Sliding-Mode-Observer-Based Position Estimation for Sensorless Control of the Planar Switched Reluctance Motor

Sun, J. J.; Cao, Guang-Zhong; Huang, Su-Dan; Peng, Yeping; He, Jiangbiao; Qing-quan, Qian

doi:10.1109/access.2019.2913702

Cited by 28 publications

(12 citation statements)

References 35 publications

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“…As the speed of the motor increases, the single cycle becomes very short, so it can be seen that the speed of the motor does not change during the single cycle. As a result, the rotation speed does not change and the inductance characteristics do not change in a single cycle [4]. Therefore, the position information of the rotator can be estimated as long as a particular position in the electrical cycle is determined, and the Position-free control and speed estimation of the motor can be achieved.…”

Section: Methods For Calculating Rotor Position Using Slope Of Phase ...mentioning

confidence: 99%

Improvement of phase inductance slope method for Without Position Sensor control of switched reluctance motor

Qiu

Sun

2022

J. Phys.: Conf. Ser.

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At present, the research hotspot of switched reluctance motor is to realize the accurate control of motor without position sensor. Among many control methods, the phase inductance slope method is concerned by researchers because it does not need to add additional power supply circuit, and does not need to calculate the flux linkage and inductance curve in advance. Aiming at the defect that the phase inductance slope algorithm can not advance the opening angle and the position signal error is easy to appear near the opening angle, this paper makes an improvement and verifies the experiment.

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Section: Methods For Calculating Rotor Position Using Slope Of Phase ...mentioning

confidence: 99%

Improvement of phase inductance slope method for Without Position Sensor control of switched reluctance motor

Qiu

Sun

2022

J. Phys.: Conf. Ser.

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“…Regarding constraints, the motor model should be firstly considered. In order to form the optimization problem with less computations, the plant model in ( 1) is simplified as (3).…”

Section: B Optimization Problem Formation and Solutionmentioning

confidence: 99%

Data-Driven Digital Direct Position Servo Control by Neural Network With Implicit Optimal Control Law Learned From Discrete Optimal Position Tracking Data

et al. 2019

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To get better control performance in motor control, more and more researches tend to apply non-linear control laws in the field of motor control. However, most conventional non-linear control theory is based on explicit model of controlled object and often resulting in complexity. Besides, the control parameters tuning is mainly aiming at stability of the system. No valid direct performance-oriented nonlinear control theory has been proposed. Facing the limitations, this paper presents a direct motor position control in an implicit data-driven manner. Unlike conventional non-linear motor controls that are based on explicit models and with stability-based parameters tuning, this study gives performance-oriented non-linear control by mastering non-linear discrete optimal control law in an implicit data-learning manner. Firstly, optimal data of position tracking problem is obtained by solving optimization problem. Secondly, the implicit discrete optimal control law hidden in data is learned by a BP neural network. Finally, the learned control law is implemented in real-time control to reproduce optimal control performance. Simulation and experiment results validated the feasibility of the data-driven controller, which could be helpful for performance-oriented non-linear control designs. The merits and further improvements are also discussed.INDEX TERMS Position control, implicit discrete optimal control, artificial neural network, motor, data learning.

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“…Compared with speed control technology with a speed sensor, sensorless technology can observe rotor flux and speed through designed observers. At present, commonly used observers include the Kalman filtering algorithm [21], model reference adaptive algorithm [22], and sliding mode observer algorithm [23], [24]. Among them, the sliding mode observer algorithm is simple in design and has a strong dynamic response-ability, but it may cause chattering of the system due to its attributes [25].…”

Section: Introductionmentioning

confidence: 99%

Design of Model-Free Speed Regulation System for Permanent Magnet Synchronous Linear Motor Based on Adaptive Observer

Zhang

Feng

et al. 2022

IEEE Access

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To simplify the system structure of permanent magnet synchronous linear motor (PMSLM), optimize the kinematic performance of the control system, and further improve the speed tracking accuracy of PMSLM, a model-free speed regulation system design of PMSLM based on the adaptive observer is proposed. Aiming at the control instability problem caused by the parameter uncertainty of PI control in PMSLM, a model-free speed controller (MFC) based on the ultra-local model is proposed, which has strong robustness to the uncertainty of motor parameters. Meanwhile, the actual model of PMSLM is used to build the reference model of the adaptive observer, and the adjustable model is designed with the current equation of PMSLM, to identify the speed of PMSLM. The design not only reduces the dependence of the speed controller on the motor parameters, but also simplifies the complexity and cost of the control system, and improves the control performance and anti-interference ability of the control system. The MFC and the observer based on the model reference adaptive system (MRAS) are built by simulation software and applied to the PMSLM system to verify the superiority of the designed control system. Compared with PI controller, sliding mode controller (SMC), and sliding mode observer (SMO), this control method not only simplifies the control system in structure, but also improves the accuracy of PMSLM tracking speed, improves the dynamic response performance of PMSM control system, and optimizes the control ability of the control system.

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Sliding-Mode-Observer-Based Position Estimation for Sensorless Control of the Planar Switched Reluctance Motor

Cited by 28 publications

References 35 publications

Improvement of phase inductance slope method for Without Position Sensor control of switched reluctance motor

Improvement of phase inductance slope method for Without Position Sensor control of switched reluctance motor

Data-Driven Digital Direct Position Servo Control by Neural Network With Implicit Optimal Control Law Learned From Discrete Optimal Position Tracking Data

Design of Model-Free Speed Regulation System for Permanent Magnet Synchronous Linear Motor Based on Adaptive Observer

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