Improved predictive position control for linear synchronous motor with disturbance observer

Abstract

This paper proposes an improved predictive position control (IPPC) method based on the disturbance observer (DOB) for linear synchronous motor (LSM) to enhance the tracking accuracy and robustness against parameter mismatches and load disturbance. A soft constraint containing the difference between predictive tracking errors and their exponential convergence trajectory is developed to the cost function to improve the tracking accuracy. Moreover, the IPPC employs a variable exponential-based DOB to estimate disturbances to improve the system's robustness to parameter mismatches and load disturbance. The control system's stability is analyzed using the Lyapunov stability theory and Bellman's principle of optimality. The experimental results show the effectiveness of the control scheme.

Position Control of Linear Synchronous Reluctance Motor Using a Modified Camel Traveling Algorithm-Based Proportional Integral Controller

“…The Lyapunov stability theory and Bellman's principle of optimality are used to examine stability of the control system. The experimental results show that the control method is effective [19].…”

“…The suggested technology is attractive from an industrial standpoint because it is non-contact, high resolution, and miniaturized [18]. In 2022, Shi and Lan [19] presented an improved predictive position control (IPPC) approach for linear synchronous motors (LSM) according to the disturbance observer (DOB) to improve tracking precision and robustness to parameter mismatches and load disturbances. To improve tracking accuracy, a soft constraint is built for the cost function that includes the gap between projected tracking errors and their exponential convergence trajectory.…”

Thrust Coordinated Assignment and Ripple Suppression of a Multiple-Modular Permanent Magnet Linear Synchronous Motor Based on Model Predictive Thrust Control

“…In [16], Fourier analysis was performed on the position loop output of the motor, and curve-fitting was performed to obtain the linear relationship between the thrust ripple and the position loop control parameter output. As far as the improved control strategy is concerned, predictive control [17,18], adaptive control [19], neural network control [20,21], iterative learning control [22,23], etc., are introduced to reduce the thrust pulsation while improving the robustness and anti-interference ability. However, the advanced control As for the control of the permanent magnet linear motors, many experts and scholars have proposed control strategies to effectively suppress thrust ripple, mainly including compensation and improved control algorithms [13,14].…”