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叶片叶根形变补偿控制方法研究

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由于风电叶片叶根合模后固化变形会引起预埋螺栓孔安装误差较大、形变补偿困难。为此,基于滚珠丝杠式的防缩模误差补偿工装结构方案研究了误差补偿控制系统。该控制系统考虑了滚珠丝杠系统补偿过程中存在未知扰动以及滑模控制自身抖振现象难以抑制等影响因素。首先,建立滚珠丝杠式误差补偿系统的集总参数数学模型;其次,提出基于神经网络扰动观测器的滑模位置控制方法;最后,为实现滑模控制参数自适应调节,设计了RBF-SMC算法,通过RBF网络对滑模控制系统的信息进行在线辨识。通过实验验证,所提出的基于神经网络扰动观测器的滑模位置控制方法能削弱滑模控制自身抖振现象,并减小稳态误差,具有较好的抗扰性能,为叶根防缩模工装生产过程数字化提供新思路。
Study on Compensation Control Method of Blade Root Deformation
The installation error of embedded bolt hole is large and the deformation compensation is difficult because of the solidification deformation of wind turbine blade after closing the mould.Therefore,the error compensation control system is studied based on the structure scheme of the anti-shrinking mould error compensation tool of the ball screw type.The unknown disturbance in the compensation process of the ball screw system and the chattering phenomenon of sliding mode control are considered in the control system.Firstly,the lumped parameter mathematical model of the ball screw error compensation system is established.Secondly,a sliding mode position control method based on neural network disturbance observer is proposed.Finally,in order to realize the adaptive adjustment of the sliding mode control parameters,RBF-SMC algorithm is designed,and the information of sliding mode control system is identified on-line by RBF Network.The experimental results show that the proposed sliding mode position control method based on neural network disturbance observer can weaken the chattering phenomenon of sliding mode control and reduce the steady-state error,it provides a new idea for digitizing the production process of leaf root anti-shrinking mould tooling.

leaf root digitisation compensationdisturbance observersliding mode controlRBF neural network

卢家骐、张万娟、侯丽媛、刘慧、陈惠贤、高学鹏

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中材科技(酒泉)风电叶片有限公司,甘肃 酒泉 735000

兰州理工大学机电工程学院,兰州 730050

叶根数字化补偿 扰动观测器 滑模控制 RBF神经网络

2025

机电工程技术
广东省机械研究所,广东省机械技术情报站,广东省机械工程学会

机电工程技术

影响因子:0.348
ISSN:1009-9492
年,卷(期):2025.54(1)