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Nonlinear Systems Identification and Control Using Uncertain Rule-based Fuzzy Neural Systems with Stable Learning Mechanism

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Abstract

This paper proposes an uncertain rule-based fuzzy neural system (UFNS-S) with stable learning mechanism for nonlinear systems identification and control. The proposed UFNS-S system not only preserves the ability of handling uncertain information but also performs less computational effort. The sinusoidal perturbations are adopted to combine with the fuzzy term sets of UFNS-S. For training the UFNS-S systems on system identification and control applications, the gradient descent method with adaptive learning rate is derived. This guarantees the convergence of UFNS-S by choosing adaptive learning rates which enhance the convergent speed. This provides a simple way for choosing the learning rates for training the UFNS-S which also guarantees convergence and faster learning. Finally, the effectiveness and performance of the proposed approach is illustrated by several examples, computational complexity analysis, nonlinear system identification, and tracking control of two-link robot manipulator system.

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Acknowledgments

The authors would like to thank anonymous reviewers for their insightful comments and valuable suggestions. This work was supported in part by the National Science Council, Taiwan, R.O.C., under contracts MOST-100-2221-E-005-093-MY2, 104-3011-E-005-001, 103-2218-E-005-005-MY2, and 102-2221-E-005-095-MY2.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Chung Hsing University, Taichung, 40227, Taiwan, ROC

    Ching-Hung Lee

  2. Department of Electrical Engineering, Yuan Ze University, Taoyuan, 32003, Taiwan, ROC

    Yi-Han Lee & Chih-Min Lin

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  1. Ching-Hung Lee
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  3. Chih-Min Lin
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Correspondence to Ching-Hung Lee.

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Lee, CH., Lee, YH. & Lin, CM. Nonlinear Systems Identification and Control Using Uncertain Rule-based Fuzzy Neural Systems with Stable Learning Mechanism. Int. J. Fuzzy Syst. 19, 470–488 (2017). https://doi.org/10.1007/s40815-016-0170-4

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  • DOI: https://doi.org/10.1007/s40815-016-0170-4

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