Adaptive neural network tracking of a class of switched nonlinear systems with time-varying output constraints

• Published in: International journal of control, automation, and systems Vol. 15; no. 3; pp. 1425 - 1433
• Main Authors: Lee, Seung Woo, Kim, Hyoung Oh, Yoo, Sung Jin
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.06.2017; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Adaptive control; Approximation; Closed loop systems; Computer simulation; Control; Effectiveness; Engineering; Feedback; Liapunov functions; Mechatronics; Neural networks; Nonlinear systems; Output; Regular Papers; Robotics; Simulation; Stability criteria; Studies; Switching theory; Tracking; Transformations (mathematics); 제어계측공학; Switched nonlinear systems; arbitrary switching; time-varying output constraints; neural networks
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-016-0339-5

An approximation-based adaptive design problem for output-constrained tracking of a class of switched pure-feedback nonlinear systems is investigated under arbitrary switchings. All switched nonlinearities are assumed to be unknown. Contrary to the existing control results for uncertain switched pure-feedback nonlinear systems where the number of the used function approximators should be equal to the order of the systems, an adaptive control scheme based on only two neural networks is designed by using a system transformation and the common Lyapunov function method, regardless of the order of the system. In the proposed controller, the output constraints are used to establish designable time-varying bounds on the tracking performance. The stability and the constraint satisfaction of the resulting closed-loop system are shown in the sense of Lyapunov stability criterion. Finally, simulation examples are provided to illustrate the effectiveness of the proposed methodology.