Time-varying asymmetrical BLFs based adaptive finite-time neural control of nonlinear systems with full state constraints

Abstract

This paper concentrates on asymmetric barrier Lyapunov functions &#x0028 ABLFs &#x0029 based on finite-time adaptive neural network &#x0028 NN &#x0029 control methods for a class of nonlinear strict feedback systems with time-varying full state constraints. During the process of backstepping recursion, the approximation properties of NNs are exploited to address the problem of unknown internal dynamics. The ABLFs are constructed to make sure that the time-varying asymmetrical full state constraints are always satisfied. According to the Lyapunov stability and finite-time stability theory, it is proven that all the signals in the closed-loop systems are uniformly ultimately bounded &#x0028 UUB &#x0029 and the system output is driven to track the desired signal as quickly as possible near the origin. In the meantime, in the scope of finite-time, all states are guaranteed to stay in the pre-given range. Finally, a simulation example is proposed to verify the feasibility of the developed finite time control algorithm.