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Adaptive Neural Asymptotic Tracking Control of MIMO Stochastic Non-strict-feedback Nonlinear Systems

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Advances in Applied Nonlinear Dynamics, Vibration and Control -2021 (ICANDVC 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 799))

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Abstract

In this paper, the neural-based adaptive asymptotic tracking control problem is considered for a class of multiple-input multiple-output (MIMO) stochastic non-strict-feedback nonlinear systems with unknown control gains and full state constraints. A barrier Lyapunov function (BLF) is designed to avoid the violation of state constraints, and the problem of the unknown control gains is solved by introducing an auxiliary virtual controller. Besides, a new control scheme is proposed, which can not only realize the asymptotic tracking control in probability but also meet the requirement of the full state constraints imposed on the system. Eventually, the simulation results verify the feasibility of the scheme.

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Author information

Authors and Affiliations

  1. Shandong Normal University, Jinan, 250014, China

    Wei Su, Ben Niu & Guangju Zhang

Authors
  1. Wei Su
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  2. Ben Niu
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  3. Guangju Zhang
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Corresponding author

Correspondence to Ben Niu .

Editor information

Editors and Affiliations

  1. Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Xingjian Jing

  2. Shanghai University, Shanghai, Shanghai, China

    Hu Ding

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China

    Jiqiang Wang

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Cite this paper

Su, W., Niu, B., Zhang, G. (2022). Adaptive Neural Asymptotic Tracking Control of MIMO Stochastic Non-strict-feedback Nonlinear Systems. In: Jing, X., Ding, H., Wang, J. (eds) Advances in Applied Nonlinear Dynamics, Vibration and Control -2021. ICANDVC 2021. Lecture Notes in Electrical Engineering, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-16-5912-6_12

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  • DOI: https://doi.org/10.1007/978-981-16-5912-6_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5911-9

  • Online ISBN: 978-981-16-5912-6

  • eBook Packages: EngineeringEngineering (R0)

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