基于混合算法优化的叶片涂胶行走机构速度控制
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TP202+.7、TP273+.4

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河北省教育厅科学研究项目(CXY2024046)


Speed control of blade gluing travel mechanism based on hybrid algorithm optimization
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    摘要:

    风电叶片涂胶行走机构的速度控制直接影响涂胶质量与稳定性。针对涂胶过程中控制速度时自适应力差、控制精度不足、抗干扰能力弱等影响设备涂胶质量与效率问题,分析涂胶设备整体布局及行走机构的结构,建立数学模型及卡尔曼-遗传优化的模糊PID控制器。采用遗传算法对模糊控制器中的重要参数进行优化,从而实现行走机构速度追踪的精准控制;加入卡尔曼滤波器算法,用来减小干扰对系统的影响。实验结果表明,基于遗传算法优化的模糊PID控制算法超调量为0.8%、调节时间为0.153s,相对于模糊PID及PID控制算法均有所改善。基于卡尔曼遗传优化的模糊PID相较于遗传优化的模糊PID,响应时间提高10倍,降低干扰16倍。卡尔曼遗传算法优化的模糊PID提高了系统的控制精度、稳定性及抗干扰能力,从而提高涂胶设备涂胶质量与涂胶效率。

    Abstract:

    The speed control of wind turbine blade gluing travel mechanism directly affects the gluing quality and stability. Addressing issues such as inadequate adaptive response, insufficient control precision, and weak resistance to interference during the adhesive application process, an analysis of the overall layout and structural components of the adhesive application equipment is conducted. Subsequently, a mathematical model and Kalman-genetic optimization fuzzy PID controller are established. Genetic algorithms are employed to optimize key parameters within the fuzzy controller, thereby achieving precise control of the motion system"s speed. The integration of a Kalman filter algorithm is also introduced to mitigate the impact of external disturbances on the system. The experimental results show that the fuzzy PID control algorithm optimized based on genetic algorithm has an overshoot of 0.8% and a regulation time of 0.153s, which is an improvement compared to both fuzzy PID and PID control algorithms. The fuzzy PID based on Kalman genetic optimization improves the response time by 10 times and reduces interference by 16 times compared to the genetically optimized fuzzy PID. The Kalman genetic algorithm optimized fuzzy PID improves the control accuracy,stability and anti-interference ability of the system, which improves the gluing quality and gluing efficiency of the gluing equipment.

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张敬龙,何春燕,王建国,等. 基于混合算法优化的叶片涂胶行走机构速度控制[J]. 科学技术与工程, 2024, 24(28): 12200-12210.
ZhangJinglong, HeChunyan, WangJianguo, et al. Speed control of blade gluing travel mechanism based on hybrid algorithm optimization[J]. Science Technology and Engineering,2024,24(28):12200-12210.

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  • 收稿日期:2023-12-28
  • 最后修改日期:2024-08-03
  • 录用日期:2024-03-21
  • 在线发布日期: 2024-11-05
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