Adaptive neural control for switched non‐linear systems with multiple tracking error constraints. Issue 3 (1st May 2019)

• Record Type: Journal Article
• Authors: Tang, Li; Ma, Dan; Zhao, Jun
• Is Part Of: IET signal processing. Volume 13:Issue 3(2019)
• Page Start: 330
• Page End: 337
• Publication Date: 2019-05-01
• Subjects: linear systems -- stability -- Lyapunov methods -- control system synthesis -- time‐varying systems -- adaptive control -- radial basis function networks -- nonlinear control systems -- neurocontrollers -- closed loop systems; tracking errors -- switched nonlinear systems -- multiple tracking error constraints -- adaptive neural control problem -- nonlinear system -- dwell‐time method -- unknown functions -- radial basis function neural networks -- different coordinate transformations -- common transition function -- different update laws -- active time‐interval -- inactive time‐interval -- switching signals -- closed‐loop system; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2018.5077
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : 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.