Letters: Multilayer neural networks-based direct adaptive control for switched nonlinear systems

Authors:
Lei Yu;Shumin Fei;Fei Long;Maoqing Zhang;Jiangbo Yu
Affiliations:
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, PR China and School of Automation, Southeast University,Na ...;Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, PR China and School of Automation, Southeast University,Na ...;Institute of Intelligent Information Processing, Guizhou University, 550025 Guiyang, PR China;Institute of Mechanical and Electrical Engineering, Soochow University, 215021 Suzhou, PR China;Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, PR China and School of Automation, Southeast University,Na ...
Venue:
Neurocomputing
Year:
2010

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Abstract

This paper is concerned to present a direct adaptive neural control scheme for switched nonlinear systems with unknown constant control gain. Multilayer neural networks (MNNs) are used as a tool for modeling nonlinear functions up to a small error tolerance. The adaptive updated laws have been derived from the switched multiple Lyapunov function method, also an admissible switching signal with average dwell-time technique is given. It is proved that the resulting closed-loop system is asymptotically Lyapunov stable such that the output tracking error performance is well obtained. Finally, a simulation example of two Duffing forced-oscillation systems is given to illustrate the effectiveness of the proposed control scheme.