Observer and Command-Filter-Based Adaptive Neural Network Control Algorithms for Nonlinear Multi-agent Systems with Input Delay

doi:10.1007/s12559-021-09959-x

2022-01-18

PRE

Lidan Ma

Xin Wang *

Yuhao Zhou

DOI：10.1007/s12559-021-09959-x

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摘要

En 中文

Over the last decades, many researchers have investigated the distributed adaptive consensus tacking control algorithm of multi-agent systems (MASs). Nevertheless, the existing works involving the command-filter-based adaptive consensus problem for nonlinear multi-agent systems subjected to the unmeasurable states are relatively few. Besides that, the immeasurable states and the input delay will bring few challenging in dealing with the consensus problem for MASs. (1) The radial basis function neural networks (RBF NNs) are utilized to approximate the unknown nonlinear functions and the NN-based observer is established to copy with the unmeasurable states. (2) The backstepping design method of distributed adaptive consensus control is put forward on basis of the command filtering method, which overcomes the complexity explosion problem and eliminates errors by introducing compensation signals. (3) The Pade approximation approach is served to remove the obstacle originating from the input delay. This paper addresses the observer and command-filter-based adaptive tracking control problem for nonlinear multi-agent systems with the unmeasurable states and input delay under the directed graph. The Lyapunov stability theory is utilized to prove that the proposed approach can ensure that all signals in the closed-loop system reach cooperatively semi-globally uniformly ultimately bounded (CSUUB). The simulation result is presented, and it further manifests that the effectiveness of this scheme.

Keyword:

Tracking control

Nonlinear multi-agent systems

Unmeasurable states

Input delay

Command filtering