Leaderless and Leader-Following Bipartite Consensus of Multiagent Systems With Sampled and Delayed Information

Human-in-the-Loop Consensus Control for Multiagent Systems With External Disturbances

In this article, the human-in-the-loop leader-follower consensus control problem is addressed for multiagent systems (MASs) with unknown external disturbances. A human operator is deployed to monitor the MASs' team by transmitting an execution signal to a nonautonomous leader in response to any hazard detected, with the control input of the leader unknown to all followers. For each follower, a full-order observer, in which the observer error dynamic system decouples the unknown disturbance input, is designed for asymptotic state estimation. Then, an interval observer is constructed for the consensus error dynamic system, where the unknown disturbances and control inputs of its neighbors and its disturbance are treated as unknown inputs (UIs). To process the UIs, a new asymptotic algebraic UI reconstruction (UIR) scheme is proposed based on the interval observer, and one of the significant features of the UIR is the capacity to decouple the control input of the follower. The subsequent human-in-the-loop asymptotic convergence consensus protocol is developed by applying an observer-based distributed control strategy. Finally, the proposed control scheme is validated through two simulation examples.

Distributed disturbance observer-based nonfragile bipartite consensus of nonlinear multiagent systems with multiple time-varying delays

The paper concentrates on the issue of distributed disturbance observer-based nonfragile bipartite consensus within nonlinear delayed multiagent systems, encompassing both leaderless and leader-following structures. The delays under consideration are nonuniform, manifesting in the state, the nonlinearity, and the communication processes. To suppress the external disturbances and the observer gain perturbations, distributed nonfragile disturbance observers pertaining to relative output and communication delays are developed to estimate the external disturbances for each agent. Employing the developed disturbance observer, distributed control protocols for nonfragile bipartite consensus are constructed incorporating states, estimated disturbances, and communication delays. These protocols can ensure bipartite consensus, compensate for external disturbances, and tolerate uncertainties in control gain. New augmented Lyapunov-Krasovskii functions are formulated by introducing the triple integral term and the augmented vector. The new bipartite consensus criteria for the studied multiagent systems are established with less conservatism by employing the techniques on second-order Bessel-Legendre integral inequality, reciprocally convex combination, and free weight matrix. Finally, numerical simulations and comparisons are performed for both leaderless and leader-following scenarios, thereby validating and enhancing the theoretical outcomes.

Data-Driven Optimal Bipartite Consensus Control for Second-Order Multiagent Systems via Policy Gradient Reinforcement Learning

This article investigates the optimal bipartite consensus control (OBCC) problem for unknown second-order discrete-time multiagent systems (MASs). First, the coopetition network is constructed to describe the cooperative and competitive relationships between agents, and the OBCC problem is proposed by the tracking error and related performance index function. Based on the distributed policy gradient reinforcement learning (RL) theory, a data-driven distributed optimal control strategy is obtained to guarantee the bipartite consensus of all agents' position and velocity states. In addition, the offline data sets ensure the learning efficiency of the system. These data sets are generated by running the system in real time. Besides, the designed algorithm is an asynchronous version, which is essential to solve the challenge caused by the computational ability difference between nodes in MASs. Then, by means of the functional analysis and Lyapunov theory, the stability of the proposed MASs and the convergence of the learning process are analyzed. Furthermore, an actor-critic structure containing two neural networks is used to implement the proposed methods. Finally, a numerical simulation shows the effectiveness and validity of the results.