针对模型预测控制（Model Predictive Control ，MPC）应用在动力定位上时，存在的参数整定困难问题，提出一种基于非线性干扰观测器的模糊MPC策略。该策略通过模糊控制算法实现模型参数自整定，并在此基础上设计了一种非线性干扰观测器用于估计未知的环境力，进一步提升控制系统鲁棒性从而实现对环境力的补偿。研究结果表明，相比于传统的MPC，该策略能够有效提升动力定位系统的控制精度达26.6% 左右，且极大降低了模型参数整定的复杂性,对于动力定位船MPC算法的应用，具有一定参考价值。
This paper proposes a fuzzy model predictive control strategy for dynamic positioning ship aiming at the problem of parameter tuning. The strategy realizes the parameter self-tuning of model predictive control by using a fuzzy control algorithm. Then, in order to further improve the robustness of the control system, a nonlinear disturbance observer is designed to estimate the unknown environmental forces, the observer is contributed to realizing the compensation of the environmental forces. Compared with the traditional MPC, this strategy can effectively improve the control accuracy of the dynamic positioning system to about 26.6%, and greatly reduce the complexity of model parameter tuning. Theoretical analysis and simulation results well prove the effectiveness and superiority of these strategy, which are valuable for the practical application of model predictive control in dynamic positioning system.
2023,45(23): 115-121 收稿日期：2022-11-10
DOI：10.3404/j.issn.1672-7649.2023.23.020
分类号：U675.73
基金项目：国家自然科学基金资金项目（51179103）
作者简介：孔宇(1991-),男,硕士研究生,研究方向动力定位控制系统
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