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Iterative learning control for continuous-time multi-agent differential inclusion systems with full learnability

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  • Zhou, Min
  • Wang, JinRong
  • Shen, Dong

Abstract

The learnability of systems plays a crucial role in the feasibility of control objective. This study explores the learnability and iterative learning-based consensus control for continuous-time multi-agent differential inclusion systems. Unlike discrete-time systems, analysis for the relationship between output space and realizable output space that used to explore learnability is more tough since involving continuous-time system output. Using measure theory, the systems exhibit full learnability if and only if the input–output coupling matrix is of full-row rank. Considering the possibility of controller power dissipation and demand for improving tracking performance, iterative learning control with state feedback and an efficiency factor is proposed. The consensus performance for the proposed control scheme is strictly explored. Simulation on unmanned vehicles in freeway demonstrates the validity of the theoretical results.

Suggested Citation

  • Zhou, Min & Wang, JinRong & Shen, Dong, 2023. "Iterative learning control for continuous-time multi-agent differential inclusion systems with full learnability," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007968
    DOI: 10.1016/j.chaos.2023.113895
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    References listed on IDEAS

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    1. Jian Liu & Yamiao Zhang & Xiaoe Ruan, 2019. "Iterative learning control for a class of uncertain nonlinear systems with current state feedback," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(10), pages 1889-1901, July.
    2. Nana Yang & Suoping Li & Xianming Zhang, 2022. "Distributed Coordination for a Class of High-Order Multiagent Systems Subject to Actuator Saturations by Iterative Learning Control," Complexity, Hindawi, vol. 2022, pages 1-18, February.
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    Cited by:

    1. Meng, Hao & Pang, Denghao & Cao, Jinde & Guo, Yechen & Niazi, Azmat Ullah Khan, 2024. "Optimal bipartite consensus control for heterogeneous unknown multi-agent systems via reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 476(C).

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