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Adaptive Predictive PID Control Using Fuzzy Wavelet Neural Networks for Nonlinear Discrete-Time Time-Delay Systems

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Abstract

This paper presents a novel adaptive predictive proportional–integral–derivative (PID) control using fuzzy wavelet neural networks (FWNN) for a kind of highly nonlinear discrete-time system with time delay. The proposed controller, abbreviated as FWNN-APPID, is composed of an adaptive predictive PID controller with abilities of accurate tracking and disturbance rejection and an FWNN identifier with online parameter tuning and estimation. Several simulations for controlling a highly nonlinear time-delay process show constant disturbances rejection and its performance of setpoint tracking for the proposed FWNN-APPID control method, thus clearly showing its effectiveness and merit. Experimental results on a real PET stretch blow molding machine show the applicability of the proposed method.

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Acknowledgements

The authors gratefully acknowledge financial support from the Ministry of Science and Technology (MOST), the Republic of China, under contract MOST 104-2221-E-005-054-MY2.

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Authors and Affiliations

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

    Ching-Chih Tsai, Feng-Chun Tai, Ya-Ling Chang & Chia-Ta Tsai

  2. Department of Refrigeration, Air-conditioning and Energy Engineering, National Chin-Yi University of Technology, Taichung, Taiwan, ROC

    Ya-Ling Chang

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  1. Ching-Chih Tsai
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  2. Feng-Chun Tai
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  4. Chia-Ta Tsai
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Correspondence to Ching-Chih Tsai.

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Tsai, CC., Tai, FC., Chang, YL. et al. Adaptive Predictive PID Control Using Fuzzy Wavelet Neural Networks for Nonlinear Discrete-Time Time-Delay Systems. Int. J. Fuzzy Syst. 19, 1718–1730 (2017). https://doi.org/10.1007/s40815-017-0405-z

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