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Reinforcement Learning-Based Backstepping Control for Container Cranes

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  • Xiao Sun
  • Zhihang Xie

Abstract

A novel backstepping control scheme based on reinforcement fuzzy Q-learning is proposed for the control of container cranes. In this control scheme, the modified backstepping controller can handle the underactuated system of a container crane. Moreover, the gain of the modified backstepping controller is tuned by the reinforcement fuzzy Q-learning mechanism that can automatically search the optimal fuzzy rules to achieve a decrease in the value of the Lyapunov function. The effectiveness of the applied control scheme was verified by a simulation in Matlab, and the performance was also compared with the conventional sliding mode controller aimed at container cranes. The simulation results indicated that the used control scheme could achieve satisfactory performance for step-signal tracking with an uncertain lope length.

Suggested Citation

  • Xiao Sun & Zhihang Xie, 2020. "Reinforcement Learning-Based Backstepping Control for Container Cranes," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, February.
    • Handle: RePEc:hin:jnlmpe:2548319
    DOI: 10.1155/2020/2548319
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    Cited by:

    1. Alyazidi, Nezar M. & Hassanine, Abdalrahman M. & Mahmoud, Magdi S., 2023. "An Online Adaptive Policy Iteration-Based Reinforcement Learning for a Class of a Nonlinear 3D Overhead Crane," Applied Mathematics and Computation, Elsevier, vol. 447(C).

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