Integral variable structure control of nonlinear system using a CMAC neural network learning approach

Chin-Pao Hung

doi:10.1109/tsmcb.2003.811768

Integral variable structure control of nonlinear system using a CMAC neural network learning approach

IEEE Trans Syst Man Cybern B Cybern. 2004 Feb;34(1):702-9. doi: 10.1109/tsmcb.2003.811768.

Author

Chin-Pao Hung¹

Affiliation

¹ Department of Electrical Engineering, National Chin-Yi Institute of Technology, Taiping City, Taiwan, ROC. c.p.hung@seed.net.tw

PMID: 15369111
DOI: 10.1109/tsmcb.2003.811768

Abstract

This work presents a novel integral variable structure control (IVSC) that combines a cerebellar model articulation controller (CMAC) neural network and a soft supervisor controller for use in designing single-input single-output (SISO) nonlinear system. Based on the Lyapunov theorem, the soft supervisor controller is designed to guarantee the global stability of the system. The CMAC neural network is used to perform the equivalent control on IVSC, using a real-time learning algorithm. The proposed IVSC control scheme alleviates the dependency on system parameters and eliminates the chattering of the control signal through an efficient learning scheme. The CMAC-based IVSC (CIVSC) scheme is proven to be globally stable inasmuch all signals involved are bounded and the tracking error converges to zero. A numerical simulation demonstrates the effectiveness and robustness of the proposed controller.