摘要
针对传统的单入单出控制器无法解决二维直线电机存在的非线性,不确定性以及强耦合作用等问题,依据无模型自适应控制不依赖于被控系统精确数学模型,仅需受控系统输入输出数据便能实现自适应控制这一特点,采用多入多出的紧格式动态线性化无模型自适应控制算法对二维直线电机XY轴进行整体控制器设计.同时,针对二维直线电机这种含有纯二阶积分环节的非自平衡系统,提出了多入多出无模型自适应控制改良方法,并进行严格的稳定性和收敛性证明.为了提高二维直线电机的轮廓精度,在多入多出无模型自适应控制改良方法的基础上,加入交叉耦合控制器,与传统的交叉耦合控制方法相比较,提高了跟踪精度和轮廓精度.最后通过仿真和实物实验证明了所提方案的有效性.
The traditional SISO controller can’t solve the problems of non-linearity, uncertainty and strong coupling of two-dimensional linear motor, according to the fact that the model-free adaptive control does not depend on the precise mathematical model of the controlled system, it can realize the adaptive control only by the input and output data of the controlled system. A compact form dynamic linearization model-free adaptive control algorithm(CFDL–MFAC) with multiple input and multiple output(MIMO) is used to design the overall controller for the XY axis of a two-dimensional linear motor. At the same time, an improved multi-input multi-output model-free adaptive control method is proposed for a two-dimensional linear motor, which is a non-self-balancing system with a pure second-order integral unit, the stability and convergence are strictly proved. In order to improve the contour accuracy of two-dimensional linear motor, a crosscoupling controller is added based on the improved multi-input multi-output model-free adaptive control method. Compared with the traditional cross-coupling control method, the tracking accuracy and contour accuracy are improved. Finally, the effectiveness of the proposed scheme is proved by simulation and practical experiments.
作者
曾子强
曹荣敏
侯忠生
周惠兴
ZENG Zi-qiang;CAO Rong-min;HOU Zhong-sheng;ZHOU Hui-xing(School of Automation,Beijing Information Science and Technology University,Beijing 100192,China;School of Automation,Qingdao University,Qingdao Shandong 266071,China;Beijing Jin Duo Technology Development Co.Ltd.,Beijing 100000,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2020年第5期1007-1017,共11页
Control Theory & Applications
基金
国家自然科学基金项目(61833001,61433002)
北京市自然科学基金项目(4142017)
2018年促进高校内涵发展“信息+”项目(5111823311)资助.