Adaptive Prescribed Performance Control for Switched MIMO Uncertain Nonlinear Systems

In this paper, a novel adaptive neural control technique for a class of switched multi-input multi-output (MIMO) uncertain nonlinear systems, capable of guaranteeing prescribed performance, is established by exploiting the classical average dwell time (ADT) method. Neural networks are used to approximate the unknown nonlinear functions. A common output error transformation for different subsystems is introduced to convert the original "constrained" switched system into an equivalent "unconstrained" one. It is proved that stabilizing the "unconstrained" switched system is sufficient to achieve prescribed performance guarantees based on an improved ADT method. The controllers of subsystems are designed to guarantee prescribed bounds on the transient and steady-state performance of the output tracking errors, plus the boundedness of all other signals in the resulting closed-loop system under a class of switching signals with average dwell time.