LS-SVM Inverse System Decoupling Control Strategy of a Bearingless Induction Motor Considering Stator Current Dynamics

Abstract

The bearingless induction motor (BL-IM) is a multivariable, nonlinear and strongly coupled object, in order to achieve its dynamic decoupling control with high performance, on the basis of the least square support vector machine(LS-SVM) principle, a novel LS-SVM inverse system decoupling control strategy is proposed. Firstly, under the conditions of taking the stator current dynamics of torque windings into account, the state equations of the BL-IM system are established. Secondly, based on the approximation and identification fitting ability of the LS-SVM to arbitrary nonlinear function, the inverse system mathematical model of the BL-IM system considering the stator current dynamics is trained and obtained. After that, according to the decoupling principle of inverse system, the BL-IM system is decoupled into four second-order pseudo-linear integral subsystems, include a motor speed subsystem, a rotor fluxlinkage subsystem and two radial displacement component subsystems. At the end, the comprehensive simulation analysis of the LS-SVM inverse decoupling control system are carried out. From the simulation results, it can be known that the dynamic decoupling control among the motor speed, rotor flux-linkage and two radial displacement components can be realized, meanwhile after considering the dynamics of the stator current, the LS-SVM inverse decoupling control system of BL-IM has the characteristics of fast response and strong anti-interference ability.INDEX TERMS Bearingless induction motor system, dynamics of stator current, LS-SVM, inverse system decoupling.

Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

“…LS-SVM is based on concepts of machinery learning. For that we establish the model on two steps:  training  test In the first step, we consider a given training set [15][16][17][18][19][20] as follows:…”

Research on the Speed Sliding Mode Observation Method of a Bearingless Induction Motor

“…To meet the need for high-speed drive in the industrial field, the alternating current (AC) motor supported by all kinds of magnetic bearings is widely researched and developed [1][2][3][4][5], but it still has some obvious disadvantages, such as a limited critical speed and more magnetic suspension system power consumption, etc. [1,[6][7][8][9]. The bearingless motor (BLM) is a new AC motor [1,[10][11][12][13][14][15] proposed based on the similar structure between an ordinary AC motor stator and a magnetic bearing.…”

“…Compared with a magnetic bearing motor, the BLM has unique advantages, such as a shorter shaft and a higher critical speed, etc., giving it bright application prospects in the high-speed-drive field [1,10,11,[21][22][23][24][25]. In all kinds of BLMs, because of the robust structure and larger stiffness coefficient of magnetic suspension force, the bearingless induction motor (BL-IM) has aroused wide attention [1,[6][7][8][9]12,13,[26][27][28].…”

Adaptive Decoupling Control Using Radial Basis Function Neural Network for Permanent Magnet Synchronous Motor Considering Uncertain and Time-Varying Parameters

“…In [17], a novel nonlinear decoupling control scheme in which both the radial basis function neural network inverse (RBFNNI) and the state feedback robust pole placement (RPP) are applied is proposed to achieve high accuracy, fast response, and strong robustness. In [18], on the basis of the least square support vector machine (LS-SVM) principle, a novel LS-SVM inverse system decoupling control strategy is proposed. In this method, the system is decoupled into four second-order pseudo-linear integral subsystems.…”