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,建立控制电压输入和齿条速度输出的状态方程数学模型及卡尔曼-遗传优化的模糊PID(proportional-integral-derivative)控制器。采用遗传算法对模糊控制器中的控制规则和因子进行优化,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。利用行走机构样机验证遗传算法优化的模糊PID控制算法。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0.153 s,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。
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 was conducted.Subsequently,the mathematical model of state equation controlling voltage input and rack-speed output and the fuzzy proportional-integral-derivative(PID)controller with Kalman-genetic optimization were established.Genetic algorithms were 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 was also introduced to mitigate the impact of external disturbances on the system.The walking mechanism prototype was used to verify the fuzzy PID control algorithm optimized by genetic algorithm.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 0.153 s,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.
作者
张敬龙
何春燕
王建国
李明博
赵立新
ZHANG Jing-long;HE Chun-yan;WANG Jian-guo;LI Ming-bo;ZHAO Li-xin(Institute of Machinery and Equipment Engineering,Hebei University of Engineering,Handan 056038,China;Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province,Hebei University of Engineering,Handan 056038,China;Ji Zhi Kang(Beijing)Technology Co.,Ltd.,Beijing 102600,China)
出处
《科学技术与工程》
北大核心
2024年第28期12200-12210,共11页
Science Technology and Engineering
基金
河北省教育厅科学研究项目(CXY2024046)。
关键词
风电叶片涂胶设备
机电系统
模糊控制
遗传优化
卡尔曼滤波器
wind turbine blade gluing equipment
electromechanical system
fuzzy control
genetic optimization
Kalman filter
