Adaptive Neural Network Control for a Class of Fractional-Order Nonstrict-Feedback Nonlinear Systems With Full-State Constraints and Input Saturation

作者：Changhui Wang, Limin Cui, Mei Liang, Jialin Li, Yantao Wang · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2021 · DOI：10.1109/tnnls.2021.3082984 · 被引用次数：67 · 研究领域：Adaptive Control of Nonlinear Systems、Advanced Control Systems Design、Iterative Learning Control Systems

This article addresses an adaptive neural network (NN) constraint control scheme for a class of fractional-order uncertain nonlinear nonstrict-feedback systems with full-state constraints and input saturation. The radial basis function (RBF) NNs are used to deal with the algebraic loop problem from the nonstrict-feedback formation based on the approximation structure. In order to overcome the problem of input saturation nonlinearity, a smooth nonaffine function is applied to approach the saturation function. To arrest the violation of full-state constraints, the barrier Lyapunov function (BLF) is introduced in each step of the backstepping procedure. By using the fractional-order Lyapunov stability theory and the given conditions, it proves that all the states remain in their constraint bounds, the tracking error converges to a bounded compact set containing the origin, and all signals in the closed-loop system are ensured to be bounded. Finally, the effectiveness of the proposed control scheme is verified by two simulation examples.