A Variable Prediction Horizon Self-Tuning Method for Nonlinear Model Predictive Speed Control on PMSM Rotor Position System

Yao Wei,Hanhong Qi,Di Zhang,Yanjun Wei,Xiaoqiang Guo,Yang Gao,Mengyuan Li

doi:10.1109/access.2021.3084321

Yao Wei, Hanhong Qi + Show 5 more

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 14	License type: CC BY 4.0

Affiliation: Yanshan University

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Based on the receding horizon principle, cost function of nonlinear model predictive control (NMPC) becomes an accumulating type to extend prediction horizon. A nonlinear model predictive speed control (NMPSC) with prediction horizon self-tuning method is proposed in this paper, and applied into a permanent magnet synchronous motor (PMSM) rotor position control system with inner-loop of speed. The prediction horizon is improved as a positive integral discrete-time variable which needs to be adjusted according to operating states in each sampling period. Control performances such as rotor position and speed integral of time-weighted absolute value of errors (ITAEs), delay time and calculation burden are compared with the conventional control strategy, and advantages including ITAEs, delay time, weighting factor sensitivities and servo stiffness are obtained. The effectiveness and correctness are verified by simulation and experimental results.

Highlights

Due to advantages including high dynamics, easy handing and easy understanding, model predictive control (MPC) strategy is highlighted and applied in power electronics and motor driving realm recent years [1]–[3]
Since proportional controller has infinity tracking rapidity without any overshoot in theory, it is used as rotor position controller in the control system
Based on permanent magnet synchronous motor (PMSM) rotor position control system, a nonlinear model predictive speed control (NMPSC) with prediction horizon self-tuning method is proposed in this paper

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Introduction

Due to advantages including high dynamics, easy handing and easy understanding, model predictive control (MPC) strategy is highlighted and applied in power electronics and motor driving realm recent years [1]–[3]. An optimal vector is generated by modulating the voltage that satisfies the cost function in CCS-MPC, the modulation can be PWM and SVPWM [4], [5]. A vector table is pre-defined according to circuit structure and possible switching states, and the optimal vector is selected from the vector table according to the minimum cost function value [6]–[12]. Both of them belong to short prediction horizon method which covers only one sampling period Ts [13], [14]. In order to obtain better stability and dynamics, long prediction

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