Iterative Learning Identification and Compensation of Space-Periodic Disturbance in PMLSM Systems With Time Delay

Abstract

This paper aims to solve a closed-loop identification problem for the space-periodic force ripple in permanent-magnet linear synchronous motor (PMLSM) systems. Conventional identification schemes use the overall error signal to update estimates. However, the error caused by mechanical vibration and measurement noise could affect and even deteriorate the identification performance. In this paper, a novel iterative learning identification method that utilizes the partial but most pertinent information in the error signal is proposed to identify the force ripple. First, the effective error signal caused by the reference trajectory and the force ripple are extracted by projecting the overall error signal to a subspace. The subspace is spanned by some basis functions selected on the basis of the physical model of the PMLSM and the sinusoidal model of the force ripple. The time delay of the PMLSM system is compensated in these basis functions. Then, a norm-optimal approach is proposed to design the learning gain. The monotonic convergence of the iterative learning identification is further analyzed. Numerical simulation and experiments are provided to validate the proposed method and confirm its feasibility and effectiveness in force ripple identification, as well as its compensation.