obtained by solving the event-triggered Hamilton-Jacobi-Isaacs equation (ET-HJIE) via heuristic dynamic programming (HDP). Based on decentralized ET control (DETC), centralized ETC (CETC), decentralized self-triggered control (DSTC) and centralized STC (CSTC) are further proposed. In addition, the stability of the system under these four modes is proved, and the Zeno phenomenon is discussed. In the end, a simulation model is used to verify the control sequences within four trigger schemes, and satisfactory results are obtainedNote to Practitioners—Inspired by the large scale, interrelated terms, unavoidable disturbances, and nonlinear constraints of practical engineering systems, an aperiodic sampling HDP algorithm based on artificial-actual interaction is presented to handle with$H_{\infty}$optimization of nonlinear interconnected systems with state constraints. The four kinds of aperiodic sampling control methods including DETC, CETC, DSTC and CSTC are proposed in turn, which can greatly reduce data transmission while ensuring the control performance. Moreover, the advantages and characteristics of the four trigger mechanisms are summarized based on simulation, which has certain reference significance for physical systems with different control requirements.

This article investigates periodic event-triggered optimal output consensus of heterogeneous linear multiagent systems where each agent has knowledge of only its own cost function. In contrast to existing results, we consider communication delays and general strongly connected digraphs. A novel periodic event-triggered distributed control scheme is proposed, which allows asynchronous event detection and time-varying communication delays. Sufficient conditions with respect to the maximum allowable communication delays and event detection periods to achieve asymptotic optimal output consensus are established. Moreover, it is proved that the proposed periodic event-triggering mechanism can provide a positive lower bound of interevent times which is independent of the event detection period. A simulation example is provided to illustrate the effectiveness of the proposed control scheme.

The semi-global stabilization problem of discrete-time systems subject to infinite distributed input delays and actuator saturations is investigated in this article. This article develops two low-gain feedback control laws for two types of systems, respectively. It is shown that the resulting system is semi-globally exponentially stabilized. Our results include those existing results on systems subject to only input saturations and systems subject to bounded delays and input saturations as special cases. Compared with existing results on infinite delays and actuator saturations, this article develops a more accurate scaling utilizing a more general framework. Furthermore, a novel converse Lyapunov theorem for discrete-time linear infinite-delayed systems and a novel stability analysis theorem for perturbed discrete-time linear infinite-delayed systems are developed to handle the nonlinearity induced by saturations. Finally, this article provides two numerical examples to illustrate the effectiveness of the developed theorems.

In this article, a fully distributed event-triggered protocol is proposed to solve the consensus problem of uncertain Euler-Lagrange (EL) multiagent systems (MASs) under jointly connected digraphs. First, distributed event-based reference generators are proposed to generate continuously differentiable reference signals via event-based communication under jointly connected digraphs. Unlike some existing works, only the states of agents rather than virtual internal reference variables need to be transmitted among agents. Second, adaptive controllers are exploited based on the reference generators so that each agent can track the reference signals. The uncertain parameters converge to their real values under an initially exciting (IE) assumption. It is proved that the uncertain EL MAS achieves state consensus asymptotically under the proposed event-triggered protocol composed of the reference generators and the adaptive controllers. A unique feature of the proposed event-triggered protocol is its fully distributed property: the protocol does not depend on global information about the jointly connected digraphs. Meanwhile, a minimum interevent time (MIET) is guaranteed. Finally, two simulations are conducted to show the validity of the proposed protocol.

This article deals with the leader-following consensus problem of multiple uncertain Euler-Lagrange systems with unknown nonlinear dynamics. By introducing a dynamic compensator for each agent, a fully distributed control strategy is developed based on the fuzzy approximation approach, which is independent of any priori global information associated with the communication topology. Meanwhile, a distributed event-triggering mechanism (ETM) is designed such that each agent broadcasts its states only when an event occurs. It is shown that with the proposed ETM, the leader-following consensus is achieved with aperiodic intermittent communication and Zeno behavior is excluded by contradiction. Moreover, the consensus tracking errors converge to small sets around the origin. Finally, an example is provided to illustrate the effectiveness of the obtained theoretical results.

This article investigates the distributed optimal output consensus problem of heterogeneous linear multiagent systems over weight-unbalanced directed networks. A novel distributed continuous-time state feedback controller is proposed to steer the outputs of all the agents to converge to the optimal solution of the global cost function. Under the standard condition that the unbalanced digraph is strongly connected and the local cost functions are strongly convex with global Lipschitz gradients, the exponential convergence of the closed-loop multiagent system is established. Then, the proposed state feedback control law is extended to an observer-based output feedback setting. Two examples are finally provided to illustrate the effectiveness of the proposed control schemes.

In this article, the finite-frequency fault detection filter design problem is investigated for a class of networked nonlinear systems subject to medium access constraints. The nonlinear system is modeled by Takagi–Sugeno (T–S) fuzzy affine dynamic models, and only one node that includes partial measured information can gain access to the shared transmission medium according to the allocated access probability. Within the stochastic$\mathscr {H}_{-}/\mathscr {H}_{\infty }$filtering framework, an admissible filter is designed such that, in the finite-frequency domain, the filtering error system is stochastically stable and the fault can be detected using partially available measurements. First, by integrating with the$\mathscr{S}$-procedure, the generalized Kalman–Yakubovič–Popov lemma is further developed to obtain sufficient conditions guaranteeing the desired finite-frequency performance of the filtering error system. Then, by applying piecewise quadratic Lyapunov functions, Projection lemma, and some convexification techniques, the filter design approach is proposed for the constrained networked nonlinear system. It is shown that the filter design problem can be addressed by solving a set of linear matrix inequalities. Simulation studies are finally given to illustrate the effectiveness of the proposed design approach.

This article investigates the adaptive optimal control problem for networked discrete-time nonlinear systems with stochastic packet dropouts in both controller-to-actuator and sensor-to-controller channels. A Bernoulli model-based Hamilton-Jacobi-Bellman (BHJB) equation is first developed to deal with the corresponding nonadaptive optimal control problem with known system dynamics and probability models of packet dropouts. The solvability of the nonadaptive optimal control problem is analyzed, and the stability and optimality of the resulting closed-loop system are proven. Two reinforcement learning (RL)-based policy iteration (PI) and value iteration (VI) algorithms are further developed to obtain the solution to the BHJB equation, and their convergence analysis is also provided. Furthermore, in the absence of a priori knowledge of partial system dynamics and probabilities of packet dropouts, two more online RL-based PI and VI algorithms are developed by using critic-actor approximators and packet dropout probability estimator. It is shown that the concerned adaptive optimal control problem can be solved by the proposed online RL-based PI and VI algorithms. Finally, simulation studies of a single-link manipulator are provided to illustrate the effectiveness of the proposed approaches.

This paper studies the distributed continuous-time nonconvex optimization problem of multi-agent systems over unbalanced digraphs. Each agent is endowed with a local cost function, which is privately known to the agent but not necessarily convex. We aim to drive all the agents to cooperatively converge to the optimal solution of the sum of all local cost functions. Based on the adaptive control approach, a fully distributed algorithm is developed for each agent in the case that neither prior global information concerning network connectivity nor convexity of local cost functions is available. A key feature of the algorithm is that it removes the dependence on the smallest strong convexity constant of local cost functions, and the left eigenvector corresponding to the zero eigenvalue of the Laplacian matrix of unbalanced digraphs.

In this article, the cooperative output regulation problem of heterogeneous linear multiagent systems under jointly connected digraphs is addressed. The event-triggered control protocols based on state feedback and output feedback are proposed, respectively. It is shown that the output tracking errors of the resulting closed-loop control systems converge to 0 exponentially via the proposed protocols. One of the key advantages of the proposed event-triggering mechanism is that the information transmissions induced by event triggerings and topology switchings are independent, and data transmissions among agents are thus reduced. Furthermore, an explicit minimum interevent time is provided for all the agents so that the Zeno-behavior is excluded strictly. Finally, three numerical examples are provided to verify the effectiveness of the proposed protocols.

This article studies the tracking problem of a class of heterogeneous linear minimum-phase discrete-time multiagent systems (MASs) with unknown agent parameters in the presence of bounded disturbances. By introducing a distributed adaptive observer and a reference model, a novel framework is proposed to convert the complicated cooperative tracking problem of unknown heterogeneous MASs into a cooperative tracking problem of the reference models to the leader and a local robust model reference adaptive control problem. It is shown that under the adaptive controller designed based on the proposed framework, the tracking errors between the outputs of all the agents and the output of the leader converge to a residual set. It is also shown that the tracking errors will converge to zero asymptotically when the disturbances are absent. Compared with the existing related works, our main contribution is that the proposed framework could deal with the unknown MASs with arbitrary individual relative degrees and do not rely on any global graph information. Finally, the effectiveness of the proposed controller is illustrated by an example.

This article focuses on the mean-square consensus control problem for a class of discrete-time multiagent systems (DT-MASs) over time-correlated multistate Markovian fading channels, where the packet loss probability is time-varying and depends on the current channel state. In order to save limited network bandwidth, a compressed coding scheme is developed by preprocessing the measurement output. With the aid of a stochastic Lyapunov–Krasovskii functional, a sufficient condition is first obtained under which the consensus error system is mean-square stable for DT-MASs over identical fading channels. Then, the consensus gain is formulated as the feasible solution to a set of linear matrix inequalities (LMIs) whose dimensions are independent of the number of agents. Furthermore, for the case that agents communicate over nonidentical fading channels, the mean-square consensus problem is transformed into an analyzable edge agreement issue in the mean-square sense by means of properties of the edge Laplacian combined with a mapping technique. Next, a sufficient condition is derived to ensure the mean-square consensus performance, based on which the existence of the controller can be guaranteed by the feasibility of a set of LMIs. Finally, the validity and feasibility of the developed design scheme are shown by two illustrative examples.

Taking safety and performance into consideration, the state and control input of the actual engineering system are often constrained. For this kind of problem, this article puts forward an online dual event-triggered (ET) adaptive dynamic programming (ADP) optimal control algorithm for a class of nonlinear systems with constrained state and input. First, the original system is transformed into another system through the barrier function, after that, a suitable value function with a nonquadratic utility function is designed to obtain the optimal control pair. In addition, on the premise of the asymptotic stability of the system, the trigger condition is devised, and the intersampling time analysis is proved that the algorithm can avoid the Zeno phenomenon. What is more, the critic, action, and disturbance neural networks (NNs) are trained to approximate value function and control sequences, subsequently, the approximation error is proved to be uniformly ultimately boundedness (UUB). Finally, two comparative experiments based on the robot arm model are simulated to verify that the algorithm can make control policies update only when the system has the requirement and keep satisfactory control effect, which can effectively decrease the number of data transfers and reduce the calculation burden.

In this article, a finite-time distributed average tracking (DAT) problem is considered for unity relative degree nonlinear multi-agent systems (MASs) subject to external disturbances. The objective of this article is to design a distributed controller such that the output of each agent converges to the desired trajectory (the average of multiple nonlinear signals) within a finite time. First, we introduce a steady-state generator, which can reproduce the desired trajectory. Based on the steady-state generator, we design a distributed observer to estimate the desired trajectory, which is robustness to initialization errors. Then, an observer-based output-feedback controller is proposed for each agent such that the output of each agent can converge to its own generated signal within a finite time. Finally, it is shown that the output of each agent can converge to the desired trajectory within a finite time using the proposed control strategy, and hence the finite-time DAT problem is solved. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm.

In this article, the output containment control problem of heterogeneous linear multiagent systems (MASs) with unbounded distributed transmission delays is considered. A key technical lemma is first established to show that the distributed observers under unbounded distributed transmission delays can be utilized to estimate the convex combination of the states of multiple leaders. Then, a novel distributed output feedback control law is proposed based on those distributed observers. It is shown that under our proposed control law, the output of each follower converges to the convex hull spanned by the outputs of the leaders. In addition, our results include relevant results on output containment control of MASs with bounded distributed or constant transmission delays as special cases. Finally, the effectiveness of the proposed control law is demonstrated by a simulation example.

This article investigates the cooperative output regulation problem for heterogeneous nonlinear multiagent systems subject to disturbances and quantization. The agent dynamics are modeled by the well-known Takagi-Sugeno fuzzy systems. Distributed reference generators are first devised to estimate the state of the exosystem under directed fixed and switching communication graphs, respectively. Then, distributed fuzzy cooperative controllers are designed for individual agents. Via the Lyapunov technique, sufficient conditions are obtained to guarantee the output synchronization of the resulting closed-loop multiagent system. Finally, the viability of proposed design approaches is demonstrated by an example of multiple single-link robot arms.

This article investigates the cooperative output regulation problem for discrete-time heterogeneous multiagent Markov jump systems. Two cases are studied: 1) output regulation quadratic control in the case where the exosystem is accessible to all agents and 2) cooperative output regulation quadratic control in the case where only a part of agents can directly communicate with the exosystem. The hidden Markov models are employed to describe the asynchronous modes of the agents and their corresponding controllers. Via the jumping regulator equation, asynchronous control laws are constructed and the algorithms to obtain control parameters are presented in terms of linear matrix inequalities. For the first case, the optimal synchronous/mode-dependent control law, which is a special case of the asynchronous control protocol, is also given via the stochastic dynamic programming approach. Finally, an example is given to illustrate the effectiveness of the proposed approaches.

Cooperative output regulation (COR) of multiagent systems having heterogeneous uncertain nonlinear dynamics is often challenging because of the complex system dynamics and the coupling among agents. This article develops an adaptive internal model-based distributed regulator such that the outputs of a network of nonlinear agents are all regulated to a reference despite external disturbances. Specifically, we consider heterogeneous agents having nonlinear strict-feedback forms, with nonidentical unknown control directions, and subject to an unknown linear exosystem. Addressing the nonlinear COR problem shows the capability and flexibility of the proposed output regulator. The simulation results of output synchronization of Lorenz systems and cooperative tracking control of multiple ships are presented to show the capability of the proposed regulator.

In this article, we address the synchronization problem of networked uncertain Euler-Lagrange systems subject to disturbances, network delays, and uniformly connected switching networks. Compared with existing works, the current problem setting is more practical and technically more challenging. First, to tackle the disturbances under switching networks, we establish one lemma to show the convergence of a piecewise continuous function. Then, we establish the input-to-state stability (ISS) property of a class of perturbed time-delay systems to enable the distributed estimation of the system matrix and the output matrix of the leader system through delayed and switched network communication. Based on the certainty equivalence principle, we design an adaptive distributed control law. The synchronization control of four three-link cylindrical arms is used to demonstrate the effectiveness of the proposed approach.

This article investigates the leader-following consensus problem of heterogeneous linear multiagent systems under switching and directed topologies. It is assumed that the communication between agents suffers from time-varying delays and only the neighboring agents of the leader are able to get access to the information of the leader agent, including its agent matrices. A key technical lemma on the input to state stability of time-delayed systems is first established with which the main results of this article can be obtained. An adaptive distributed observer, taking into consideration of communication time delays, is proposed for each follower to estimate the leader's system matrices and its state. Then, a distributed controller based on this adaptive observer is developed. We show that the resulting closed-loop multiagent system achieves the leader-following output consensus. Two examples are finally given to illustrate the effectiveness of the proposed controller.

This article addresses the event-triggered adaptive fuzzy output-feedback control problem for a class of nonstrict-feedback nonlinear systems with asymmetric and time-varying output constraints, as well as unknown nonlinear functions. By designing a linear observer to estimate the unmeasurable states, a novel event-triggered adaptive fuzzy output-feedback control scheme is proposed. The barrier Lyapunov function (BLF) and the error transformation technique are used to handle the output constraint under a completely unknown initial tracking condition. It is shown that with the proposed control scheme, all the solutions of the closed-loop system are semiglobally bounded, and the tracking error converges to a small set near zero, while the output constraint is satisfied within a predetermined finite time, even when the constraint condition is violated initially. Moreover, with the proposed event-triggering mechanism (ETM), the Zeno behavior can be strictly ruled out. An example is finally provided to demonstrate the effectiveness of the proposed control method.

In this article, an output regulation problem is considered for nonlinear multiagent systems with unity relative degree, in which nodes are coupled by dynamic edges. The inputs of the edge dynamic systems are determined by the error outputs of the node dynamic systems. Similarly, the neighboring inputs of the node dynamic systems are formed by the outputs of the edge dynamic systems and can influence node outputs. By introducing some coordinate transformations, we can transform the output regulation problem into a robust stabilization problem for an augmented system. Then, using the relative outputs of neighboring agents, we design a distributed output-feedback control law and suitable dynamic couplings between the nodes. Finally, it is shown that the global stabilization of the augmented system can be achieved using the proposed controller. An example is presented to demonstrate the effectiveness of our control strategy.

This article investigates the event-triggered output consensus problem for a class of unknown heterogeneous discrete-time linear multiagent systems in the presence of unmodeled dynamics. The agents have individual nominal dynamics with unknown parameters, and the unmodeled dynamics are in the form of multiplicative perturbations. A novel design framework is developed based on an event-triggered internal reference model and a distributed model reference adaptive controller. To deal with the heterogeneity of the multiagent system, the event-triggered internal reference model is designed to generate a virtual reference signal for each agent with a dynamic event-triggering mechanism being adopted to reduce the communication burden between neighboring agents. To handle the unknown parameters and unmodeled dynamics, the robust model reference adaptive controller is then designed to follow the generated virtual reference signal. It is shown that if the unmodeled dynamics satisfy certain conditions, then the boundedness of all the signals and variables in the closed-loop system and convergence of consensus errors to a residual set are guaranteed. Moreover, the consensus errors will converge to zero asymptotically in the absence of unmodeled dynamics. Compared with existing related works, the proposed framework is able to deal with the agents with individual unknown nominal dynamics and unmodeled dynamics. Moreover, the proposed framework is fully distributed in the sense that no knowledge of any global information is needed. Finally, the performance of the proposed method is validated by examples.

This article addresses the cooperative output tracking problem of a class of linear minimum-phase multiagent systems, where the agent dynamics are unknown and heterogeneous. A distributed event-triggered model reference adaptive control strategy is developed. It is shown that under the proposed event-triggered control strategy, the outputs of all the agents synchronize to the output of the leader asymptotically. It is also shown that Zeno behavior can be excluded with the proposed novel event triggering mechanism. In addition, the proposed adaptive control strategy is fully distributed in the sense that no prior knowledge of some global information, such as the eigenvalues of the associated Laplacian matrix and the number of the agents is required. Finally, an example is given to demonstrate the effectiveness of the proposed control strategy.

This article is concerned with the finite-frequency$\mathcal {H}_{-}/\mathcal {H}_{\infty }$memory fault detection filtering problem for discrete-time Takagi–Sugeno fuzzy affine systems with norm-bounded uncertainties. The objective is to design a piecewise affine memory filter by using system historical information such that the resulting closed-loop filtering error system is asymptotically stable with the prescribed finite-frequency$\mathcal {H}_{-}/\mathcal {H}_{\infty }$performance. Based on the generalized Kalman–Yakubovič–Popov lemma combined with the celebrated$\mathcal {S}$-procedure, new sufficient conditions for the fuzzy affine filtering error system to have the finite-frequency$\mathcal {H}_{-}/\mathcal {H}_{\infty }$performance are given at first. By further using piecewise fuzzy quadratic Lyapunov functions and Projection lemma, the filtering analysis results for the filtering error system to be asymptotically stable with the prescribed finite-frequency$\mathcal {H}_{-}/\mathcal {H}_{\infty }$performance are obtained. Then, the filtering synthesis is carried out with the aid of matrix inequality convexification techniques, and the synthesis results are described in terms of linear matrix inequalities. It is further shown that a better filtering performance can be achieved by using more system historical information. Finally, simulation is provided to verify the effectiveness of the proposed approach.

This article investigates the leader-following output consensus problem of heterogeneous linear multiagent systems, where the nonidentical followers are subject to norm bounded parameter uncertainties. Two novel distributed event-triggered consensus strategies based on state feedback and output feedback, respectively, are proposed to solve the leader-following output consensus problem. A dynamic event-triggering mechanism which involves internal state variables is proposed to determine the next triggering instant. It should be noted that the controllers can be designed without the knowledge of some global graph information, such as the eigenvalues of the Laplacian matrix associated with the corresponding communication graph and the number of agents. In addition, the interevent time can be prolonged and the Zeno behavior can be excluded with the proposed dynamic triggering mechanism. Finally, an example is presented to illustrate the effectiveness of the proposed controllers.

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.

This article devotes to investigating the issue of fuzzy adaptive control for a class of strict-feedback nonlinear systems with nonaffine nonlinear faults. The computational complexity is reduced by adopting the dynamic surface control technique. Under the framework of finite-time stability, a novel fault-tolerant control strategy is designed so that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set in a finite time. Finally, simulation studies for an electromechanical system are shown to verify the feasibility of the presented approach.

In this paper, we investigate the distributed output-feedback tracking problem of nonlinear multiagent systems (MASs) where nonlinear functions of agent dynamics depend on unmeasurable states. Most existing results on output-feedback control of nonlinear MASs are concerned with nonlinear agent dynamics in output canonical form in which the nonlinear functions of agent dynamics only depend on the measurable output. When the nonlinear functions of agent dynamics depend on unmeasurable states, the problem remains open due to the difficulties in constructing distributed observers. When the leader is only available to a small portion of followers in the directed communication topology, we establish relationship between the network topology and the design of the corresponding distributed reduced order observers. Furthermore, by developing a new distributed high-gain homogeneous domination design method, distributed observers, and controllers are designed to guarantee that the errors between the followers' outputs and the leader's output can be made arbitrarily small while keeping all the states of closed-loop system bounded. Finally, a numerical example is employed to illustrate the effectiveness of the proposed control scheme.

This article studies the robust output regulation problem (RORP) for a class of uncertain linear systems with unknown exosystems via event-triggered control (ETC). Compared with existing related works, the system matrix of the exosystem under consideration is allowed to contain unknown parameters. Based on an adaptive internal model, an ETC strategy composed of an event-triggered adaptive output feedback control law and an output-based event-triggering mechanism is proposed such that the RORP is solved. It is shown that under the proposed ETC strategy, the tracking error converges to zero asymptotically in spite of the unknown exosystem, and meanwhile the occurrence of Zeno behavior is prevented. Finally, two examples are provided to illustrate the effectiveness of the proposed control strategy.

This paper studies the cooperative control problem of multiple nonlinear benchmark systems perturbed by second-order moment processes. The nonlinear benchmark system consists of a moving car and a rolling ball in oscillating surroundings. When the leader is only accessible to a small part of the followers in a directed graph, a new vectorial backstepping method is proposed for the design of distributed cooperative control laws. By using stochastic analysis techniques and algebra graph theory, it is shown that the cooperative control problem under consideration is solvable. Specifically, the errors between the followers' outputs and the leader's output can be made arbitrarily small while keeping all states of the closed-loop system bounded in probability. Finally, the effectiveness of the proposed control scheme is demonstrated through a simulation example.

This paper investigates the consensus problem of linear multi-agent systems subject to external disturbances via distributed event-triggered adaptive control. First, a distributed event-triggered adaptive output feedback control strategy is proposed for each agent. It is shown that under this control strategy, the consensus problem can be solved for any connected undirected communication graph in a fully distributed manner without using any global information. Then a distributed self-triggered adaptive output feedback control strategy is designed with which continuous monitoring of the measurement error is no longer needed. It is further shown that for the proposed event-triggered and self-triggered control strategies, no agent will exhibit Zeno behavior. Finally, the effectiveness of the proposed two control strategies is illustrated on a group of two-mass-spring systems.

This paper investigates the cooperative output regulation problem for heterogeneous linear multiagent systems under fixed communication graphs via event-triggered control. A fully distributed event-triggered dynamic output feedback control law is proposed based on the feedforward design approach. At the same time, a fully distributed dynamic event-triggering mechanism is designed so that each agent can determine when to broadcast its information to its neighbors. Compared with existing related results, both the control law and the event-triggering mechanism in this paper are independent of any global information. It is shown that with the proposed dynamic event-triggered control strategy, the cooperative output regulation problem can be solved in a fully distributed manner by intermittent communication. Moreover, Zeno behavior can be strictly ruled out for each agent. Finally, the effectiveness of the proposed dynamic event-triggered control strategy is validated by a numerical example.

This article addresses the finite-time adaptive fuzzy control problem for a class of nonstrict-feedback uncertain nonlinear systems via an event-triggered strategy. A novel design scheme, consisting of finite-time adaptive fuzzy controller and event-triggering mechanism (ETM), is proposed to decrease the number of data transmission and the number of control actuation updates. With the proposed event-triggered adaptive fuzzy control scheme, all the solutions of the resulting closed-loop system are guaranteed to be semi-globally bounded within finite time. Moreover, the feasibility of the proposed ETM is verified by excluding Zeno behavior. In contrast to existing results on similar problems, the restrictions on nonlinearities are relaxed and the more general uncertain nonlinear systems are considered. Finally, an example is provided to illustrate our theoretical results.

A key problem in the formation control of homogeneous multiagent systems is the collision-free convergence of the agent positions into a desired formation. It is a typical NP-hard problem by considering the problem as optimizing the assignment of multiple destinations to the same number of agents deployed in an open space. It becomes even harder if the collision avoidance is required during the motion of agents, and thus, a suboptimal but efficient solution is adequate. The traditional methods make it by accurate preplanning of the motion trajectory of each single agent, or simply letting them reach an equilibrium as a tradeoff between the collision avoidance and the desired formation. In this article, a distributed control algorithm embedded with an assignment switch scheme is proposed to guarantee that the asymptotic convergence to the desired formation is achieved with no collisions between agents. By the proposed algorithm, the agents keep moving in straight lines toward their respective destinations until they are going to collide if they do not stop, at which moment the agents will communicate their information locally to switch their destination assignments so that they will continue to move in different directions and avoid potential collisions. Distributed control rules are also defined to confine the motion space of each agent for collision avoidance. It has been rigorously proven that the positions of all agents converge to the desired formation with no collision under random initial deployment. In addition, a detailed parameter design procedure is provided for both setting and controlling of the formation. Finally, Monte Carlo simulations and actual experiments in the outdoor environment are implemented and the results verify the effectiveness of the proposed algorithm.

This paper studies the finite-time H∞control problem of T-S fuzzy systems subject to external disturbances. A novel fuzzy control approach is developed on the basis of the control Lyapunov function technique and the finite-time Lyapunov theorem so that the closed-loop fuzzy system is finite-time stable with guaranteed H∞performance. It is shown that the proposed control approach does not require the existence of a Lyapunov function before design of the fuzzy controller in comparison with many existing control approaches to finite-time control of nonlinear systems via the technique of control Lyapunov functions, and thus, the finite-time controller and the corresponding Lyapunov function can be designed simultaneously. The procedures on how to obtain the fuzzy controller are provided in terms of linear matrix inequalities. Moreover, an upper bound on the time taken for state trajectories to arrive at their desired targets is estimated. The effectiveness of the proposed finite-time H∞control approach is finally illustrated via numerical simulations.

This paper investigates the quantized consensus problem of general linear multiagent systems (MASs) by event-triggered control. A novel event-triggered distributed control protocol is proposed based on a new dynamic quantizer. Compared with periodical sampling for most existing works on quantized consensus, the proposed event-triggered control approach can significantly save communication and thus energy resource. It is shown that with the proposed distributed control protocol quantized consensus of the concerned MAS can be achieved, and the Zeno behavior and continuous monitoring of the neighbors' states are avoided. Moreover, it is shown that the required number of the quantization levels of the new quantizer remains small even if the number of the agents in the MAS is large. Three simulation examples are given to illustrate the effectiveness of the proposed consensus protocol.

In this paper, we study the adaptive fuzzy output feedback control problem for a class of nonstrict-feedback nonlinear systems with output constraint. The case of asymmetric and time-varying output constraint together with unmeasurable states is considered. By integrating the barrier Lyapunov function (BLF) approach and the fuzzy approximation technique, we show that the output constrained adaptive fuzzy output feedback control problem for a class of uncertain nonlinear systems can be solved. Finally, the effectiveness of the main result is demonstrated by simulation studies of a one-link manipulator system.

In this study, we investigate the leader-following consensus problem of a class of heterogeneous secondorder nonlinear multi-agent systems subject to disturbances. In particular, the nonlinear systems contain uncertainties that can be linearly parameterized. We propose a class of novel distributed control laws, which depends on the relative state of the system and thus can be implemented even when no communication among agents exists. By Barbalat’s lemma, we demonstrate that consensus of the second-order nonlinear multi-agent system can be achieved by the proposed distributed control law. The effectiveness of the main result is verified by its application to consensus control of a group of Van der Pol oscillators.

In this paper, the robust output consensus problem for a class of heterogeneous linear multiagent systems (MASs) in presence of aperiodic sampling and random deny-of-service (DoS) attack is investigated. A novel distributed output-feedback control strategy is proposed so that the controlled MAS achieves the objective of output consensus in spite of aperiodic sampling and DoS attack. By assuming that the sampling process is nonuniform and the consecutive attack duration is upper bounded, the closed-loop control system is first described as a discrete-time switched stochastic delay system. Some sufficient conditions are then obtained for the solvability of the secure consensus problem. Furthermore, a constructive design procedure for the proposed controller is then presented. Finally, a simulation example is introduced to illustrate the effectiveness of controller design.

This paper considers a robust adaptive fuzzy control problem for a class of uncertain nonlinear systems via an event-triggered control strategy. Fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. A novel robust adaptive control scheme together with a novel event-triggering mechanism (ETM) is proposed to reduce communication burden. It should be noted that both the control signal and the adaptive parameters are updated only at the triggering time instants in the proposed scheme, which further saves the system energy and resources. It is shown that with the proposed event-triggered robust adaptive control scheme, all the signals in the closed-loop system are guaranteed to be semiglobally bounded, and the output of the system converges to a small neighborhood of the origin. Moreover, with the proposed ETM, Zeno behavior can be strictly excluded. Finally, a one-link manipulator system is used to demonstrate the effectiveness of the proposed control scheme.

In this paper, the leader-following consensus problem of multiple uncertain Euler-Lagrange systems is studied by developing a new adaptive distributed control law based on sensory feedback. In comparison with existing results, the developed distributed control law depends on the relative position of the Euler-Lagrange systems instead of the relative internal state of the controller. In the case that all systems are only equipped with sensors rather than communication devices, the developed distributed control law shows its distinct advantage. Moreover, the communication cost can be reduced by the new adaptive control law. The effectiveness of the main result is demonstrated by its application to cooperative control of multiple two-link robot arms.

This paper investigates the adaptive output regulation problem for heterogeneous linear multiagent systems under randomly switching communication topologies. The switching mechanism is governed by a time-homogeneous Markov process, whose states correspond to all possible communication topologies among agents. A novel distributed adaptive cooperative controller is presented, where the dynamic compensators are utilized to estimate the exogenous signal for all the agents in mean square sense. The distributed control law is based upon the local information of agents, without using the global information of the communication topologies. Finally, illustrative examples are put forward to demonstrate the effectiveness of the given control scheme.

In this paper, an antisynchronization problem is considered for an array of linearly coupled reaction-diffusion neural networks with cooperative-competitive interactions and time-varying coupling delays. The interaction topology among the neural nodes is modeled by a multilayer signed graph. The state evolution of a neuron in each layer of the coupled neural network is described by a reaction-diffusion equation (RDE) with Dirichlet boundary conditions. Then, the collective dynamics of the multilayer neural network are modeled by coupled RDEs with both spatial diffusion coupling and state coupling. An edge-based adaptive antisynchronization strategy is proposed for each neural node to achieve antisynchronization by using only local information of neighboring nodes. Furthermore, when the activation functions of the neural nodes are unknown, a linearly parameterized adaptive antisynchronization strategy is also proposed. The convergence of the antisynchronization errors of the nodes is analyzed by using a Lyapunov-Krasovskii functional method and a structural balance condition. Finally, some numerical simulations are presented to demonstrate the effectiveness of the proposed antisynchronization strategies.

This paper investigates exponential stabilization of continuous-time dynamic systems (CDSs) via event-based impulsive control (EIC) approaches, where the impulsive instants are determined by certain state-dependent triggering condition. The global exponential stability criteria via EIC are derived for nonlinear and linear CDSs, respectively. It is also shown that there is no Zeno-behavior for the concerned closed loop control system. In addition, the developed event-based impulsive scheme is applied to the synchronization problem of master and slave memristive neural networks. Furthermore, a self-triggered impulsive control scheme is developed to avoid continuous communication between the master system and slave system. Finally, two numerical simulation examples are presented to illustrate the effectiveness of the proposed event-based impulsive controllers.

In this paper, we consider the cooperative output regulation problem of heterogeneous linear multi-agent systems (MASs) by event-triggered control. We first develop an event-triggering mechanism for leader-following consensus of homogeneous MASs. Then by proposing an internal reference model for each agent, a novel distributed event-triggered control scheme is developed to solve the cooperative output regulation problem of heterogeneous MASs. Furthermore, a novel self-triggered control scheme is also proposed, such that continuous monitoring of measurement errors can be avoided. The feasibility of both proposed control schemes is studied by excluding Zeno behavior for each agent. An example is finally provided to demonstrate the effectiveness of the control schemes.

This paper addresses the output consensus problem of heterogeneous linear multi-agent systems. We first propose a novel distributed event-triggered control scheme. It is shown that, with the proposed control scheme, the output consensus problem can be solved if two matrix equations are satisfied. Then, we further propose a novel self-triggered control scheme, with which continuous monitoring is avoided. By introducing a fixed timer into both event- and self-triggered control schemes, Zeno behavior can be ruled out for each agent. The effectiveness of the event- and self-triggered control schemes is illustrated by an example.

This paper studies the adaptive finite-time stabilization problem for a class of nonlinear systems described by Takagi-Sugeno (T-S) fuzzy dynamic models with parametric uncertainties. A novel adaptive state feedback control scheme for the T-S fuzzy systems is proposed, and the scheme is developed based on finite-time Lyapunov theorem and adaptive backstepping-like method. Augmented dynamics are introduced in the design of finite-time stabilization controllers to construct suitable finite-time Lyapunov functions. It is shown that finite-time convergence of the closed-loop adaptive control system can be achieved, and the potential controller singularity problem caused by the augmented dynamics can be avoided. In addition, constructive procedures to obtain such an adaptive finite-time controller are given. Convergence time as a transient performance specification is also taken into account, and a finite upper bound on the convergence time is estimated. Finally, two numerical examples are provided to illustrate the effectiveness and practicality of the proposed adaptive control approach.

This paper studies the consensus problem of heterogeneous linear multiagent systems with arbitrarily large constant, time-varying, or distributed communication delays. Novel distributed dynamic controllers are proposed for such multiagent systems with fixed and switching directed communication topologies, respectively. It is shown that the controlled heterogeneous linear multiagent system can reach consensus for arbitrarily large constant, time-varying, and distributed communication delays under some sufficient conditions. Simulation examples are provided to demonstrate the effectiveness of the proposed controllers.

This paper considers the finite-time stabilization problem for a class of nonlinear systems that can be described by Takagi-Sugeno (T-S) fuzzy models. We propose a novel finite-time switching fuzzy control scheme for T-S fuzzy models, and the scheme is based on the Lyapunov stability theory and the control Lyapunov function technique. It is shown that the finite-time fuzzy controller and the quadratic control Lyapunov function can be obtained at the same time by solving a set of linear matrix inequalities, which can be easily facilitated by available software packages. It is also shown that the potential control law singularity can be avoided with the proposed control scheme. Unlike many existing approaches to finite-time stabilization of general nonlinear systems, the proposed approach does not require the restrictive assumption on the existence of a control Lyapunov function before the corresponding control law is constructed. Furthermore, a finite upper bound on the settling time is estimated, which indicates that within the settling time, the system trajectory would arrive and stay at the origin thereafter. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approach.

The memristor has been extensively studied in electrical engineering and biological sciences as a means to compactly implement the synaptic function in neural networks. The cellular neural network (CNN) is one of the most implementable artificial neural network models and capable of massively parallel analog processing. In this paper, a novel memristive multilayer CNN (Mm-CNN) model is presented along with its performance analysis and applications. In this new CNN design, the memristor crossbar circuit acts as the synapse, which realizes one signed synaptic weight with a pair of memristors and performs the synaptic weighting compactly and linearly. Moreover, the complex weighted summation is executed in an efficient way with a proper design of Mm-CNN cell circuits. The proposed Mm-CNN has several merits, such as compactness, nonvolatility, versatility, and programmability of synaptic weights. Its performance in several image processing applications is illustrated through simulations.

In this paper, the consensus problem for linear multi-agent systems subject to non-uniform time-varying communication delays and jointly connected switching networks is investigated. Both distributed dynamic state feedback control law and distributed dynamic output feedback control law are proposed. By establishing some technical lemmas, it is shown that the proposed distributed control laws can solve the consensus problem.

This paper studies the consensus problem of multi-agent systems with general linear dynamics. We propose a novel event-triggered control scheme with some desirable features, namely, distributed, asynchronous, and independent. It is shown that consensus of the controlled multi-agent system can be reached asymptotically. The feasibility of the event-triggered strategy is further verified by the exclusion of both singular triggering and Zeno behavior. Moreover, a self-triggered algorithm is developed, where the next triggering time instant for each agent is determined based on its local information at the previous triggering time instant. Continuous monitoring of measurement errors is thus avoided. The effectiveness of the proposed control schemes is demonstrated by two examples.

This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems.

This paper considers the global robust output regulation problem of nonlinear output feedback systems in the case that both exosystem and high-frequency gain sign are unknown. The unavailability of these information poses challenges in the control law design. To solve the output regulation problem, a feedback controller is proposed by integrating the internal model principle, certainty equivalence adaptive control scheme and the Nussbaum gain technique.

In this paper, the universal integral sliding-mode controller problem for the general stochastic nonlinear systems modeled by Itô type stochastic differential equations is investigated. One of the main contributions is that a novel dynamic integral sliding mode control (DISMC) scheme is developed for stochastic nonlinear systems based on their stochastic T-S fuzzy approximation models. The key advantage of the proposed DISMC scheme is that two very restrictive assumptions in most existing ISMC approaches to stochastic fuzzy systems have been removed. Based on the stochastic Lyapunov theory, it is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time, and moreover, the stochastic stability of the sliding motion can be guaranteed in terms of linear matrix inequalities. Another main contribution is that the results of universal fuzzy integral sliding-mode controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy integral sliding-mode controllers, are provided, respectively. Simulation results from an inverted pendulum example are presented to illustrate the advantages and effectiveness of the proposed approaches.

In this paper, a novel dynamic integral sliding-mode control (ISMC) scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing ISMC approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities, and moreover, the sliding-mode controller can be obtained simultaneously. Simulation results from an inverted pendulum example illustrating the advantages and effectiveness of the proposed approaches are provided in the end.

This paper addresses the universal fuzzy integral sliding-mode controllers' problem for continuous-time multi-input multi-output nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. By using the approximation capability of T-S fuzzy models, the nonlinear systems are expressed by uncertain T-S fuzzy models with norm-bounded approximation errors. A novel fuzzy dynamic integral sliding-mode control (DISMC) scheme is then developed for the nonlinear systems based on their T-S fuzzy approximation models. One of the key features of the new DISMC scheme is that the restrictive assumption that all local linear systems share a common input matrix, which is required in most existing fuzzy integral sliding-mode control (ISMC) approaches, is removed. Furthermore, the results of universal fuzzy ISMCs for two classes of nonlinear systems, along with constructive procedures to obtain the universal fuzzy ISMCs, are provided, respectively. Finally, the advantages and effectiveness of the proposed approaches are illustrated via a numerical example.