Event-Based Adaptive Optimal Fault-Tolerant Consensus Control for Uncertain Nonlinear Multiagent Systems With Actuator Failures

This study explores fault-tolerant consensus in leader–following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler–Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

Observer-based reinforcement learning for optimal fault-tolerant consensus control of nonlinear multi-agent systems via a dynamic event-triggered mechanism

This paper investigates finite-horizon optimal consensus control problem for unknown multiagent systems with state delays. It is well known that optimal consensus control is the solutions to the coupled Hamilton-Jacobi-Bellman (HJB) equations. An off-policy reinforcement learning (RL) algorithm is developed to learn the two-stage optimal consensus solutions to the coupled time-varying HJB equations using the measurable state data instead of the knowledge of the state-delayed system dynamics. Subsequently, for each agent, a single critic neural network (NN) is utilized to approximate the time-varying cost function and help to calculate optimal consensus control policy. Based on the method of weighted residuals, adaptive weight update laws for the critic NNs are proposed. Finally, the simulation results are provided to illustrate the effectiveness of the proposed off-policy RL method.

In this paper, we described a class of nonlinear multi-agent dynamic systems in consensus problem which every agent is multi-dimensional dynamic system. The control objective is expect to find suitable consensus controller for every agent such that 1) guarantee all the signals in the dynamic systems remain bounded. 2) design the consensus controllers for every agent then the average-consensus behavior of the multi-agent systems can be obtained.

This paper deals with the optimal bipartite consensus control (OBCC) for heterogeneous multi-agent systems (MASs) with time-delay based on reinforcement learning (RL) scheme. MASs, denoted by communication network, is modeled as a signed graph, and the competitive (cooperative) relationship is represented by negative (positive) edges. Firstly, Heterogeneous MASs is depicted by cooperative and competitive (hereinafter referred to as coopetition) network, and local neighborhood bipartite consensus errors and their performance index function (PIF) are put forward to formulate OBCC problem. Secondly, a proper model reduction method is developed to tackle time-delay in coopetition network, and then MASs with time-delay can be transformed into a delay-free MASs. Thirdly, a RL algorithm is employed to solve the solution of Hamilton-Jacobi-Bellman (HJB) equations under OBCC laws, and substantially we adopt neural networks (NNs) to calculate the control policy and PIF online. Finally, the pivotal simulation validates availability of proposed algorithm.

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.

This paper investigates adaptive fuzzy output feedback fault-tolerant optimal control problem for a class of single-input and single-output nonlinear systems in strict feedback form. The considered nonlinear systems contain unknown nonaffine nonlinear faults and unmeasured states. Fuzzy logic systems are used to approximate cost function and unknown nonlinear functions, respectively. It is assumed that the states of the systems to be controlled are unmeasurable, thus an adaptive state observer is developed. To solve the nonaffine nonlinear fault control design problem, filtered signals are introduced into the adaptive backstepping control design procedures, and in the framework of adaptive critic technique and fault-tolerant control technique, a novel adaptive fuzzy fault-tolerant optimal control scheme is developed. The stability of the closed-loop system is proved by using Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control strategy.

In this paper, a novel NN-based optimal adaptive consensus-based formation control scheme over finite horizon is presented for networked mobile robots or agents in the presence of uncertain robot/agent dynamics. The uncertain robot formation dynamics are approximated online by using an NN-based identifier and a suitable weight tuning law. In addition, a novel time-varying value function is derived by using the augmented error vector, which consists of the regulation and consensus-based formation errors of each robot. By using the value function approximation and the identified dynamics, the near optimal control input over finite horizon is derived. This finite horizon optimal control leads to a time-varying value function, which becomes the solution of the Hamilton–Jacobi–Bellman equation, and control input is approximated by a second NN with time-varying activation function. A novel weight update law for the NN value function is developed to tune the value function, satisfy the terminal constraint, and relax an initial admissible controller requirement. The Lyapunov stability method is utilized to demonstrate the consensus of the overall formation. Finally, simulation results are given to verify theoretical claims. Copyright © 2015 John Wiley & Sons, Ltd.

Q-learning solution for optimal consensus control of discrete-time multiagent systems using reinforcement learning

In this paper, optimal synchronization problem of multi-agent systems (MASs) with time-varying input delay is investigated. To remove the effect of input delay, the delayed MAS model is firstly transformed into a new discrete-time delay-free version. Then we establish an equivalence relation on the performance indexes of the two systems so as to formulate a new optimal consensus control problem of the transformed delay-free system. Based on the Bellman optimality theorem, we derive optimal consensus control policies from the coupled HJB equations. A policy iteration algorithm is introduced to learn the solutions to the HJB equations in real-time. To execute iterative ADP method, the critic neural networks (NN) are utilized to approximate the value functions and help to calculate the control policies. Finally, a numerical simulation is provided to show the effectiveness of the proposed approach.


 
 
 This paper presents an adaptive fault-tolerant control (FTC) scheme for leader-follower consensus control of uncertain mobile agents with actuator faults. A local FTC component is designed for each agent in the distributed system by using local measurements and certain information exchanged between neighboring agents. Each local FTC component consists of a fault detection module and a reconfigurable controller module comprised of a baseline controller and an adaptive fault-tolerant controller activated after fault detection. Under certain assumptions, the closed-loop system stability and leader-follower consensus properties of the distributed system are rigorously established. A simulation example is used to illustrate the effectiveness of the FTC method.
 
 

This paper investigates the optimal couple-group consensus control (OCGCC) for heterogeneous multi-agent systems (HeMASs) with completely unknown dynamics. The agents in HeMASs are divided into two groups according to order differences. Meanwhile, heterogeneous systems are transformed into homogeneous ones by adding virtual velocities. Then, a novel data-driven distributed control protocol for HeMASs is proposed based on policy gradient reinforcement learning (RL). The proposed algorithm is implemented asynchronously, and is specifically designed to address the issue of computational imbalance caused by individual differences among participants. It achieves this by constructing an actor-critic (AC) framework. The system’s learning efficacy is optimized using offline data sets. The convergence and stability are ensured by applying functional analysis and the Lyapunov stability theory. Finally, the effectiveness of the proposed algorithm is confirmed by various simulation examples.

Adaptive fully distributed consensus for a class of heterogeneous nonlinear multi-agent systems

This article studies the semi-global adaptive sampled-data formation control problem for a class of nonlinear multiagent systems. The nonlinear growth rate is not limited, which expands the applicable scope of the obtained results. To remove the assumption that the system matrix of the leader system can be obtained by all follower systems, a sampled-data adaptive distributed observer is proposed to estimate not only the state but also the system matrix of the leader system, which are measured only at discrete time. All the estimation errors of the observers tend to zero as time tends to infinity. Then, by using the estimated state, a sampled-data adaptive control law is designed to achieve the formation control practically. Finally, a simulation example is shown to demonstrate the feasibility of our result.

This paper studies the robust containment control problem for a class of nonlinear multi-agent systems in lower triangular form with parameter uncertainty. The agents are heterogenous with the particularity that the dynamics of the leaders are totally different from those of the followers. The interconnection topology is described by a general directed graph. Based on the distributed internal model, the robust containment control problem is first converted into the stabilisation problem for an augmented network. A distributed state feedback control is then proposed to solve the stabilisation problem of the subsequent augmented network by the block backstepping technique. Under the proposed control, all the followers' outputs will converge to the convex hull spanned by the leaders.