Analysis of Current Predictive Control Algorithm for Permanent Magnet Synchronous Motor Based on Three-Level Inverters

Abstract

In some high-precision control applications, such as defense industry and computerized numerical control machine tools, fast and stable electromagnetic torque response is required to ensure the high dynamic performance of the system, while traditional PI control often cannot meet its requirements. For this purpose, a predictive control algorithm based on the deadbeat control algorithm is proposed in order to improve the performance of the motor current loop. In order to solve the problem that the conventional deadbeat control algorithm has a large dependence on system parameters and low robustness, this paper proposes an improved deadbeat control scheme for the permanent magnet synchronous motor based on the three-level inverters. The scheme is based on the second theorem of Lyapunov stability. The improved deadbeat control algorithm can achieve a good output waveform when the switching frequency is not high and the response speed is fast. The robustness of the system is improved, and there are good characteristics in reduced torque ripple. Compared to the traditional PI regulators, the improved deadbeat control can quickly track the current commands without overshoot and oscillation and suppress torque ripple. The simulation and experimental results show that the improved deadbeat control proposed in this paper has good dynamic and static performance.

Current-Controller-Free Self-Commissioning Scheme for Deadbeat Predictive Control in Parametric Uncertain SPMSM

“…The DBPC can achieve higher gain compared with the PI current controller [9], which means the PI speed controller works with DBPC can also increase its gain compared with the PI speed controller works with PI current controller. To demonstrate this in detail, comparative experiments of PI speed controller when working with different current control schemes (PI/DBPC) are shown in Fig.…”

“…The predictive current control (PCC) has been widely studied recently due to its ideal transient state performance [4]- [9]. Generally speaking, the PCC can be categorized into two sets: the model predictive control (MPC) and the deadbeat predictive control (DBPC) [5].…”

Robust Current Predictive Control-Based Equivalent Input Disturbance Approach for PMSM Drive

“…Predictive control can achieve good tracking control and can be completed easily. For the current control of PMSM, predictive control methods mainly includes deadbeat predictive control [10,11] and model predictive control (MPC) [12][13][14][15]. In comparison, the requirement for high computational resources is one of the main drawbacks in a drive with MPC.…”

Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification

“…IPMSM has been extensively utilized in the fields of electromechanical drives, servo systems, and automotive industry due to its strong reliability, high efficiency, high power density, and a large torque-ampere ratio [1,2]. In order to achieve excellent performance of both dynamic and steady-state current control, many researches focused on the control algorithms of IPMSM, and the most common control algorithms are hysteresis control, proportional-integral (PI) control [3], MPC [4,5], and nonlinear control [6].…”

“…However, there are unavoidable challenges in MPC systems, when the model parameters are mismatched, the control effect will be ruined. MPC relies heavily on accurate IPMSM mathematical model, which means that the disturbance of motor parameters and the delay of the digital control system would deteriorate the tracking performance of the stator current [4], while the performance of current control is the key factor to the whole IPMSM control system. Many novel methods have been drawn out to solve the problems mentioned above.…”