Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism

Science China Information Sciences volume 60, Article number: 058201 (2017) Cite this article

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61233001, 61273140, 61304018, 61304086, 61533017, U1501251), Beijing Natural Science Foundation (Grant No. 4162065), Tianjin Natural Science Foundation (Grant No. 14JCQNJC05400), Early Career Development Award of SKLMCCS, and Research Fund of Tianjin Key Laboratory of Process Measurement and Control (Grant No. TKLPMC-201612).

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Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Ding Wang

  2. Tianjin Key Laboratory of Process Measurement and Control, School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Ding Wang & Chaoxu Mu

Authors
  1. Ding Wang
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  2. Chaoxu Mu
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Correspondence to Chaoxu Mu.

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The authors declare that they have no conflict of interest.

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Wang, D., Mu, C. Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism. Sci. China Inf. Sci. 60, 058201 (2017). https://doi.org/10.1007/s11432-016-9022-1

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