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An RBFNN-Informed Adaptive Sliding Mode Control for Wheeled Mobile Robots

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Intelligent Robotics and Applications (ICIRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13016))

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Abstract

This paper deals with the trajectory tracking of differential driven wheeled mobile robots (WMRs). It is widely known that parameter uncertainty and unknown external disturbances could bring adverse effects to WMRs. To achieve high-performance tracking, a control scheme is proposed in this paper, which integrates a kinematics controller and a dynamics controller. The kinematics controller is designed by the backstepping control and the dynamics controller is derived via adaptive sliding mode control. Moreover, dual radial basis function neural networks (RBFNNs) with adaptive adjust algorithms are adopted to approximate parameter alterations of system dynamics as well as external disturbances. To solve the uncertainty of wheel rolling radius, an adaptive control law is applied. In addition, a PI-type sliding mode control strategy with an exponential reaching law is designed to compensate for the approximation errors of the RBFNNs. Furthermore, the stability and global asymptotic convergence of the control system are proved by the Lyapunov stability theorem. Finally, simulations are provided to verify the effectiveness of the proposed control strategy. The results indicate that the proposed control law is effective in response to parameter uncertainty and external disturbances.

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Acknowledgments

This work was supported by the National Key R&D Program of China under Grant 2019YFA0706701 and by the National Natural Science Foundation of China under Grants 91948301 and 51922057.

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Authors and Affiliations

  1. The State Key Laboratory of Tribology and Institute of Manufacturing Engineering, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Quan Liu, Zhao Gong, Fugui Xie, Shuzhan Shentu & Xin-Jun Liu

  2. Beijing Key Lab of Precision/Ultra-Precision Manufacturing Equipments and Control, Tsinghua University, Beijing, 100084, China

    Fugui Xie & Xin-Jun Liu

Authors
  1. Quan Liu
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  2. Zhao Gong
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  3. Fugui Xie
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  4. Shuzhan Shentu
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  5. Xin-Jun Liu
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Corresponding author

Correspondence to Xin-Jun Liu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Xin-Jun Liu

  2. Tsinghua University, Beijing, China

    Zhenguo Nie

  3. Beihang University, Beijing, China

    Jingjun Yu

  4. Tsinghua University, Beijing, China

    Fugui Xie

  5. Shandong University, Shandong, China

    Rui Song

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Liu, Q., Gong, Z., Xie, F., Shentu, S., Liu, XJ. (2021). An RBFNN-Informed Adaptive Sliding Mode Control for Wheeled Mobile Robots. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13016. Springer, Cham. https://doi.org/10.1007/978-3-030-89092-6_59

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  • DOI: https://doi.org/10.1007/978-3-030-89092-6_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89091-9

  • Online ISBN: 978-3-030-89092-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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