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Neural-Network-Based Robust Tracking Control for Condenser Cleaning Crawler-Type Mobile Manipulators with Delayed Uncertainties

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Abstract

In this paper, the problem of the robust tracking for two-arm condenser cleaning crawler-type mobile manipulators (CCCMM) with delayed angle-velocity uncertainties is original investigated. The two-arm condenser cleaning crawler-type mobile manipulators are composed of a crawler-type mobile platform and two-arm industrial manipulators.The uncertainty is nonlinear time-varying and does not require a matching condition. A wavelet transform and probabilistic neural network (WTPNN) system is used to approximate an unknown controlled system from the strategic manipulation of the model following tracking errors. Based on the Lyapunov method and the linear matrix inequality (LMI) approach, several sufficient conditions, which guarantee the state variables of the closed loop system to converge, globally, uniformly and exponentially, to a ball in the state space with any pre-specified convergence rate, are derived. Experiment results are given to illustrate the superior control performance of the proposed intelligent control method.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.

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Authors and Affiliations

  1. The School of Electronic Information and Electrical Engineering, Changsha University, Changsha, 410022, Hunan, People’s Republic of China

    Yi Zuo, Minghua Xie & Lihua Cao

  2. The School of Information Science and Engineering, Changsha Normal University, Changsha, 410000, Hunan, People’s Republic of China

    Dong Hu

  3. The College of Electric and Information Technology, Hunan University, Changsha, 410082, Hunan, People’s Republic of China

    Yaonan Wang

  4. The Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Xinzhi Liu

  5. The School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, 410004, Hunan, People’s Republic of China

    Zhisheng Chen & Huimin Zhao

Authors
  1. Yi Zuo
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  2. Dong Hu
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  3. Yaonan Wang
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  4. Xinzhi Liu
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  5. Minghua Xie
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  6. Lihua Cao
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  7. Zhisheng Chen
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  8. Huimin Zhao
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Corresponding author

Correspondence to Dong Hu.

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This work was supported by the Changsha University Talent Introduction Research Project (2019), National Natural Science Foundation of China (51674042), Key Laboratory Foundation for Power Technology of Renewable Energy Sources of Hunan Province (2011KFJJ004), China Scholarship Council (201808430235), China Institute of Electrical Engineering Power Youth Science and Technology Innovation Projects (201014), Hunan Natural Science Foundation (2018JJ3561), Changsha Science and Technology Planning Project (k17005065), Hunan Education Authorities Science Research Project (16C0041, 17C0131), Hunan Education Authorities Science Research Outstanding Youth Project (20B057), the Young Teachers Program of Changsha University of Science and Technology (2019QJCZ041, 2019QJCZ079).

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Zuo, Y., Hu, D., Wang, Y. et al. Neural-Network-Based Robust Tracking Control for Condenser Cleaning Crawler-Type Mobile Manipulators with Delayed Uncertainties. Int J of Soc Robotics 13, 1385–1396 (2021). https://doi.org/10.1007/s12369-020-00728-8

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  • DOI: https://doi.org/10.1007/s12369-020-00728-8

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