Permanent magnet synchronous motor (PMSM) drive has emerged as one of the most preferred motor drives for industrial applications owing to its distinguished advantages such as high torque density, high efficiency, and wide speed range operation. However, accurately updated knowledge about the PMSM parameters is crucial to designing a high-performance trajectory tracking controller. This paper presents a state-of-the-art comprehensive review and up-to-date progress of the offline and online parameter estimation techniques of the PMSM drives. First, the problems associated with parameter estimation of the PMSM are systematically summarized to identify the parameters having fast and accurate convergence. Second, the offline parameter estimation techniques are categorized based on the frequency and time responses. Third, the recent advances and developments of the online parameter estimation techniques for PMSMs are assessed. Finally, there is a discussion of extensive industrial applications and future research trends for the parameter estimation techniques.

This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.

This paper proposes an online identification method that can accurately estimate the stator resistance and dq-axis stator inductances for the effective model-based sensorless control of interior permanent magnet synchronous motors (IPMSMs). The proposed affine projection algorithms are uniquely designed in the estimated rotating γ-δ frame to precisely identify the parameters mentioned above. The two time-scale approaches are employed in the affine projection algorithms to estimate the three electrical parameters. Despite the electrical parameter variations due to the temperature change and magnetic saturation during operation, the rich enough data are provided to the affine projection algorithms in the discrete-time domain to accurately retrieve the updated parameters. These correctly estimated parameters are adapted to the extended back electromotive force observer for the sensorless control of IPMSM drives. Hence, the adaptation of online updated parameters makes the observer stable and robust to parameter variations as compared to the conventional observer without updated parameters. The MATLAB/Simulink-based simulation results and experimental results via a prototype IPMSM test-bed having TMS320F28335 DSP are given to verify the accurate convergence of the estimated parameters, which results into a stable sensorless control system under various operating conditions.

Industrial applications such as automation, robots, and electric vehicles are widely employing permanent magnet synchronous motors (PMSM) due to inherent characteristics such as high torque to power ratio, low maintenance, high efficiency, and wide operating range. However, PMSM drives can suffer from unwanted torque ripples caused by periodic disturbances of the electromagnetic effect. First, this article analyzes the sources of torque ripple in PMSMs. Second, the torque measurement techniques of the PMSM including torque sensors, torque observers, and indirect measurement methods are discussed to highlight their shortcomings. Third, this article categorizes and summarizes advanced control-based torque ripple minimization (CB-TRM) techniques for the PMSM by critically evaluating their impacts. These CB-TRM techniques actively control the current waveforms to ensure smooth torque generation and mitigate the effects of torque ripples. Finally, the article concludes by discussing remaining research questions and opportunities for future research on TRM techniques for PMSMs.

This paper designs a simple model reference adaptive control based speed controller (MRAC-SC) for a surface-mounted permanent magnet synchronous motor drive. The proposed adaptive scheme is designed to track a reference model that ensures the desired exponential decay of a controlled speed error trajectory. Also, the proposed MRAC method includes an adaptive compensating control term and a stabilizing feedback control term. The former is proposed to compensate for uncertain model parameters (i.e., inertia, friction, and load torque) and the latter is constructed to asymptotically stabilize the error dynamics. The asymptotic stability of the closed loop is guaranteed with both control terms using the Lyapunov approach. The comparative studies between the proposed MRAC-SC, the nonadaptive model reference speed controller, and the conventional proportional-integral SC are performed to justify a fast transient response, a good tracking possibility, and robustness against the parameter uncertainties of the proposed MRAC technique.

There has been active research in fault diagnosis of permanent magnet synchronous motors (PMSMs) with the recent increase in reliability-critical PMSM applications. Most of the research efforts are focused on the motor current signature analysis (MCSA), as it allows remote online detection of the rotor and load defects using the current measurement available in the drive. A critical limitation of MCSA or vibration analysis is their inability to distinguish the type of rotor or load defect. It is crucial for the maintenance engineer to identify the type of defect since the course of action for continued operation differs depending on the fault. In this letter, a simple automated test method for identifying mass unbalance using vibration measurement commonly available in motor drive systems is proposed. The effectiveness of the proposed method is verified on a PMSM under emulated PM demagnetization, dynamic eccentricity, and mass unbalance conditions.