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Internal model control based on a novel least square support vector machines for MIMO nonlinear discrete systems

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Abstract

To improve the robustness of the traditional inverse system method, the internal model control based on a novel least square support vector machines (LS-SVM) is proposed. The novel LS-SVM considers general errors that include noises of input variables and output variables as empirical errors. The data of original MIMO discrete system is exploited to approximate its inverse model by the novel LS-SVM. By cascading the inverse model and the original system to constitute a decoupling pseudo-linear system, the internal model control strategy is carried out to the pseudo-linear system to realize the effective control. Simulation validates that the novel LS-SVM used in the inverse system identification is effective and shows that the internal model control of nonlinear discrete systems has better robustness of anti-interference and parameters varying than that of the open-loop system only based on inverse control.

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Acknowledgments

This work has been supported by the National Natural Science Foundation of China (No. 60874013 and No. 60953001).

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Chaoxu Mu, Changyin Sun & Xinghuo Yu

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  1. Chaoxu Mu
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  2. Changyin Sun
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  3. Xinghuo Yu
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Correspondence to Changyin Sun.

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Mu, C., Sun, C. & Yu, X. Internal model control based on a novel least square support vector machines for MIMO nonlinear discrete systems. Neural Comput & Applic 20, 1159–1166 (2011). https://doi.org/10.1007/s00521-010-0468-3

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  • DOI: https://doi.org/10.1007/s00521-010-0468-3

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