For the full-speed sensorless control of permanent magnet synchronous motors (PMSM) used in robotic joints, conventional methods adopt a combined strategy of zero-low-speed and medium-high-speed control methods. This requires switching between methods during motor operation, which reduces the reliability and stability, making it unable to be used in robotic joint motors. In this paper, a novel full-speed range sensorless control method is proposed. The method consists of two ranges: the zero-speed and the operation range. In the zero-speed range, a high-frequency square wave injection (HFSI) method with only 20 square-wave pulses (SWPs) is employed, resulting in a short injection duration, which ensures low noise. Leveraging the bidirectional convergence property of the linear time-invariant enhanced phase-locked loop (LTI-EPLL), the initial position and NS polarity can be determined without additional signal injection, ensuring smooth startup and rotor standstill. In the operation range, based on the eletrical properties of the PMSM, a sliding-mode speed observer (SMSO) is constructed to directly estimate the speed, and the position is obtained via integration. Thus, full-speed range operation is achieved without the need to switch between methods during operation range. Finally, the feasibility of this method is validated in MATLAB/Simulink.

This paper proposes a parameter tuning strategy for the PI controller of permanent magnet synchronous motors (PMSMs) based on an improved particle swarm optimization (IPSO) algorithm. To address the limitations of traditional PI controllers with fixed parameters, which struggle to adapt to external disturbances and dynamic environmental changes, the study enhances the global search capability and convergence speed of the PSO algorithm by incorporating adaptive mutation and elite learning strategies. Experiments conducted on the MATLAB/Simulink platform compared the performance of electrical parameter self-tuning, the standard PSO algorithm, and the proposed IPSO algorithm. The results demonstrate that the PI controller tuned by the IPSO algorithm significantly outperforms conventional methods in terms of dynamic speed response, overshoot suppression (only 4.73%), and resilience to sudden load changes (settling time of 0.18 seconds). Additionally, the algorithm exhibits faster convergence and effectively mitigates the issue of local optima in time-varying multi-parameter optimization. This research provides an adaptive and robust parameter tuning solution for high-performance control of PMSMs.

Gongyao Jiang, Xinran Shi, Qiong Luo. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

In permanent magnet synchronous motor (PMSM) full speed range position sensorless control, the conventional hybrid control method usually determines the switching speed point as 10%-30% of the rated speed in the switching process empirically, which fails to effectively reduce the pulsations of position and speed. In this paper, a method is proposed to determine the switching speed point based on the scalarized estimated position error to achieve smooth switching and reduce the pulsations of position and speed during switching. The method is divided into three ranges: In zero-low and medium-high speed ranges, the estimated speed and position are obtained using the high-frequency injection method and the sliding mode observer approach, respectively. In transition range, the switching speed is determined when the position errors of the two methods are similar. After weighing the two errors, the estimated position and speed are acquired through a phase-locked loop. Finally, the effectiveness of the proposed method is verified in MATLAB/Simulink based on the built-in PMSM.

This paper proposes a sensorless speed control strategy for a permanent-magnet synchronous motor (PMSM) based on proportional resonant filtering and an improved sliding-mode observer (SMO) with an optimized critical saturation switching function. This strategy suppresses pulsation and decreases the delay to improve the accuracy of position estimation. First, an optimized critical saturation switching function was designed to output a sine wave with a boundary layer fixed at 1. This strategy kept the convergence speed of SMO constant and weakened the pulsation. However, the phase delay in the estimated back-EMF caused by introducing a low-pass filter (LPF) persists in the system. Therefore, this paper proposes a proportional resonant filter (PRF) to address this problem. The PRF, without phase delay, can extract the fundamental component and filter out the harmonic components in the estimated back electromotive force (back-EMF). The simulation results verify the effectiveness and accuracy of the proposed strategy.