Adaptive neural control for switched non-linear systems with multiple tracking error constraints

• Published in: IET signal processing Vol. 13; no. 3; pp. 330 - 337
• Main Authors: Tang, Li, Ma, Dan, Zhao, Jun
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.05.2019
• Subjects: active time‐interval; adaptive control; adaptive neural control problem; closed loop systems; closed‐loop system; common transition function; control system synthesis; different coordinate transformations; different update laws; dwell‐time method; inactive time‐interval; linear systems; Lyapunov methods; multiple tracking error constraints; neurocontrollers; nonlinear control systems; nonlinear system; radial basis function networks; radial basis function neural networks; Research Article; stability; switched nonlinear systems; switching signals; time‐varying systems; tracking errors; unknown functions; time-varying systems; unknown functions; different update laws; nonlinear system; common transition function; adaptive neural control problem; neurocontrollers; radial basis function networks; stability; switched nonlinear systems; multiple tracking error constraints; radial basis function neural networks; inactive time-interval; tracking errors; control system synthesis; adaptive control; linear systems; dwell-time method; switching signals; closed loop systems; active time-interval; nonlinear control systems; different coordinate transformations; closed-loop system; Lyapunov methods
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2018.5077

Here, an adaptive neural control problem for a switched non-linear system with multiple tracking error constraints is studied by using the dwell-time method. The unknown functions are approximated by radial basis function neural networks. In order to avoid the difficulty caused by the adoption of different coordinate transformations, a common transition function is selected. Moreover, different update laws are designed for both active time-interval and inactive time-interval of each subsystem. The proposed controllers and switching signals guarantee the stability of the closed-loop system and the boundedness of all signals. Furthermore, both transient-state and steady-state performances of the tracking errors are ensured. Finally, a simulation example is used to clarify the effectiveness of the proposed method.