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<p>The electro-hydraulic control system (EHCS) ensures compensatory motion precision of the serial active motion-compensated gangway (SAMCG). However, the performance of the EHCS is compromised due to the presence of nonlinearities, model uncertainties, and unknown dynamics of external loads. To address this concern, this article presents a data-driven nonlinear control strategy that integrates a Takagi–Sugeno (T–S) fuzzy clustering multi-modeling approach with the theory of generalized predictive control (GPC). The T–S fuzzy clustering multi-modeling approach, which can effectively characterize nonlinear and uncertain systems, is proposed to develop the model of the EHCS. Based on the established model, GPC is designed to ensure the control performance. Furthermore, to enhance the suppression of unmodeled dynamics, a backpropagation (BP) neural network is introduced for accurate prediction of the unmodeled dynamics, and a feedforward compensation is performed accordingly. Finally, comparative co-simulations are conducted to demonstrate the effectiveness of the proposed control scheme.</p>

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T–S Fuzzy Multi-model Predictive Control for Nonlinear SAMCG System with Unmodeled Dynamics Compensation

  • 期刊论文

  • 发表日期：

    2025年9月17日

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  • Yue Xu,
  • Gang Yang,
  • Baoren Li,
  • Zhe Wu,
  • Zhaozhuo Wang

摘要

The electro-hydraulic control system (EHCS) ensures compensatory motion precision of the serial active motion-compensated gangway (SAMCG). However, the performance of the EHCS is compromised due to the presence of nonlinearities, model uncertainties, and unknown dynamics of external loads. To address this concern, this article presents a data-driven nonlinear control strategy that integrates a Takagi–Sugeno (T–S) fuzzy clustering multi-modeling approach with the theory of generalized predictive control (GPC). The T–S fuzzy clustering multi-modeling approach, which can effectively characterize nonlinear and uncertain systems, is proposed to develop the model of the EHCS. Based on the established model, GPC is designed to ensure the control performance. Furthermore, to enhance the suppression of unmodeled dynamics, a backpropagation (BP) neural network is introduced for accurate prediction of the unmodeled dynamics, and a feedforward compensation is performed accordingly. Finally, comparative co-simulations are conducted to demonstrate the effectiveness of the proposed control scheme.