HGO and neural network based integral sliding mode control for PMSMs with uncertainty
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ORIGINAL ARTICLE
HGO and neural network based integral sliding mode control for PMSMs with uncertainty Yang Ge1 · Lihui Yang1 · Xikui Ma1 Received: 27 December 2019 / Revised: 4 June 2020 / Accepted: 8 June 2020 © The Korean Institute of Power Electronics 2020
Abstract This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Since the second-order motion equation of the PMSM is used to improve the control performance, the speed derivative, which cannot be measured directly, is required. Thus, the HGO is designed to estimate the unknown state (speed derivative). In addition, the RBFNN is designed to approximate the compounded disturbance including the lumped disturbance of system and the HGO error effect. Unlike previous studies, the output of the RBFNN is compensated by both the controller and the HGO to improve the system robustness and observer accuracy. The sliding function and the HGO error are both taken into account in the RBFNN to explicitly guarantee the stability of the whole system. To demonstrate the superiority of the proposed method, comparative simulations and experiments were carried out in different cases. Keywords Permanent magnet synchronous motor · Integral sliding mode control · High-gain observer · Radial basis function neural network · Parameter adaptive algorithm
1 Introduction In recent years, permanent magnet synchronous motors (PMSMs) have been widely used in high-performance motion control systems due to their advantages such as high air gap flux density, large torque to inertia ratio and smaller torque ripple. However, in many applications, high-performance PMSM speed control still faces some challenges, e.g., varying operating conditions, structured and unstructured dynamical uncertainties, load disturbances, and parametric uncertainties (such as the resistance varying with temperature) [1–3]. Various control techniques have been investigated and applied in PMSMs, such as adaptive control [4, 5], feedback linearization control [6], sliding mode control (SMC) [7], predictive control [8], and intelligent control [9, 10]. These methods improved the system control performance from different perspectives.
* Lihui Yang [email protected] 1
The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China
Among these methods, due to its robustness, SMC has gained more attention in electromechanical drive systems. In the SMC design of PMSMs, to obtain a satisfactory speed response, knowledge of the speed derivative is usually required for the construction of the sliding function [11, 12]. However, there are usually no sensors to measure the motor acceleration. In addition, the direct calculation of the speed derivative strongly amplifies the measurement noise. Currently, the most widely used state estimation methods are the track-differentiato
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