Design and simulation of adaptive speed control for SMO-based sensorless PMSM drive

Kung, Ying‐Shieh; Quynh, Nguyen Vu; Huang, Chung-Chun; Huang, Liang-Chiao

doi:10.1109/icias.2012.6306234

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…Therefore, it is concluded that by changing the inverter circuitry, i.e. in a buck regulator‐type CSI, there is a significant improvement in harmonic distortion at the output end [23]. Hence, the efficiency of the proposed inverter can be improved which in turn enhances the effectiveness for various loading conditions of the PMSM drive system.…”

Section: Simulation Resultsmentioning

confidence: 99%

Updated PSO optimised fuzzy‐PI controlled buck type multi‐phase inverter‐based PMSM drive with an over‐current protection scheme

Sain

Banerjee

Biswas⃰

et al. 2020

IET Electric Power Applications

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This study confers a novel approach towards fuzzy‐PI (proportional–integral) controlled pulse width modulation (PWM) controlled permanent magnet synchronous motor (PMSM) drive operated through a conventional voltage source inverter (VSI) supplemented with buck‐converter fed current source inverter (CSI). It is observed that in a PWM controlled VSI, output voltage and current obtained contains certain harmonic distortion. A buck‐converter fed CSI is capable of minimising the voltage and current ripples prominently even with the application of PWM. Thereby, in this study, an economic, as well as robust design procedure for the buck converter, fed CSI is proposed. Moreover, a particle swarm optimisation (PSO) mechanism is introduced to optimise the performance of the proposed fuzzy logic controller. Additionally, an over‐current protection scheme of the PMSM motor is proposed. The performance of a PMSM drive is analysed in MATLAB/SIMULINK platform. Furthermore, the proposed concept is tested and verified in a real‐time test‐bench developed in the laboratory. Finally, from the performed test conditions, it can be confirmed that the proposed current control strategy of the drive system shows excellent performance in various operating conditions and can be employed in light‐weight electric vehicles.

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Section: Simulation Resultsmentioning

confidence: 99%

Updated PSO optimised fuzzy‐PI controlled buck type multi‐phase inverter‐based PMSM drive with an over‐current protection scheme

Sain

Banerjee

Biswas⃰

et al. 2020

IET Electric Power Applications

View full text Add to dashboard Cite

show abstract

“…The Equations of dynamic errors are: (27) With: The analysis of the observer convergence will be carried out using the following Lyapunov function: . Knowing that [4], [14], [22]: thus, the gains will be tuned such as:…”

Section: Sensorless Speed Control Associated With Sliding Mode Observmentioning

confidence: 99%

“…However, these sensors are expensive and very sensitive to environmental constraints such as vibration and temperature [3]. In order to overcome these problems, instead of using position sensors, a sensorless control method has been developed for control of the motor [4]. The basic principle of sensorless control is to deduce the rotor speed and position using various information and means, including direct calculation, parameter identification, condition estimation, indirect measuring and so on.…”

mentioning

confidence: 99%

A Robust Sensorless Control of PMSM Based on Sliding Mode Observer and Model Reference Adaptive System

Larbi

Gherabi

Khireddine

2017

IJPEDS

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This paper is intended to study and compare the operation of two methods for estimating the position/ speed of the permanent magnet synchronous motor (PMSM) under sliding mode control. The first method is a model reference adaptive system (MRAS). The second method based on sliding mode observer (SMO). The stability condition of Sliding Mode Observer was verified using the Lyapunov method to make sure that the observer is stable in converging to the sliding mode plane. In this paper the performances of the proposed two algorithms are analyzed using SIMULINK/MATLAB. The simulations results are presented to verify the proposed sensorless control algorithms and can resolve the problem of load disturbance effects by simulations which verify that the two closed-loop control system is robust with respect to torque disturbance rejection. Keyword:Model reference adaptive MRAS PMSM Sensorless speed control Sliding mode control SMC Sliding mode observer SMO Copyright © 2017 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:LARBI M'hamed, L2GEGI-Labotatory, University Ibn-Khaldoun of Tiaret, Algeria, 14000. Email: larbi_mh@yahoo.fr INTRODUCTIONThe permanent magnet synchronous motors attract the industrial world attention thanks to their superior advantages, for instance their higher efficiency, low inertia, high torque to current ratio. As an important application of PMSM, the motion control requires not only the accurate knowledge of rotor position for field orientation but also the information of rotor speed for closed-loop control; thus, position transducers such as optical encoders and resolvers are needed to be installed on the shaft [1], [2]. However, these sensors are expensive and very sensitive to environmental constraints such as vibration and temperature [3]. In order to overcome these problems, instead of using position sensors, a sensorless control method has been developed for control of the motor [4]. The basic principle of sensorless control is to deduce the rotor speed and position using various information and means, including direct calculation, parameter identification, condition estimation, indirect measuring and so on. The stator currents and voltages are generally used to calculate the information of speed and rotor position [5].The sliding mode control has been used to improve the robustness of the controller. during the sliding mode, this controller is insensitive to parameter variations and disturbances [6]. Therefore, many approaches for speed estimation have been investigated in the literature [7]-[8].This paper presents two methods for estimating the position and speed of a permanent magnet synchronous motor (PMSM) drive. The first method is Model Reference Adaptive System. It makes use of the redundancy of two machine models of different structures that estimate the same state variable (rotor speed) of different set of input variables [9]. The estimator that does not involve the quantity to be estimated is chosen as the reference model, and the other estima...

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“…To infer the unknown relationship, data-driven strategies, e.g., machine learning techniques, are commonly employed to tune the parameters of controllers. To this end, neural network (NN) based controller tuning is widely studied, whose control performance relies strongly on the accuracy level of nonlinear features of neural networks [21]. Accurate features as initial value require sufficient knowledge of the unknown system or well-trained hyperparameters [22], inducing large human efforts.…”

Section: Introductionmentioning

confidence: 99%

A Learning-based Online Controller Tuning Method for Electric Motors using Gaussian Processes

Yin,

Dai,

Yang

et al. 2024

Preprint

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In industrial applications, Proportional-Integral (PI) controllers are frequently employed for controlling Permanent Magnet Synchronous Motors (PMSMs) due to their fast response rate and comprehensibility. However, their control performance may deteriorate with unforeseen environmental disturbances and uncertainties. To enhance the adaptivity of the controller, Gaussian Process Regression (GPR), a machine learning technique, is used to mitigate the impact of unknown components in system dynamics in this paper. In particular, GPR is adopted to autonomously tune the parameters of the PI controller, composing a novel GPR-based PI (GPR-PI) controller that maintains both interpretability and safety, because of its theoretical prediction error bound. Moreover, the stability of the system is guaranteed under the sufficiently small designed learning rate of the PI coefficients in the gradient descent rule, indicating the tradeoff between stability and adaptivity. Then, the GPR-based PI is compared with the NN-based PI and demonstrates the priority of enlarging the stabilized speed range. Ultimately, the experiments validate the efficacy of the GPR-PI approach, showcasing a significant reduction of over 60% in response time when compared to the standard PI controller.

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Design and simulation of adaptive speed control for SMO-based sensorless PMSM drive

Cited by 13 publications

References 15 publications

Updated PSO optimised fuzzy‐PI controlled buck type multi‐phase inverter‐based PMSM drive with an over‐current protection scheme

Updated PSO optimised fuzzy‐PI controlled buck type multi‐phase inverter‐based PMSM drive with an over‐current protection scheme

A Robust Sensorless Control of PMSM Based on Sliding Mode Observer and Model Reference Adaptive System

A Learning-based Online Controller Tuning Method for Electric Motors using Gaussian Processes

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