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A reinforcement learning integral sliding mode control scheme against lumped disturbances in hot strip rolling

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  • Ding, Hongfei
  • Wang, Yudong
  • Shen, Hao

Abstract

In the hot strip rolling production process, due to the influence of external disturbances and unmodeled errors which are defined as lumped disturbances (LDs), the looper operating angle and strip tension may not satisfy the requirements of normal production under the set values, which may lead to the product not satisfying the requirements. In this paper, an advanced reinforcement learning (RL) algorithm combined with integral sliding mode control (ISMC) is utilized to deal with LDs, and then the robustness of the system is improved. Firstly, the hot strip rolling is a complex nonlinear system, the looper model is linearized to be approximated around the operating point for simplicity. Secondly, the ISMC is addressed for LDs in hot strip rolling; meanwhile, due to the chattering phenomenon in conventional sliding mode control, a novel reaching law is designed; whereafter an off-policy RL algorithm is adopted to obtain the optimal controller. Finally, the effectiveness and superiority of the addressed method are elaborated by an example.

Suggested Citation

  • Ding, Hongfei & Wang, Yudong & Shen, Hao, 2024. "A reinforcement learning integral sliding mode control scheme against lumped disturbances in hot strip rolling," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    • Handle: RePEc:eee:apmaco:v:465:y:2024:i:c:s0096300323005763
    DOI: 10.1016/j.amc.2023.128407
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    References listed on IDEAS

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    1. Liu, Yu-An & Tang, Shengdao & Liu, Yufan & Kong, Qingkai & Wang, Jing, 2021. "Extended dissipative sliding mode control for nonlinear networked control systems via event-triggered mechanism with random uncertain measurement," Applied Mathematics and Computation, Elsevier, vol. 396(C).
    2. Zhang, Haoyue & Ding, Shihong, 2023. "Adaptive asymptotic tracking control design for high-order uncertain nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 445(C).
    3. Xu, Ning & Zhao, Xudong & Zong, Guangdeng & Wang, Yuanqing, 2021. "Adaptive control design for uncertain switched nonstrict-feedback nonlinear systems to achieve asymptotic tracking performance," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    4. Zhao, Zhi-Ye & Jin, Xiao-Zheng & Wu, Xiao-Ming & Wang, Hai & Chi, Jing, 2022. "Neural network-based fixed-time sliding mode control for a class of nonlinear Euler-Lagrange systems," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    5. Xue, Yanmei & Ren, Wen & Zheng, Bo-Chao & Han, Jinke, 2022. "Event-triggered adaptive sliding mode control of cyber-physical systems under false data injection attack," Applied Mathematics and Computation, Elsevier, vol. 433(C).
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    Cited by:

    1. Liu, Yu-An & Zhang, Li, 2024. "Enhancing secondary recovery in microgrid: Event-driven robust security control with semi-Markov switched dynamics," Applied Mathematics and Computation, Elsevier, vol. 478(C).

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