改进无模型自适应迭代学习的直线电机滑模控制
Improved model free adaptive iterative learning control for linear motor sliding mode control
郑鑫鑫 1张大海 1曹荣敏 2侯忠生3
作者信息
- 1. 北京交通大学 电气工程学院,北京100044
- 2. 北京信息科技大学 自动化学院,北京100192
- 3. 青岛大学 自动化学院,山东 青岛266071
- 折叠
摘要
二维直线电机具有非线性、多变量及强耦合等特点,其精确模型无法获得,同时,在实际过程中由于参数摄动及扰动而造成的运行不稳定现象,致使对其跟踪控制十分困难.针对以上问题,根据无模型自适应迭代学习控制不依赖被控系统精确数学模型的特点,及滑模控制具有可设计且与对象参数和扰动无关的滑动模态,提出一种改进无模型自适应迭代学习的滑模结构复合控制策略.在无模型自适应迭代学习控制方案的准则函数中加入误差变化率,并对其收敛性进行分析论证;然后在紧格式动态线性化基础上,设计指数趋近律滑模控制,使改进无模型自适应迭代学习的滑模控制复合策略能够克服不稳定现象并具有很强的鲁棒性,从而进一步提高系统响应速度和控制精度.最后通过仿真和实物验证,控制精度稳定在1μm范围内,并与其他控制方案相比,验证了所提方法的准确性和有效性.
Abstract
The two-dimensional linear motor is characterized by nonlinearity,uncertainty,multivariable and strong coupling,and its exact model is not available,meanwhile,the operational instability caused by parameter uptake and perturbation in the actual process makes its tracking control very difficult. To address the above problems,based on the characteristics of model free adaptive iterative learning control that does not depend on the accurate mathematical model of the controlled system and the sliding mode control that can be designed independently of the object parameters and perturbations,a composite con-trol strategy was proposed to improve the model free adaptive iterative learning sliding mode structure. Then,the exponential convergence law sliding mode control was designed on the basis of the tight-form dynamic linearization,so that the improved model free adaptive iterative learning sliding mode control composite strategy can overcome the instability phenomenon and has strong robustness,thus further im-proving the system response speed and control accuracy. Finally,the control accuracy was stabilized within 1 μm by simulation and physical verification,and the accuracy and effectiveness of the proposed method was verified compared with other control schemes.
关键词
改进无模型自适应迭代学习控制/误差变化率/准则函数/滑模控制/二维直线电机Key words
improved model free adaptive iterative learning control/sliding mode control/two-dimensional linear motor/error rate of change/criterion function引用本文复制引用
基金项目
国家自然科学基金重点项目(61833001)
北京市自然科学基金(4142017)
出版年
2024
NETL
NSTL
万方数据