基于最小二乘的自适应最优迭代学习控制研究

Research on Adaptive Optimal Iterative Learning Control Based on Least Squares

传统的最优迭代学习控制(TOILC)可以有效地提高伺服系统的跟踪性能。然而,在伺服系统的运行过程中可能存在参数扰动,其参数不断缓慢变化,从而导致TOILC的收敛性变差,系统的跟踪性能严重恶化。因此,考虑到系统的时变特性,提出了一种基于最小二乘的自适应最优迭代学习控制(LSAOILC)算法。在迭代过程中,根据输入和输出数据辨识得到系统的名义模型,来更新最优迭代学习控制器。当系统存在参数扰动时,它仍具有良好的跟踪性能,弥补了TOILC的不足。仿真和实验证明了该算法对时变系统的有效性。

Traditional Optimal Iterative Learning Control (TOILC) can effectively improve the tracking perfor-mance of the servo system. However, there may be parameter perturbation in the running process of the servo system, and its parameters are constantly changing slowly. As a result, the convergence of the TOILC becomes worse, and the tracking performance of the system deteriorates seriously. Therefore, in view of the time-varying characteristics of the system, a least squares adaptive opti-mal iterative learning control (LSAOILC) algorithm is proposed. In the process of iteration, the nominal model of the system is identified according to input and output data so as to update the op-timal iterative learning controller. It still has good tracking performance when the system has pa-rameter perturbation, which makes up for the shortage of the TOILC. The simulations and experi-ments prove the effectiveness of the proposed algorithm for the time-varying system.