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Intelligent Actor-Critic Learning Control for Collison-Free Trajectory Tracking of Mecanum-Wheeled Mobile Robots

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Abstract

In the paper, an intelligent actor-critic learning control method augmented by a fuzzy broad learning system with output recurrent feedback (abbreviated as ORFBLS) is proposed for obstacle-avoiding trajectory tracking of heterogeneous Mecanum-wheeled omnidirectional mobile robots (HMOMRs) augmented with un-modeled errors and the learning control system’s parameters are changed with the working environment. By using actor-critic learning algorithms and the ORFBLS online function approximation, a new ORFBLS-based actor-critic learning control approach, dubbed as ORFBLS-ACLC, is presented to accomplish stable trajectory tracking of such uncertain HMOMRs. The ORFBLS-ACLC method combined with an obstacle avoidance scheme is presented to follow the desired collision-free trajectory tracking more closely than the existing methods do. The merits, superiority, and practicality of the raised control approach are well verified by carrying out three simulation comparisons and two experiments on an uncertain HMOMR.

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Acknowledgements

The authors are very thankful to financial support from Ministry of Science and Taiwan, ROC, under the contract MOST 109-2221- E-005-066 -MY2 and NSTC 112-2622-8-005 -005 -TE1.

Funding

Funding was provided by National Science and Technology Council (112-2622-8-005 -005 -TE1) and Ministry of Science and Technology, Taiwan (MOST 109-2221- E-005-066 -MY2).

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Authors and Affiliations

  1. Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan

    Ching-Chih Tsai, Hsing-Yi Chen, Chi-Chih Hung & Guan-Ming Chen

  2. Department of Computer Science and Information Engineering, National Formosa University, Yunlin, Taiwan

    Chun-Chieh Chan

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  1. Ching-Chih Tsai
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  2. Hsing-Yi Chen
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Correspondence to Ching-Chih Tsai.

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Tsai, CC., Chen, HY., Chan, CC. et al. Intelligent Actor-Critic Learning Control for Collison-Free Trajectory Tracking of Mecanum-Wheeled Mobile Robots. Int. J. Fuzzy Syst. 26, 1133–1142 (2024). https://doi.org/10.1007/s40815-023-01656-1

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  • DOI: https://doi.org/10.1007/s40815-023-01656-1

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