A Fixed-Point Position Observation Algorithm and System-on-Chip Design Suitable for Sensorless Control of High-Speed Permanent Magnet Synchronous Motor

Abstract

In the sensorless control algorithm of permanent magnet synchronous motors (PMSMs), there is a significant research focus on developing a high-performance position observer and a cost-effective, high-performance processor specifically designed for PMSMs. In this paper, we first reviewed the current mainstream position observation algorithms. Subsequently, we proposed a position observation algorithm based on a fixed-point nonlinear flux linkage model that introduces a variable gain model and an observer compensation model. Furthermore, we implemented the improved position observation algorithm in the circuit and designed a System-on-Chip (SOC) suitable for the sensorless control of high-speed permanent magnet synchronous motors using the ARM Cortex-M0 core. The functionality of the proposed SOC was verified on the Field Programmable Gate Array (FPGA) platform. The results demonstrate that the improved algorithm offers an excellent angle observation accuracy and fast convergence speed. The error between the observed angle and the actual angle is less than 0.2 rad. Additionally, the designed SOC balances the high efficiency of hardware implementation with the flexibility of software implementation. When running a sensorless control algorithm for PMSM, it achieves a 30.3% increase in execution efficiency, enabling support for higher frequency output PWM switching and making it suitable for the high-precision control of high-speed PMSM.

The Impact of Computational Accuracy on the Quality of Direct Drive Control

“…The second group of logical systems on which vector controllers are based are Field-Programmable Gate Arrays (FPGAs) [24,25]. These systems contain a matrix of logical elements (LEs), registers, multipliers, and input/output interfaces.…”

Unscented Kalman Filter Sensorless Permament Magnet Synchronous Motor Model Predictive Control

“…The second major group involves algorithmic methods. The Electromagnetic Force (EMF) estimator is extensively used to acquire speed and position information, as seen in [4,10,11]. Other proposed computational methods, such as those based on the basic sliding mode observer, are developed in [12,13].…”

Unscented Kalman filter Sensorless Permament Magnet Synchronous Motor Model Predictive Control

“…The second major group involves algorithmic methods. The Electromagnetic Force (EMF) estimator is extensively used to acquire speed and position information, as seen in [4,10,11]. Other proposed computational methods, such as those based on the basic sliding mode observer, are developed in [12] and [13].…”