Observed-based adaptive finite-time tracking control for a class of nonstrict-feedback nonlinear systems with input saturation

• Published in: Journal of the Franklin Institute Vol. 357; no. 16; pp. 11518 - 11544
• Main Authors: Ma, Li, Zong, Guangdeng, Zhao, Xudong, Huo, Xin
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.11.2020; Elsevier Science Ltd
• Subjects: Adaptive control; Control methods; Control stability; Dynamic stability; Feedback control; Feedback control systems; Finite element analysis; Neural networks; Nonlinear control; Nonlinear systems; Nonlinearity; Saturation; Stability analysis; Tracking control; Tracking control systems
• ISSN: 0016-0032; 1879-2693; 0016-0032
• DOI: 10.1016/j.jfranklin.2019.07.021

This paper concentrates upon the problem of adaptive neural finite-time tracking control for uncertain nonstrict-feedback nonlinear systems with input saturation. The design difficulty of non-smooth input saturation nonlinearity is solved by applying a smooth non-affine function to approximate the saturation signal. Neural networks, as a kind of specialized function estimators, are used to estimate the uncertain function. Meanwhile, a neural network-based observer is constructed to observe the unavailable states, and thus an observer-based adaptive finite-time tracking control strategy is developed by combining dynamic surface control (DSC) technique and backstepping approach. Furthermore, the stability of the considered system is analyzed via semi-global practical finite-time stability theory. Under the proposed control method, all the signals in the closed-loop system are bounded, and the system output can almost surely track the desired trajectory within a specified bounded error in a finite time. In the end, two examples are adopted to illustrate the validity of our results.