Predictive Control Approach For Permanent Magnet Synchronous Motor Drive

Bartsch, Arthur G.; Negri, Gabriel H.; Scalabrin, Camila R.; Cavalca, Mariana Santos Matos; Nied, Ademir; Oliveira, José de

doi:10.18618/rep.2015.4.2567

Cited by 5 publications

(8 citation statements)

References 24 publications

(18 reference statements)

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“…Therefore, this work proposes the use of a multi-model approach to deal with the BLDC motor drive nonlinear relation between control action and speed. This paper can be considered a continuation from the works proposed by Bartsch et al (2015) and Negri et al (2014) previously published by the authors. Here only a single linear model to describe the motor behavior, which could be improved with the multi-model treatment, is considered.…”

Section: Introductionmentioning

confidence: 68%

“…In electrical machinery drives, there is a significant amount of studies considering the application of FCS-MPC (Preindl and Bolognani 2013;Lim et al 2014;Niu et al 2016;Xie et al 2015;Vazquez et al 2014;Bartsch et al 2017). However, there is a considerably lower number of studies about the use of CCS-MPC techniques in motor drives (Kennel and Linder 2001;Preindl 2016;Bartsch et al 2015). One reason for fewer studies could be the linear prediction model dependence of CCS-MPC, to be computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

“…The new contributions are the multi-model approach proposition and the multi-model transition rule. Previous works included integral action using delta formulation (Negri et al 2014) or a disturbance estimator (Bartsch et al 2015). The present work uses linear MPC with accumulated error prediction in its formu-lation.…”

Section: Introductionmentioning

confidence: 99%

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Procedures to Design Multi-Model-Based Predictive Controller Applied to BLDC Drive

Bartsch

Teixeira

Oliveira

et al. 2019

J Control Autom Electr Syst

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One of the main reasons that prevent the use of model-based predictive control (MPC) in power electronics and motor drive is the MPC design. The processes related to these fields have fast dynamics, and most of the MPC design guides involve the control of slow dynamic processes, which allow high sampling times and, consequently, a large time for the control action calculation. Moreover, practically only the linear model-based predictive controller can have small computational cost, which causes difficulty for MPC application in nonlinear processes as electrical motors. Therefore, this paper presents a MPC design theory for first-order control models, as the brushless direct current (BLDC) motor drive, assuming direct speed control (without current inner loop). Thus, the MPC cost function tune is obtained according to prediction model parameters, the study of optimization gain curves and, optionally, an extended closed-loop root locus analysis. To deal with the motor nonlinearities, both multi-model approach and six-step modulation are employed. This paper also discusses current peak transients, salient pole BLDC motor modeling, integral action included in the MPC control action and solutions for Hall effect sensor nonlinear speed measurement. Simulation and experimental results confirm the proposed design theory as well as the other discussed issues.

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Section: Introductionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

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Procedures to Design Multi-Model-Based Predictive Controller Applied to BLDC Drive

Bartsch

Teixeira

Oliveira

et al. 2019

J Control Autom Electr Syst

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“…This control method combines a sliding mode technique with a disturbance observer, aiming to compensate the disturbances and uncertainties of the PMSM. In [15], a predictive scheme for speed control of a PMSM was presented. A comparison with PI controller was carried out to verify dynamic performance.…”

Section: Introductionmentioning

confidence: 99%

Linear Matrix Inequality Based Synthesis of PI Controllers for PMSM with Uncertain Parameters

Koch¹,

Gabbi²,

Vieira³

et al. 2018

Eletrônica de Potência

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-This paper addresses the design of robust PI controllers for permanent magnet synchronous motors in terms of a linear matrix inequality based problem. A polytopic model of the plant is obtained and validated for the motor uncertain parameters belonging to intervals. The design procedure proposed here encompasses: i. suitable plant uncertainties inclusion and the use of practical design control constraints; ii.robust PI computation based on linear matrix inequalities with a very fast solution; iii. simulation analyses; and iv. experimental evaluations. The robust PI controller can produce superior speed regulation than a PI controller designed only for the nominal parameters, including better disturbance rejection and H ∞ performance. Experimental results confirm the viability of the proposal, which can be seen as an efficient alternative to trade off performance and robustness for PI controllers in this application.

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“…The use of mechanical devices such as encoders or resolvers enables the direct position feedback, but causes an increase in the drive system volume, in addition to increasing maintenance and manufacturing costs [2]. PMSMs are usually divided into sinusoidal PMSM, in which the magnets are distributed in a manner that the back-EMFs have sinusoidal waveforms and non-sinusoidal PMSM, in which the backEMFs present trapezoidal waveforms [3].…”

Section: Introductionmentioning

confidence: 99%

Back-EMF Based Rotor Position Estimation for Low Cost PMSM Drive Using Fully Connected Cascade Artificial Neural Networks

Negri¹,

Nazário²,

Oliveira³

et al. 2017

Eletrônica de Potência

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-Permanent Magnet Synchronous Motors (PMSMs) are widely used mainly due to their high torque per volume, high efficiency and low maintenance cost, among other advantages. To perform vector control on rotor speed and stator currents, the feedback of those variables is necessary, which can be done directly or by estimation. Measuring rotor position and speed directly requires the use of a mechanical device attached to the motor shaft, increasing the drive system volume and its maintenance cost. To overcome such disadvantages, many sensorless methods for speed estimation have been proposed. Among those methods, various strategies based on Artificial Neural Networks (ANNs) can be found. This paper presents a back-electromotive force estimator based on Fully Connected Cascade ANNs (FCC-ANNs). From the estimator, rotor position and speed can be obtained. Simulation and experimental results using automatically generated C code functions for the FCC-ANNs using fixed point notation provided rotor position estimation with simple implementation.

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Predictive Control Approach For Permanent Magnet Synchronous Motor Drive

Cited by 5 publications

References 24 publications

Procedures to Design Multi-Model-Based Predictive Controller Applied to BLDC Drive

Procedures to Design Multi-Model-Based Predictive Controller Applied to BLDC Drive

Linear Matrix Inequality Based Synthesis of PI Controllers for PMSM with Uncertain Parameters

Back-EMF Based Rotor Position Estimation for Low Cost PMSM Drive Using Fully Connected Cascade Artificial Neural Networks

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