Uncertain Nonlinear Multi-agent Systems Research Articles

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Event-triggered adaptive tracking for constrained multi-agent systems with saturated inputs and actuator faults

An fault-tolerant tracking control of uncertain nonlinear multi-agent systems with state constraints and input saturation is investigated. A saturated controller is processed by converting the saturation function into a linear form of the control input. The uncertain nonlinear dynamics are approximated by fuzzy logic systems and the unknown parameters are estimated by adaptive laws. By utilizing the nonlinear state-dependent function, the constrained system is converted into the unconstrained one. An adaptive distributed event-triggered control mechanism for consensus tracking is developed utilizing the backstepping approach. By stability analysis, it is ensured that all system signals are bounded, consensus tracking can be achieved with bounded errors, and no Zeno behavior happens. Finally, simulation is done to validate the theoretical results.

Time-varying formation of uncertain nonlinear multi-agent systems via adaptive feedback control approach with event-triggered impulsive estimator

The time-varying formation problem under directed topology is investigated for the uncertain nonlinear multi-agent systems (MASs) via the neural networks-based adaptive feedback control (NNAFC). Compared with the simple adaptive control, the designed control protocol consists of two parts: NNAFC and the event-triggered impulsive estimator. To save communication costs, communications among followers are only carried out at discrete instants determined by the event-triggered impulsive rule. Two kinds of estimators and controllers are designed for two cases with or without communication delay, respectively. Further, NNAFC can be further improved to eliminate errors due to uncertain function approximation for some special cases. Utilizing the Lyapunov stability theory and impulsive control theory, we obtain some sufficient conditions to implement time-varying formation task. Finally, three examples are given to show the feasibility of theoretical analysis.

Adaptive Fuzzy Distributed Optimization for Uncertain Nonlinear Multiagent Systems

Adaptive Fuzzy Distributed Optimization for Uncertain Nonlinear Multiagent Systems

Adaptive Output Synchronization for a Class of Uncertain Nonlinear Multi-Agent Systems Over Switching Networks

Adaptive Output Synchronization for a Class of Uncertain Nonlinear Multi-Agent Systems Over Switching Networks

Nonsingular adaptive finite-time consensus control for uncertain nonlinear multi-agent systems with input quantization

Nonsingular adaptive finite-time consensus control for uncertain nonlinear multi-agent systems with input quantization

Low-Complexity Control With Funnel Performance for Uncertain Nonlinear Multiagent Systems

Low-Complexity Control With Funnel Performance for Uncertain Nonlinear Multiagent Systems

Adaptive Fixed-Time Optimal Formation Control for Uncertain Nonlinear Multiagent Systems Using Reinforcement Learning

Adaptive Fixed-Time Optimal Formation Control for Uncertain Nonlinear Multiagent Systems Using Reinforcement Learning

Cooperative Control of Multiagent Systems: A Quantization Feedback-Based Event-Triggered Approach.

This article addresses the synchronization tracking problem for high-order uncertain nonlinear multiagent systems via intermittent feedback under a directed graph. By resorting to a novel storer-based triggering transmission strategy in the state channels, we propose an event-triggered neuroadaptive control method with quantitative state feedback that exhibits several salient features: 1) avoiding continuous control updates by making the parameter estimations updated intermittently at the trigger instants; 2) resulting in lower-frequency triggering transmissions by using one event detector to monitor the triggering condition such that each agent only needs to broadcast information at its own trigger times; and 3) saving communication and computation resources by designing the intermittent updating of neural network weights using a dual-phase technique during the triggering period. Besides, it is shown that the proposed scheme is capable of steering the tracking/disagreement errors into an adjustable neighborhood close to the origin, and the existence of a strictly positive dwell time is proved to circumvent Zeno behavior. Both theoretical analysis and numerical simulation authenticate and validate the efficiency of the proposed protocols.

Adaptive consensus with limited information for uncertain nonlinear multi-agent systems

This paper investigates the adaptive consensus with limited information for a class of uncertain nonlinear multi-agent systems (MASs). The limited information, meaning the incomplete information transmitted between agents, stems from nonzero-kernel weight matrices of communication networks, which deserves concerns owing to its practical interest such as in privacy preserving and information compression. Apart from the limited/incomplete information, the paper considers nonlinear MASs with large uncertainties (without known upper bounds). This is essentially different from the existing results of linear systems and poses challenges for the design and analysis of adaptive consensus. As for fully distributed protocol design, dynamic high gains are introduced to compensate for the system uncertainties and for the unavailable global graph information. In closed-loop consensus analysis, a new Lyapunov function is constructed by utilising weight and incidence matrices instead of Laplacian matrix. It turns out that the designed protocol can guarantee the boundedness of closed-loop MASs signals and the convergent consensus. Two numerical examples are given to demonstrate the effectiveness of the proposed control scheme.

Security-based distributed fuzzy funnel cooperative control for uncertain nonlinear multi-agent systems against DoS attacks

This article focuses on the distributed funnel cooperative control of nonlinear multi-agent systems suffering denial-of-service (DoS) attacks, which aim to disrupt communication links between agents and can lead to discontinuities in system outputs. First, an actual measurement model of output signals is given, and a fuzzy switching observer is designed by utilizing the practical output model to obtain the unknown states. Second, a funnel-based tracking control strategy is presented by skillfully blinding the funnel function and backstepping technique to defend against DoS attacks. A crucial stability criterion is established by combining an improved average dwell time (ADT) approach and Lyapunov stability theory that integrates the constraints imposed by DoS attacks. In particular, based on an appropriate error transformation, the developed control law has a simpler structure than existing barrier function methods to solve performance constraints. It guarantees that the output of each follower agent yi is able to track the reference signal yd, and synchronization errors ϵi=∑s=1Mai,s(yi−ys)+bi(yi−yd) are consistently confined within a prearranged performance funnel Fϕ, (that is, (t,ϵi(t))∈Fϕ,∀t). Ultimately, a simulation example is presented to prove the validity of the established solution.

Distributed Adaptive Formation Tracking for a Class of Uncertain Nonlinear Multiagent Systems: Guaranteed Connectivity Under Moving Obstacles.

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static or dynamic objects as obstacles. The nonlinear error variables for formation tracking and collision avoidance are presented, and the auxiliary signals in formation tracking errors are introduced to maintain network connectivity under the avoidance mechanism. The adaptive formation controllers using command-filtered backstepping are constructed to ensure closed-loop stability with collision avoidance and preserved connectivity. Compared with the previous formation results, the resulting features are as follows: 1) the nonlinear error function for the avoidance mechanism is considered an error variable, and an adaptive tuning mechanism for estimating the dynamic obstacle velocity is derived in a Lyapunov-based control design procedure; 2) network connectivity during dynamic obstacle avoidance is preserved by constructing the auxiliary signals; and 3) owing to neural networks-based compensating variables, the bounding conditions of time derivatives of virtual controllers are not required in the stability analysis.

Predefined-Time Output-Feedback Leader-Following Consensus of Pure-Feedback Multiagent Systems.

This article delves into the predefined-time output-feedback leader-following consensus problem of uncertain pure-feedback nonlinear multiagent systems for the first time. To streamline subsequent design, the original systems in pure-feedback form are first transformed into canonical systems. Following this, a distributed predefined-time extended state observer (ESO) and a local predefined-time ESO are developed to reconstruct the unknown states/lumped disturbance of the transformed leader system and follower systems, respectively. Based on the estimated states and utilizing a bounded regulation function, two nonsingular and nonconservative predefined-time control laws are formulated to achieve consensus tracking. The proposed method showcases the following advantages: 1) the actual convergence time rather than the upper bound of the convergence time (UBCT) of the tracking errors can be explicitly specified a priori regardless of the initial conditions in a bounded region, optimizing control energy usage and 2) the system overshoot could be effectively reduced by selecting appropriate parameters for the regulation function. Finally, numerical examples are conducted to verify the obtained results.

Event-Triggered Distributed Adaptive Leaderless Consensus of Uncertain Heterogenous Nonlinear Multi-Agent Systems

Event-Triggered Distributed Adaptive Leaderless Consensus of Uncertain Heterogenous Nonlinear Multi-Agent Systems

Neuroadaptive Prescribed-Time Consensus of Uncertain Nonlinear Multi-Agent Systems

Neuroadaptive Prescribed-Time Consensus of Uncertain Nonlinear Multi-Agent Systems

Cooperative Fault-Tolerant Control for a Class of Nonlinear MASs by Resilient Learning Approach.

In this article, a learning-based resilient fault-tolerant control method is proposed for a class of uncertain nonlinear multiagent systems (MASs) to enhance the security and reliability against denial-of-service (DoS) attacks and actuator faults. With the framework of cooperative output regulation, the developed algorithm consists of designing a distributed resilient observer and a decentralized fault-tolerant controller. Specifically, by using the data-driven method, an online resilient learning algorithm is first presented to learn the unknown exosystem matrix in the presence of DoS attacks. Then, a distributed resilient observer is proposed working against DoS attacks. In addition, based on the developed observer, a decentralized adaptive fault-tolerant controller is designed to compensate for actuator faults. Moreover, the convergence of error systems is shown by using the Lyapunov stability theory. The effectiveness of our result is examined by a simulation example.

Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method.

A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step n and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.

Fuzzy Fixed-time Event-triggered Consensus Control for Uncertain Nonlinear Multi-Agent Systems with Memory based Learning

Fuzzy Fixed-time Event-triggered Consensus Control for Uncertain Nonlinear Multi-Agent Systems with Memory based Learning

Neural Network-based Distributed Generalized Nash Equilibrium Seeking for Uncertain Nonlinear Multi-agent Systems

Neural Network-based Distributed Generalized Nash Equilibrium Seeking for Uncertain Nonlinear Multi-agent Systems

Low Complexity Distributed Synchronization of Uncertain Nonlinear Multi-Agent Systems With Global Funnel Performance

Low Complexity Distributed Synchronization of Uncertain Nonlinear Multi-Agent Systems With Global Funnel Performance