Force ripple compensation in a PMLSM position servo system using periodic adaptive learning control.

Clicks: 195
ID: 69728
2019
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Abstract
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.
Reference Key
zhang2019forceisa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Wenjing;Nan, Nan;Yang, Yifan;Zhong, Weifeng;Chen, YangQuan;
Journal ISA transactions
Year 2019
DOI
S0019-0578(19)30206-X
URL
Keywords
force ripple compensation model reference adaptive control pmlsm position servo system periodic adaptive learning control

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