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

Abstract

Unlike rotational motors, permanent magnetic linear motors (PMLMs) 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 backpropagation 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.