Abstract

This paper aims to investigate the speed regulation problem for permanent magnet synchronous motor (PMSM) servo systems subject to unknown load torque disturbances. The proposed method utilizes sliding mode control (SMC), invariant manifold theory, and disturbance observation technique. In the PMSM servo systems, the unknown load torques will affect the control performance to a large extent, which is unmatched. In addition, compared with full-state measurement, the output-feedback framework is easy to implement and reduces the sensor costs. However, it is difficult to handle unmatched disturbance and unmeasured states simultaneously. To this end, this paper specifically combines the sliding mode control theory with the invariant manifold theory and puts forward an output-feedback disturbance rejection control method. The key idea is that the unmatched disturbance in the PMSM servo systems is transformed into matched one by taking advantage of the invariant manifold, which is different from existing results. The transformation maintains most of dynamics of the PMSM system for control design, which improves the accuracy. In addition, an extended state observer is designed to estimate the current and lumped disturbance simultaneously; then, the output-feedback SMC method is proposed by introducing the estimations. Besides, the switching gain in the proposed sliding mode controller can change with estimation errors adaptively, and the chattering reduces. Simulation results on a PMSM system validate the effectiveness of the proposed control strategy.

Highlights

  • Permanent magnet synchronous motor (PMSM) has been widely applied to various practical systems, such as robotics, aerospace, and power generations [1,2,3,4], due to the highefficiency, high air-gap flux density, large torque-to-inertia ratio, and high power density [1]

  • In order to obtain better performance, many advanced nonlinear control methods have been developed for PMSM servo systems in recent years, such as adaptive control [2, 7], robust control [8, 9], linearization control [10], disturbance observer-based control [2, 11], fuzzy-logic based control [6, 12], finite time control [13, 14], fractional order control [15], sliding mode control [16, 17], and neuro-network based control [5, 12]. ese control strategies improve control performance for PMSM servo systems from different aspects

  • In order to estimate the unmeasured mechanical parameters of PMSM, a terminal sliding mode observer is proposed in [26], which can estimate the control performance with a finitetime convergence rate. e work in [27] employs the sliding mode control technique and the extended state observer for PMSM system to improve the robustness against load disturbance and parameter variations. ese results are mostly implemented in the condition that all the system states are available

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Summary

Introduction

Permanent magnet synchronous motor (PMSM) has been widely applied to various practical systems, such as robotics, aerospace, and power generations [1,2,3,4], due to the highefficiency, high air-gap flux density, large torque-to-inertia ratio, and high power density [1]. The output-feedback SMC for PMSM servo system subject to unknown load torque disturbances is a challenging problem, since it is difficult to estimate both the unmeasured states and the unmatched disturbance at the same time. E main contributions and benefits of the proposed method are summarized as follows: (1) full dynamics of the PMSM system are thoroughly exploited in the invariant manifold based output-feedback SMC design process, which admits higher bandwidth without higher observer gains, attenuating measurement noises to a large extent; (2) the switching gain changes with estimation errors adaptively, and the chattering will be reduced; (3) the proposed output-feedback SMC method can compensate the influences caused by unknown derivatives of the desired reference signal without resorting to tracking differentiators, which results in a simpler control structure and saves the implementation burden of the algorithm.

System Model and Problem Formulation
Main Results
Simulations
Methods
Proof of Theorem 1
Full Text
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