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Nonlinear generalized predictive control based on online least squares support vector machines

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Abstract

For a class of nonlinear discrete systems with unknown parameters, an adaptive direct generalized predictive control method based on online least squares support vector machines (OLS–SVM) is proposed. In the method, the OLS–SVM is used to design the controller directly, and an improved projection algorithm based on the tracking error is introduced to adjust the weights of the OLS–SVM adaptively, so the inverse matrix is avoided in the process of online real-time control. It is proved that the proposed method can make the tracking error converge to a small neighborhood of the origin. Simulation results have shown the correctness and effectiveness of the proposed method.

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Acknowledgments

This work was supported by National Science Foundation of China (61374108); The Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province Grant of China (BS2012DX007, BS2011DX006); The Shandong Provincial Natural Science Foundation, China (ZR2011FM016); The General Program of Shanghai Postdoctoral Research Project of China (12R21414300).

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Authors and Affiliations

  1. School of Mathematics and Statistics Science, Ludong University, Yantai, 264025, China

    Zhenkai Guo

  2. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China

    Zhenkai Guo & Xinping Guan

  3. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

    Zhenkai Guo & Xinping Guan

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  1. Zhenkai Guo
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  2. Xinping Guan
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Correspondence to Zhenkai Guo.

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Guo, Z., Guan, X. Nonlinear generalized predictive control based on online least squares support vector machines. Nonlinear Dyn 79, 1163–1168 (2015). https://doi.org/10.1007/s11071-014-1733-2

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  • DOI: https://doi.org/10.1007/s11071-014-1733-2

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