Robust Neuro-Adaptive Containment of Multileader Multiagent Systems With Uncertain Dynamics
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2019 IEEE. One typical reflection of our understanding on multiagent systems (MASs) is our ability to design the emergence mechanism responsible for their various cooperative behaviors. This paper is concerned with the cooperative robust containment control problem of multileader MASs subject to unknown nonlinear dynamics and external disturbances. Specifically, quasicontainment and asymptotic containment problems are, respectively, considered by using tools from neural network (NN) approximation theory and Lyapunov stability theory of nonsmooth systems. A new kind of containment controllers consisting of a linear local information-based feedback term, a neuroadaptive approximation term and a nonsmooth feedback term are designed to complete the goal of quasi-containment. Under the assumption that the subgraph depicting the coupling configuration among followers is detail-balanced and each follower can be influenced by at least one leader, it is proven that the containment error vector of the closed-loop MASs will be uniformly ultimately bounded if the control parameters of the proposed containment controllers are suitably designed. By introducing a pseudo ideal weighting matrix for NN approximator embedded at each follower, a novel class of containment controllers are further designed to precisely achieve asymptotic containment in the considered MASs where the Euclidean norm of containment error vector asymptotically converges to zero. At last, numerical simulations are given to verify the validity of these derived theoretical results.