School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, PR China.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, PR China.
ISA Trans. 2019 Dec;95:266-277. doi: 10.1016/j.isatra.2019.04.032. Epub 2019 May 4.
Force ripple deteriorates the performance of permanent magnet linear synchronous motor (PMLSM) servo systems. Using a model reference adaptive control and periodic adaptive learning control (MRAC-PALC) algorithm, this paper presents a novel compensation method to eliminate the influence of force ripple on the system performance of a position servo system under repetitive motion tasks. The key idea of the proposed method is to utilize the periodic characteristics of both force ripple and system motion. The controller consists of four components: a PD component, a feedforward component, a velocity feedback component and an MRAC-PALC compensator. The first three components are designed in a conventional way. The compensator is divided into two parts: in the 0th-iteration, an MRAC algorithm is employed to obtain the initial information, and in the ith-iteration (i≥ 1), a PALC algorithm is used to learn from the information obtained in the previous period and update the controller parameters for estimating force ripple. Moreover, a theoretical stability analysis is given via Lyapunov stability theorem, and some comparative results are provided through simulations and experiments.
力波纹会降低永磁直线同步电机（PMLSM）伺服系统的性能。本文提出了一种基于模型参考自适应控制和周期性自适应学习控制（MRAC-PALC）算法的新型补偿方法，用于消除在重复运动任务下位置伺服系统中力波纹对系统性能的影响。该方法的关键思想是利用力波纹和系统运动的周期性特征。控制器由四个部分组成：PD 部分、前馈部分、速度反馈部分和 MRAC-PALC 补偿器。前三个部分以传统方式设计。补偿器分为两部分：在第 0 次迭代中，采用 MRAC 算法获得初始信息，在第 i 次迭代（i≥1）中，采用 PALC 算法从前一周期获得的信息中学习，并更新控制器参数以估计力波纹。此外，通过 Lyapunov 稳定性定理给出了理论稳定性分析，并通过仿真和实验提供了一些比较结果。
