High‐performance PMSM self‐tuning speed control system with a low‐order adaptive instantaneous speed estimator using a low‐cost incremental encoder

Abstract

In practical industrial applications, the control performance in a wide speed range is hard to ensure, especially under the low-speed condition with a low-cost incremental encoder, while the unknown structure parameters may also degrade the tracking performance. This paper proposes a low-order adaptive instantaneous speed estimator (AISE) and a self-tuning control strategy to promote the speed control performance in a wide speed range with unknown inertia parameters. Together with the adaptive-Kalman-filter-based AISE, a novel measurement noise variance updating scheme, which allows more appropriate compensation in the different speed range than fixed error variance, is introduced through the theoretical analysis based on probability and stochastic process. Moreover, an easy-to-implement self-tuning law, integrated with an online recursive-least-square-based parameters identification method, is developed to tune the speed controller, while the AISE is also adjusted online to ensure the control performance with a considerable variation of load inertia.All strategies were implemented in a TMS320F28335-based permanent magnet synchronous motor (PMSM) control system with a low-cost 2500-line incremental encoder, and the results demonstrated the effectiveness of the proposed techniques. KEYWORDSadaptive Kalman Filter, instantaneous speed estimator, measurement noise adaptation, motor control, parameter self-tuning Recently, high-performance PMSM speed control system has been more and more popular in many industrial applications, such as robots, manufacturing instruments, hybrid or pure electric vehicles, due to the demand of high precision, high reliability, and user-friendliness. However, there are many obstacles to achieve high-performance PMSM speed control [1][2][3][4][5][6]. Among them, how to acquire the speed information accurately and rapidly is one of the principal challenges. In traditional PMSM applications, the methods by direct detection from the position sensors, including frequency measurement and period measurement [7,8], is widely applied. These methods are usually reliable owing to the development and progress of the sensor technology in the past decades, while there are

Design of PMSM Dual-Loop Control Systems Integrating LADRC and PI Controllers via an Improved PSO Algorithm

“…To address the aforementioned problems, many control schemes have been employed to enhance the performance of PMSM control systems in recent years, e.g., optimal control [5], adaptive control [6], model predictive control [7], internal model control [8], and sliding mode control [9]. Although the abovementioned control methods can improve the performance of PMSM servo systems, they still struggle to deal with some of the inadequacies [10][11][12], such as the instability caused by oscillation in the control system, the need for accurate model information in the controller design, and the presence of internal and external uncertain disturbances, among others.…”

A Sage–Husa Prediction Algorithm-Based Approach for Correcting the Hall Sensor Position in DC Brushless Motors

“…In practical applications, motor parameters evolve over time, necessitating corresponding adjustments to certain system model parameters within the observer. Some researchers opt for an adaptive filtering method based on the Kalman filter [ 29 ] to suppress the noise in the rotor position signal. The adaptive filter, while filtering measured data, concurrently estimates some system model parameters, utilizing limited, indirect, and noisy measurements to infer information that is challenging to measure or ascertain directly [ 30 ].…”

Sensorless Control of High-Speed Motors Subject to Iron Loss

“…The vector control strategy, which is extensively used in the control of motors, requires mechanical sensors such as encoders to provide position information. However, the deployment of sensors will inevitably lead to hardware consumption, reliability reduction and cost increases [3]. Moreover, at high speeds, it is very sensitive to environmental variations such as vibration and temperature.…”