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<p>A robust control strategy combining iterative learning and adaptive sliding mode is presented for the positioning and swing suppression tasks of a 3-dimensional overhead crane. Based on the introduction of system dynamics, a control-oriented model including only the actuated variables is first established. Then, a novel sliding mode control (SMC) law is designed for the established model. In the design of the control law, an iterative learning technique is used to identify the crane dynamics caused by unknown payloads; an adaptive technique is used to estimate the uncertainty caused by factors such as friction and external disturbances. The stability of the designed SMC system is demonstrated using Lyapunov theory. Finally, the results verify the effectiveness of the presented strategy and demonstrate that it outperforms the neural network-based SMC strategy.</p>

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Adaptive sliding mode control of 3D overhead cranes based on iterative learning

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Weiqiang Tang,
  • Zanmei Ren,
  • Jiazhen Zhang,
  • Haiyan Gao

摘要

A robust control strategy combining iterative learning and adaptive sliding mode is presented for the positioning and swing suppression tasks of a 3-dimensional overhead crane. Based on the introduction of system dynamics, a control-oriented model including only the actuated variables is first established. Then, a novel sliding mode control (SMC) law is designed for the established model. In the design of the control law, an iterative learning technique is used to identify the crane dynamics caused by unknown payloads; an adaptive technique is used to estimate the uncertainty caused by factors such as friction and external disturbances. The stability of the designed SMC system is demonstrated using Lyapunov theory. Finally, the results verify the effectiveness of the presented strategy and demonstrate that it outperforms the neural network-based SMC strategy.