Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Abstract

AbstractThis article presents a distributed adaptive containment control strategy using quantized state feedback and communication for uncertain multiple‐input‐multiple‐output pure‐feedback nonlinear multi‐agent systems with state quantization. It is assumed that quantized state variables of multiple followers and quantized outputs of multiple dynamic leaders are only available for constructing local adaptive feedback controllers of followers under a directed network. Compared with existing containment control approaches, the primary contribution of this study is dealing with discontinuously quantized state feedback and communication problems for containment control design in the presence of unknown nonaffine nonlinearities. Based on quantized state and communication information, local adaptive neural network trackers of followers and their adaptive tuning laws are developed to guarantee that the trajectories of all followers converge to the dynamic convex hull spanned by the multiple leaders regardless of unknown pure‐feedback nonlinearities. By analyzing the stability of the local quantization errors between quantized signals and original signals, we prove the boundedness of all closed‐loop signals and the convergence of the containment errors to a sufficiently small domain around the origin. Simulation examples involving flexible‐joint manipulators are presented to validate the proposed quantized feedback scheme.