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Event-triggered-based Decentralized Optimal Control of Modular Robot Manipulators Using RNN Identifier

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Abstract

In this paper, an event-triggered-based decentralized tracking control method is proposed for modular robot manipulators (MRMs) using a recurrent neural network (RNN) and neuro-dynamic programming (NDP). The joint torque feedback (JTF) technique is introduced to model the MRM subsystems. The cost function of each subsystem consists of a tracking error fusion function and a term summarizing the RNN identifier errors. The event-triggered Hamiltonian-Jacobi-Bellman (ETHJB) equation is solved by constructing a critic neural network using NDP, and a decentralized optimal tracking control policy under the event-triggered framework can be obtained. The closed-loop MRM system is shown to be uniformly ultimately bounded under the Lyapunov stability theorem. Finally, the experimental results verify that the proposed control method is superior to the time-triggered optimal control policy and the observer-critic-based event-triggered optimal control policy proposed in the previous work of the author.

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Data Availability

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant no. 62173047), the Scientific Technological Development Plan Project in Jilin Province of China (Grant no. YDZJ202201ZYTS508) and the Science and Technology project of Jilin Provincial Education Department of China during the 13th Five-Year Plan Period (Grant no. JJKH20220689KJ).

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Authors and Affiliations

  1. Department of Control Science and Engineering, Changchun University of Technology, Changchun, 130021, China

    Qiang Pan, Yuanchun Li, Bing Ma, Tianjiao An & Fan Zhou

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  1. Qiang Pan
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  2. Yuanchun Li
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  3. Bing Ma
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  4. Tianjiao An
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  5. Fan Zhou
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Contributions

Fan Zhou: Software. Qiang Pan: Writing-original draft. Yuanchun Li: Supervision. Bing Ma: Conceptualization, Methodology. Tianjiao An: Data curation.

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Correspondence to Fan Zhou.

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Pan, Q., Li, Y., Ma, B. et al. Event-triggered-based Decentralized Optimal Control of Modular Robot Manipulators Using RNN Identifier. J Intell Robot Syst 106, 55 (2022). https://doi.org/10.1007/s10846-022-01746-6

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