| 马炳图,杜钦君,张婷,吴育桐,李伟强,刘家合.数据驱动的风电机组变桨系统双电机同步驱动控制[J].电子测量与仪器学报,2025,39(9):202-214 |
| 数据驱动的风电机组变桨系统双电机同步驱动控制 |
| Dual motor synchronous drive control of data-driven wind turbine pitch system |
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| DOI： |
| 中文关键词: 双电机驱动变桨系统 同步控制 数据驱动 模型预测控制器 核极限学习机 麻雀算法 |
| 英文关键词:dual drive propeller system synchronization control data-driven model predictive controller kernel extreme learning machine sparrow algorithm |
| 基金项目:国家自然科学基(62076152)、山东省科技型中小企业创新能力提升工程项目(2024TSGC0291)资助 |
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| Author | Institution |
| Ma Bingtu | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255022, China |
| Du Qinjun | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255022, China |
| Zhang Ting | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255022, China |
| Wu Yutong | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255022, China |
| Li Weiqiang | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255022, China |
| Liu Jiahe | School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255022, China |
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| 中文摘要: |
| 双电机驱动变桨系统是一个强耦合非线性时变系统，两台伺服电机的参数也会在运行过程中发生变化，导致系统的机理模型不准确，影响两台电机的同步控制精度。提出了一种基于改进麻雀搜索算法优化混合核极限学习机（CGSSA-HKELM）的数据驱动模型以及量子遗传算法（QGA）求解目标函数的双电机模型预测同步控制系统。首先，采用核极限学习机回归原理，建立两台电机的统一预测模型，提高预测模型的准确度、泛化能力以及学习的快速性。其次，针对核极限学习机对参数设置敏感的问题，利用改进的麻雀搜索算法优化其模型参数并进行离线训练，获得具有自适应能力的预测模型。在构建的模型预测同步控制系统中，引入量子遗传算法对目标函数进行优化求解，避免求解陷入局部最优，得到两个电机的最优控制量。最后，为了证明该方案的有效性，进行了仿真和实验验证，结果表明，设计的双电机同步控制策略与交叉耦合滑模控制策略相比，两台电机的转矩误差下降了45%，转矩脉动下降了40%，仿真和实验结果有效证明了所设计的双电机模型预测同步控制方案的合理性及有效性。 |
| 英文摘要: |
| The dual-motor drive pitch system is a strong coupling nonlinear time-varying system. The parameters of the two servo motors will also change during operation, resulting in inaccurate mechanism model of the system and affecting the synchronous control accuracy of the two motors. In this paper, a data-driven model based on improved sparrow search algorithm to optimize hybrid kernel extreme learning machine (CGSSA-HKELM) and a dual-motor model predictive synchronous control system based on quantum genetic algorithm (QGA) to solve the objective function are proposed. Firstly, the kernel extreme learning machine regression principle is used to establish a unified prediction model for two motors, which improves the accuracy, generalization ability and learning speed of the prediction model. Secondly, aiming at the problem that the kernel extreme learning machine is sensitive to parameter settings, the improved sparrow search algorithm is used to optimize its model parameters and conduct offline training to obtain a prediction model with adaptive ability. In the constructed model predictive synchronous control system, quantum genetic algorithm is introduced to optimize the objective function, so as to avoid falling into local optimum and obtain the optimal control of two motors. Finally, in order to prove the effectiveness of the scheme, simulation and experimental verification are carried out. The results show that the torque error of the two motors is reduced by 45% and the torque ripple is reduced by 40% compared with the cross-coupled sliding mode control strategy. The simulation and experimental results effectively prove the rationality and effectiveness of the dual-motor model predictive synchronous control scheme designed in this paper. |
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