Robust adaptive motion control of permanent magnet linear motors based on disturbance compensation

• Published in: IET electric power applications Vol. 1; no. 4; pp. 543 - 548
• Main Authors: ZHANG, D. L, CHEN, Y. P, ZHOU, Z. D, AI, W, LI, X. D
• Format: Journal Article
• Language: English
• Published: London Institution of engineering and technology 01.07.2007; The Institution of Engineering & Technology
• Subjects: Adaptive control systems; Applied sciences; Computer simulation; Disturbances; Electric motors; Electrical engineering. Electrical power engineering; Electrical machines; Exact sciences and technology; Linear machines; Mathematical models; Miscellaneous; Motion control; Neural networks; Regulation and control; Ripples; Disturbance rejection; Control system; Control synthesis; Parameter variation; Permanent magnet motors; Neural network; Experimental study; Adaptive control; Adaptive method; Robust control; Backpropagation algorithm; Speed reducer; System simulation; Linear motor; Electric motor; Motion control
• ISSN: 1751-8660; 1751-8679
• DOI: 10.1049/iet-epa:20060319

Unlike rotational motors, permanent magnetic linear motors (PMLM) are more sensitive to various force disturbances because of the reduction of gears. The parameters of PMLMs are difficult to accurately measure and vary in experiments and applications. To efficiently control a PMLM and alleviate the influence of parameter variation, robust adaptive control method is proposed to be used in the motion control of PMLMs. The force ripple is the main force disturbance when the velocity is close to zero, but it is complex and difficult to be described by an accurate model. So, in the designed robust adaptive controller, an off-line back propagation neural network is proposed to approximate the function of the force ripple. Simulation and experimental results show that the designed robust adaptive controller can obtain higher control precision and be robust to the parameter variation.