Adaptive Neural Network-Based Finite-Time Tracking Control for Nonstrict Nonaffined MIMO Nonlinear Systems

Abstract

An adaptive neural network (NN)-based finite-time tracking control method is presented for the nonstrict nonaffined nonlinear multi-input-multi-output systems. The hardship of this article is that each subsystem responses to all input variables and any other subsystems of the whole system. Moreover, the uncertainty of the input transition matrix further soars the difficulty of controller design. In this article, NNs are used to approximate these functions with uncertainty automatically. Based on the Lyapunov stability theory, the controller we designed has proven to be semiglobal finite-time stable, implying that all the tracking errors converge to a small neighborhood of the original states in finite time, and the closed-loop system is semiglobal practical finite-time stable. At last, a simulation example is applied to verify the effectiveness of the proposed control algorithm.