IEEE Trans Neural Netw Learn Syst - Distributed Containment Control for Multiple Unknown Second-Order Nonlinear Systems With Application to Networked Lagrangian Systems.

Tópicos

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Resumo

In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors' velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

Resumo Limpo

paper consid distribut contain control problem multiag system unknown nonlinear dynam specif focus multipl secondord nonlinear system network lagrangian system first studi distribut contain control problem multipl secondord nonlinear system multipl dynam leader presenc unknown nonlinear extern disturb general direct graph character interact among leader follow distribut adapt control algorithm adapt gain design base approxim capabl neural network propos present necessari suffici condit direct graph contain error can reduc small desir byproduct leaderless consensus problem solv asymptot converg relat veloc measur neighbor general difficult obtain relat posit measur propos distribut contain control algorithm without use neighbor veloc inform twostep lyapunovbas method use studi converg closedloop system next appli idea deal contain control problem network unknown lagrangian system general direct graph propos algorithm distribut can implement use local measur absenc communic final simul exampl provid show effect propos control algorithm

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