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Neuro-control of Nonlinear Systems with Unknown Input Constraints

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10634))

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Abstract

This paper establishes an adaptive dynamic programming algorithm based neuro-control scheme for nonlinear systems with unknown input constraints. The control strategy consists of an online nominal optimal control and a neural network (NN) based saturation compensator. For nominal systems without input constraints, we develop a critic NN to solve the Hamilton-Jacobi-Bellman equation. Hereafter, the online approximate nominal optimal control policy can be derived directly. Then, considering the unknown input constraints as saturation nonlinearity, NN based feed-forward compensator is employed. The ultimate uniform bounded stability of the closed loop system is analyzed via Lyapunov’s direct method. Finally, simulation on a torsional pendulum system is provided to verify the effectiveness of the proposed control scheme.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 61603387, U1501251, 61533017, 61773075, 61374051, 61374105 and 61503379, in part by the Scientific and Technological Development Plan Project in Jilin Province of China under Grants 20150520112JH and 20160414033GH, and in part by Beijing Natural Science Foundation under Grant 4162065.

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Authors and Affiliations

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

    Bo Zhao

  2. The Bureau of Informationization Development, Cyberspace Administration of China, Beijing, 100044, China

    Xinliang Liu

  3. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Derong Liu

  4. Department of Control Science and Engineering, Changchun University of Technology, Changchun, 130012, China

    Yuanchun Li

Authors
  1. Bo Zhao
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  2. Xinliang Liu
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  3. Derong Liu
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  4. Yuanchun Li
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Corresponding author

Correspondence to Xinliang Liu .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Zhao, B., Liu, X., Liu, D., Li, Y. (2017). Neuro-control of Nonlinear Systems with Unknown Input Constraints. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_80

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  • DOI: https://doi.org/10.1007/978-3-319-70087-8_80

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70086-1

  • Online ISBN: 978-3-319-70087-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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