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Journal articles on the topic 'Predictive motor control'

Author: Grafiati

Published: 11 March 2023

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1

G. Bartsch, Arthur, Gabriel Hermann Negri, Camila R. Scalabrin, Mariana S. M. Cavalca, Ademir Nied, and José de Oliveira. "Predictive Control Approach For Permanent Magnet Synchronous Motor Drive." Eletrônica de Potência 20, no. 4 (November 1, 2015): 395–403. http://dx.doi.org/10.18618/rep.2015.4.2567.

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2

Kozakevych, Ihor, and Kyrylo Budnikov. "Predictive control of induction motor drive." E3S Web of Conferences 280 (2021): 05006. http://dx.doi.org/10.1051/e3sconf/202128005006.

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The work is devoted to the study of the possibilities of using the predictive torque control system instead of the currently widely used direct torque control system. Aspects of using the system of direct torque control of an induction motor are considered and it is found that its significant disadvantage is the variable frequency of semiconductor switches. As a further development of the direct torque control system, a predictive torque control system is analyzed, which contains blocks for estimating unmeasured state variables, as well as predicting the state of a dynamic system when applying possible control signals. The systems were compared by mathematical modeling in Matlab / Simulink.

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3

Singh, Ravinesh, Godfrey C. Onwubol, Krishnileshwar Singh, and Ritnesh Ram. "DC Motor Control Predictive Models." American Journal of Applied Sciences 3, no. 11 (November 1, 2006): 2096–102. http://dx.doi.org/10.3844/ajassp.2006.2096.2102.

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4

Singh. "DC Motor Control Predictive Models." American Journal of Applied Sciences 3, no. 11 (November 1, 2006): 2096–102. http://dx.doi.org/10.3844/ajassp.2006.2196.2102.

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5

Li, Jian, Xiaoyan Huang, Feng Niu, Chaojie You, Lijian Wu, and Youtong Fang. "Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs." Energies 11, no. 8 (August 7, 2018): 2051. http://dx.doi.org/10.3390/en11082051.

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Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy—discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.

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6

Hu, Mingmao, Feng Yang, Yi Liu, and Liang Wu. "Finite Control Set Model-Free Predictive Current Control of a Permanent Magnet Synchronous Motor." Energies 15, no. 3 (January 30, 2022): 1045. http://dx.doi.org/10.3390/en15031045.

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In this paper, a finite control set model-free predictive current control (FCS-MFPCC) of a permanent magnet synchronous motor is presented. The control scheme addresses the problems of large current fluctuation and decline of the motor system performance during parameter perturbation for the traditional finite control set model predictive current control (FCS-MPCC). Firstly, the mathematical model of the motor is analyzed and derived during parameter perturbation, and a new hyperlocal model of the motor is established based on this mathematical model. Secondly, a finite control set model-free predictive current controller is designed based on the new hyperlocal model, and a current error correction factor is introduced to correct the prediction error. Meanwhile, the stability of the observer is demonstrated via the Lyapunov theory. The simulation results show that the proposed control strategy reduces current fluctuation compared with the FCS-MPCC strategy, and the system is robust during parameter perturbation.

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7

Dumur, D., P. Boucher, and A. U. Ehrlinger. "Constrained Predictive Control for Motor Drives." CIRP Annals 45, no. 1 (1996): 355–58. http://dx.doi.org/10.1016/s0007-8506(07)63079-0.

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8

Haar, Shlomi, and Opher Donchin. "A Revised Computational Neuroanatomy for Motor Control." Journal of Cognitive Neuroscience 32, no. 10 (October 2020): 1823–36. http://dx.doi.org/10.1162/jocn_a_01602.

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We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical–subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes that each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and BG. These subcortical areas are thus engaged in domain-appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modeled? We suggest that one fundamental division is between modeling of task and body whereas another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices.

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9

Hu, Lian Jun, Xiao Hui Zeng, Hong Song, Xiao Long Huang, and Ming Liu. "The Research on Suppression Strategies for Electromagnetic Torque Ripples of Brushless DC Motors." Advanced Materials Research 910 (March 2014): 327–31. http://dx.doi.org/10.4028/www.scientific.net/amr.910.327.

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Despite of its remarkable active performances, brushless DC motors, which are widely used in mechanical engineering, have an obvious disadvantage in its high electromagnetic torque ripples. In the paper, a ripple suppression method based on predictive controls of stator currents is proposed according to analysis of causes electromagnetic torque ripples generate in commutation periods of brushless DC motors. First of all, a relative accurate prediction is acquired through DC motor on-line parameter corrections based on generalized predictive control algorithms. Then rolling optimizations make tracking errors and control qualities optimized for best control effects. And finally, minimum electromagnetic torque ripples are achieved. The simulation results show that torque ripples can be suppressed effectively with improved reliabilities by using the method proposed.

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10

HUANG, Yu-chuan, Dao-kui QU, Fang XU, and Xiao-lei REN. "Model predictive control and PID control on servo motor." Journal of Computer Applications 32, no. 10 (May 23, 2013): 2944–47. http://dx.doi.org/10.3724/sp.j.1087.2012.02944.

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11

Simi P.B, Simi P. B. "Sensorless Motor Drives with Predictive Current Control." IOSR Journal of Environmental Science, Toxicology and Food Technology 3, no. 2 (2013): 1–10. http://dx.doi.org/10.9790/2402-0320110.

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12

Hedjar, R., R. Toumi, P. Boucher, and D. Dumur. "Cascaded Nonlinear Predictive Control of Induction Motor." European Journal of Control 10, no. 1 (January 2004): 65–80. http://dx.doi.org/10.3166/ejc.10.65-80.

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13

BRONSTEIN, A. M., and C. KENNARD. "PREDICTIVE OCULAR MOTOR CONTROL IN PARKINSON'S DISEASE." Brain 108, no. 4 (1985): 925–40. http://dx.doi.org/10.1093/brain/108.4.925.

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14

Alkorta, Patxi, José A. Cortajarena, Oscar Barambones, and Francisco J. Maseda. "Effective generalized predictive control of induction motor." ISA Transactions 103 (August 2020): 295–305. http://dx.doi.org/10.1016/j.isatra.2020.04.008.

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15

Bobál, Vladimír, Petr Chalupa, Marek Kubalčík, and Petr Dostál. "Self-Tuning Predictive Control of Nonlinear Servo-Motor." Journal of Electrical Engineering 61, no. 6 (November 1, 2010): 365–72. http://dx.doi.org/10.2478/v10187-010-0056-x.

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Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model — Amira DR300.

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16

Huang, Qilan, and Min Kang. "Model Predictive Current Control of Multiphase Motor at Low Carrier Ratio." Electronics 10, no. 5 (March 3, 2021): 591. http://dx.doi.org/10.3390/electronics10050591.

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Multiphase motors have multiple control planes, and harmonics are decoupled in different planes. Multiphase motors can improve magnetic field distribution, power density and core utilization by injecting certain harmonic currents into the harmonic planes. In the harmonic plane control process, due to the switching frequency of the inverter being limited, the ratio of the switching frequency to the current frequency (the carrier ratio) of the harmonic plane is low, the digital control delay increases, and the inverter output current contains more harmonics, which makes it difficult for the proportional-integral (PI) current controller to effectively control the d-axis and q-axis currents of the harmonic plane and thus unable to track the given values stably. Moreover, the PI current controller is relatively dependent on the motor parameters. For these reasons, a model predictive current control method with predictive error compensation is proposed. Taking a nine-phase induction motor as an example, the control voltage is calculated by the cost function and corrected by the current predictive error, which realizes the current control method at a low carrier ratio. Additionally, the robustness of the control method is analyzed after the parameters of the multiphase motor have large errors. The experimental results show that the proposed method can control the current of the harmonic plane at low carrier ratio, accurately track the harmonic current commands and attain strong robustness for the motor parameters.

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17

Chen, Xiaoling, and Haihua Li. "Research and Simulation of Electromagnetic Voltage-Regulated Soft Starter Based on Predictive Control." MATEC Web of Conferences 232 (2018): 04040. http://dx.doi.org/10.1051/matecconf/201823204040.

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Aiming at the problems of large starting current and unsmooth starting of asynchronous motor, an electromagnetic voltage-regulated soft start control method based on predictive control is proposed. The model of motor soft starter based on predictive control algorithm is established. The control principle of predictive control algorithm is analyzed. The CARIMA model is used to adjust the parameters of motor starting process. With the help of MATLAB, the motor direct start model, the electromagnetic control soft starter model based on PID control algorithm and predictive control algorithm are simulated. The results show that the starting current waveform of the electromagnetic voltage regulator soft starter based on the predictive control algorithm is relatively flat, and the control algorithm can achieve a smooth start of the motor.

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18

Guzinski, Jaroslaw, and Haitham Abu‐Rub. "Speed sensorless induction motor drive with motor choke and predictive control." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 30, no. 2 (March 8, 2011): 686–705. http://dx.doi.org/10.1108/03321641111101159.

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19

Xu, Lingliang, Guiming Chen, Guangshuai Li, and Qiaoyang Li. "Model Predictive Control Based on Parametric Disturbance Compensation." Mathematical Problems in Engineering 2020 (October 7, 2020): 1–13. http://dx.doi.org/10.1155/2020/9543928.

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Model predictive control (MPC) has been widely implemented in the motor because of its simple control design and good results. However, MPC relies on the permanent magnet synchronous motor (PMSM) system model. With the operation of the motor, parameter drift will occur due to temperature rise and flux saturation, resulting in model mismatch, which will seriously affect the control accuracy of the motor. This paper proposes a model predictive control based on parameter disturbance compensation that monitors system disturbances caused by motor parameter drift and performs real-time parameter disturbance compensation. And the frequency-domain method was used to analyze the convergence and filterability of the model. The Bode diagram of measurement error and input disturbance was studied when the parameters were underdamped, critically damped, and overdamped. Guidelines for parameter selection are given. Simulation results show that the proposed method has good dynamic performance, anti-interference ability, and parameter robustness, which effectively avoids the current static difference and oscillation problems caused by parameter changes.

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20

Masoumi Kazraji, S., M. R. Feyzi, M. B. Bannae Sharifian, and S. Tohidi. "Fuzzy Predictive Force Control (FPFC) for Speed Sensorless Control of Single-side Linear Induction Motor." Engineering, Technology & Applied Science Research 7, no. 6 (December 18, 2017): 2132–38. http://dx.doi.org/10.48084/etasr.1591.

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In this paper a model fuzzy predictive force control (FPFC) for the speed sensorless control of a single-side linear induction motor (SLIM) is proposed. The main purpose of of predictive control is minimizing the difference between the future output and reference values. This control method has a lower force ripple and a higher convergence speed in comparison to conventional predictive force control (CPFC). In this paper, CPFC and FPFC are applied to a linear induction motor and their results are compared. The results show that this control method has better performance in comparison to the conventional predictive control method.

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21

Zhang, Yufeng, Zihui Wu, Qi Yan, Nan Huang, and Guanghui Du. "An Improved Model−Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on High−Gain Disturbance Observer." Energies 16, no. 1 (December 23, 2022): 141. http://dx.doi.org/10.3390/en16010141.

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Predictive current control (PCC) is an advanced control strategy for permanent magnet synchronous motors (PMSM). When the motor drive system is undisturbed, predictive current control exhibits a good dynamic response speed and steady−state performance, but the conventional PCC control performance of PMSM that depends on the motor body model is vulnerable to parameter perturbation. Aiming at this problem, an improved model−free predictive current control (IMFPCC) strategy based on a high−gain disturbance observer (HGDO) is proposed in this paper. The proposed strategy is introduced with the idea of model−free control, relying only on the system input and output to build an ultra−local current prediction model, which gets rid of the constraints of the motor body parameters. In the paper, the ultra−local structure is optimized by comparing and analyzing the equation of the state of the classical ultra−local structure and PMSM system. The system’s current state variables are incorporated into the ultra−local system modeling, as a result, the current estimation errors existing in the classical ultra−local structure are eliminated. For the unmodeled and parametric perturbation part of the ultra−local system, a high−gain disturbance observer is designed to estimate it in real time. Finally, the proposed IMFPCC strategy is compared with the conventional model−based predictive current control (MPCC) and the conventional model−free predictive current control (CMFPCC) in simulation and experiment. The results show that the current steady−state error of the IMFPCC strategy in the case of parameter variation is only 50% of the MPCC method, which proves the effectiveness and correctness of the proposed strategy.

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22

Hassan, A. A., and J. Thomas. "Model Predictive Control of Linear Induction Motor Drive." IFAC Proceedings Volumes 41, no. 2 (2008): 10904–9. http://dx.doi.org/10.3182/20080706-5-kr-1001.01847.

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23

Boucher, P., D. Dumur, and H. P. Kurzweil. "Polynomial-Predictive Functional Control (PPFC) for Motor Drives." CIRP Annals 42, no. 1 (1993): 453–56. http://dx.doi.org/10.1016/s0007-8506(07)62484-6.

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24

Mamdouh, M., and Mohammad Ali Abido. "Efficient Predictive Torque Control for Induction Motor Drive." IEEE Transactions on Industrial Electronics 66, no. 9 (September 2019): 6757–67. http://dx.doi.org/10.1109/tie.2018.2879283.

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25

Wu, Di, Peng Shi, Wei Wang, and Xi-Ming Sun. "Robust predictive control for networked control and application to DC-motor control." IET Control Theory & Applications 8, no. 14 (September 18, 2014): 1312–20. http://dx.doi.org/10.1049/iet-cta.2013.0901.

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26

Zhang, Yongchang, Haitao Yang, and Bo Xia. "Model-Predictive Control of Induction Motor Drives: Torque Control Versus Flux Control." IEEE Transactions on Industry Applications 52, no. 5 (September 2016): 4050–60. http://dx.doi.org/10.1109/tia.2016.2582796.

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27

Gonzalez, Osvaldo, Magno Ayala, Jesus Doval-Gandoy, Jorge Rodas, Raul Gregor, and Marco Rivera. "Predictive-Fixed Switching Current Control Strategy Applied to Six-Phase Induction Machine." Energies 12, no. 12 (June 15, 2019): 2294. http://dx.doi.org/10.3390/en12122294.

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In applications such as multiphase motor drives, classical predictive control strategies are characterized by a variable switching frequency which adds high harmonic content and ripple in the stator currents. This paper proposes a model predictive current control adding a modulation stage based on a switching pattern with the aim of generating a fixed switching frequency. Hence, the proposed controller takes into account the prediction of the two adjacent active vectors and null vector in the ( α - β ) frame defined by space vector modulation in order to reduce the (x-y) currents according to a defined cost function at each sampling period. Both simulation and experimental tests for a six-phase induction motor drive are provided and compared to the classical predictive control to validate the feasibility of the proposed control strategy.

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28

Zhou, Zhanqing, Zhengchao Xu, Guozheng Zhang, and Qiang Geng. "Cooperative Control for Dual Permanent Magnet Motor System with Unified Nonlinear Predictive Control." World Electric Vehicle Journal 12, no. 4 (December 17, 2021): 266. http://dx.doi.org/10.3390/wevj12040266.

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In order to improve the position tracking precision of dual permanent magnet synchronous motor (PMSM) systems, a unified nonlinear predictive control (UNPC) strategy based on the unified modeling of two PMSM systems is proposed in this paper. Firstly, establishing a unified nonlinear model of the dual-PMSM system, which contains uncertain disturbances caused by parameters mismatch and external load changes. Then, the position contour error and tracking errors are regarded as the performance index inserted into the cost function, and the single-loop controller is obtained by optimizing the cost function. Meanwhile, the nonlinear disturbance observer is designed to estimate the uncertain disturbances, which is used for feed-forward compensation control. Finally, the proposed strategy is experimentally validated on two 2.3 kW permanent magnet synchronous motors, and the experimental results show that effectiveness and feasibility of proposed strategy.

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29

Shelhamer, Mark, and Wilsaan M. Joiner. "Saccades Exhibit Abrupt Transition Between Reactive and Predictive, Predictive Saccade Sequences Have Long-Term Correlations." Journal of Neurophysiology 90, no. 4 (October 2003): 2763–69. http://dx.doi.org/10.1152/jn.00478.2003.

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To compensate for neural delays, organisms require predictive motor control. We investigated the transition between reaction and prediction in saccades (rapid eye movements) to periodically paced targets. Tracking at low frequencies (0.2–0.3 Hz) is reactive (eyes lag target) and at high frequencies (0.9–1.0 Hz) is predictive (eyes anticipate target); there is an abrupt rather than smooth transition between the two modes (a “phase transition,” as found in bistable physical systems). These behaviors represent stable modes of the oculomotor control system, with attendant rapid switching between the neural pathways underlying the different modes. Furthermore, predictive saccades exhibit long-term correlations (slow decay of the autocorrelation function, manifest as a 1/ f α spectrum). This indicates that predictive trials are not independent. The findings have implications for the understanding of predictive motor control: predictive performance during a given trial is influenced by a feedback process that takes into account the latency of previous trials.

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30

Filho, Adjair, Andrés Salazar, Luciano Júnior, Francisco Souza, José Lopes, and Werbet Silva. "An Evaluation Of Predictive Control Methods Applied To An Induction Motor." Eletrônica de Potência 22, no. 2 (June 1, 2017): 187–95. http://dx.doi.org/10.18618/rep.2017.2.2672.

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31

Kuo, Yong-Lin, Tsu-Pin Lin, and Chun Yu Wu. "Experimental and Numerical Study on the Semi-Closed Loop Control of a Planar Parallel Robot Manipulator." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/769038.

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This paper implements the model predictive control to fulfill the position control of a 3-DOF 3-RRRplanar parallel manipulator. The research work covers experimental and numerical studies. First, an experimental hardware-in-the-loop system to control the manipulator is constructed. The manipulator is driven by three DC motors, and each motor has an encoder to measure the rotating angles of the motors. The entire system is designed as a semiclosed-loop control system. The controller receives the encoder signals as inputs to produce signals driving the motors. Secondly, the motor parameters are obtained by system identification, and the controllers are designed based on these parameters. Finally, the numerical simulations are performed by incorporating the manipulator kinematics and the motor dynamics; the results are compared with those from the experiments. Both results show that they are in good agreement at steady state. There are two main contributions in this paper. One is the application of the model predictive control to the planar parallel manipulator, and the other one is to overcome the effects of the uncertainties of the DC motors and the performance of the position control due to the dynamic behavior of the manipulator.

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32

Ha, Vo Thanh, and Pham Thi Giang. "Control for induction motor drives using predictive model stator currents and speeds control." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 4 (December 1, 2022): 2005. http://dx.doi.org/10.11591/ijpeds.v13.i4.pp2005-2013.

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<span>This paper is presented for designing a new controller using the predictive model current and speed control method for the asynchronous motor. This control method is based on traditional predictive controller development to have a cascade structure similar to the rotor flux control (field-oriented control) and direct torque control (DTC). Therefore, this control method will have two control loops. Both inner and outer loop controllers use predictive power. The outer ring is speed control, while the internal circle is stator current control. The inner loop is based on the finite control set – model predictive control (FCS-MPC), while the outer ring to take full advantage of the high dynamic response of the inner circle uses the deadbeat MPC. MATLAB simulation results show that this control method has performance equivalent to traditional controllers while minimizing overshoot and having fast, on-demand response times.</span>

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33

Zhu, Keke, and Lin Ruan. "Model Predictive Control and Position Sensorless Control Algorithm for Induction Motor." Journal of Physics: Conference Series 1993, no. 1 (August 1, 2021): 012016. http://dx.doi.org/10.1088/1742-6596/1993/1/012016.

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34

Nacus, Matías A., Mónica E. Romero, Herman Haimovich, María M. Seron, and Sergio J. Junco. "Model predictive control for induction motor control reconﬁguration after inverter faults." Journal Européen des Systèmes Automatisés 46, no. 2-3 (April 30, 2012): 307–21. http://dx.doi.org/10.3166/jesa.46.307-321.

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35

Šlapák, Viktor, Karol Kyslan, Milan Lacko, Viliam Fedák, and František Ďurovský. "Finite Control Set Model Predictive Speed Control of a DC Motor." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9571972.

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The paper describes the design procedure for a finite control set model predictive control (FCS-MPC) of brushed permanent magnet DC (PMDC) machine supplied from DC-DC converter. Full order linear Kalman filter is used for estimation of an unmeasured load torque and reduction of speed measurement noise. A new cost function has been introduced with a feedforward dynamic current component and a feedforward static load current component. The performance of the proposed control strategy is compared to the conventional PI-PWM cascade speed control through the experimental verification on the 250 W laboratory prototype. Obtained results show excellent dynamic behaviour and indicate possible energy savings of the proposed speed control.

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36

De Martin, Ismaele Diego, Dario Pasqualotto, Fabio Tinazzi, and Mauro Zigliotto. "Model-Free Predictive Current Control of Synchronous Reluctance Motor Drives for Pump Applications." Machines 9, no. 10 (September 28, 2021): 217. http://dx.doi.org/10.3390/machines9100217.

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Climate changes and the lack of running water across vast territories require the massive use of pumping systems, often powered by solar energy sources. In this context, simple drives with high-efficiency motors can be expected to take hold. It is important to emphasise that simplicity does not necessarily lie in the control algorithm itself, but in the absence of complex manual calibration. These characteristics are met by synchronous reluctance motors provided that the calibration of the current loops is made autonomous. The goal of the present research was the development of a current control algorithm for reluctance synchronous motors that does not require an explicit model of the motor, and that self-calibrates in the first moments of operation without the supervision of a human expert. The results, both simulated and experimental, confirm this ability. The proposed algorithm adapts itself to different motor types, without the need for any initial calibration. The proposed technique is fully within the paradigm of smarter electrical drives, which, similarly to today’s smartphones, offer advanced performance by making any technological complexity transparent to the user.

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37

Akpunar, Adile, and Serdar Iplikci. "Runge-Kutta Model Predictive Speed Control for Permanent Magnet Synchronous Motors." Energies 13, no. 5 (March 6, 2020): 1216. http://dx.doi.org/10.3390/en13051216.

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Permanent magnet synchronous motors (PMSMs) have commonly been used in a wide spectrum ranging from industry to home appliances because of their advantages over their conventional counterparts. However, PMSMs are multiple-input multiple-output (MIMO) systems with nonlinear dynamics, which makes their control relatively difficult. In this study, a novel model predictive control mechanism, which is referred to as the Runge-Kutta model predictive control (RKMPC), has been applied for speed control of a commercial permanent magnet synchronous motor. Furthermore, the RKMPC method has been utilized for the adaptation of the speed of the motor under load variations via RKMPC-based online parameter estimation. The superiority of RKMPC is that it can take the constraints on the inputs and outputs of the system into consideration, thereby handling the speed and current control in a single loop. It has been shown in the study that the RKMPC mechanism can also estimate the load changes and unknown load disturbances to eliminate their undesired effects for a desirable control accuracy. The performance of the employed mechanism has been tested on a 0.4 kW PMSM motor experimentally for different conditions and compared to the conventional Proportional Integral (PI) method. The tests have shown the efficiency of RKMPC for PMSMs.

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38

Ramana, T. V., S. S. Manaktala, K. Valarmathi, Nitika Vats Doohan, Dasharathraj K. Shetty, Harish Kumar, and Reynah Akwafo. "Energy Auditing in Three-Phase Brushless DC Motor Drive Output for Electrical Vehicle Communication Using Machine Learning Technique." Wireless Communications and Mobile Computing 2022 (April 18, 2022): 1–14. http://dx.doi.org/10.1155/2022/9644795.

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Using predictive nonlinear optimal control, this model examines the output power of a three-phase brushless DC motor (BLDC) drive to ensure that it is stabilized (PNOC). A BLDC is a kind of electric motor that is used in a variety of applications and is one of the models of electric motors that are utilized in constant speed applications. In this motor, the movable component of the rotor created torque and the rotor rotated in a position of low reluctance; the location of the rotor is determined by the motor’s maximum inductance value. The BLDC drive controls the motor via the converter circuit, and the converter circuit ensures that the motor receives the appropriate output power. The project manager should have a thorough discussion with the team about the demagnetization of the malfunctioning BLDC motor before beginning this job. It is possible to model a machine using many existing technologies, such as electrical equivalent circuit diagram (EEC), which are based on a number of assumptions that make the analysis process or the analysis approach simpler. Despite numerical methodologies, these approach scenarios give frequency domain loop (FDL) precision frequency domain, using a suitable weight strategy to deliver high power solution creation (NM). The purpose of this essay is to integrate these two technologies in order to make contributions via the development of a new hybrid EEC-FDL model closed-loop brushless DC motor. PNOC is a driving system that uses predictive nonlinear optimal control (PNOC). The generated model is subjected to simulations under both healthy and incorrect settings, respectively. MATLAB software is utilized to construct the simulation of the control circuit, and simulation outputs are validated by experimental findings. Predictive nonlinear optimal control (PNOC) is employed to eliminate torque ripple and improve system stability.

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Li, Shujing, Zewen Wang, Yan Yan, and Tingna Shi. "Finite Set Model Predictive Control of a Dual-Motor Torque Synchronization System Fed by an Indirect Matrix Converter." Energies 14, no. 5 (March 1, 2021): 1325. http://dx.doi.org/10.3390/en14051325.

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In the dual-motor torque synchronization system fed by an indirect matrix converter (IMC), a finite set model predictive control (FCS-MPC) strategy based on a standard quadratic cost function was proposed to solve the open-loop problem of the torque synchronization error in a traditional closed-loop control strategy. Through the unified modeling of a dual-motor system, the torque synchronization error as a new state variable was involved in the switching state selection of the inverter stages, and the space vector modulation method was still used in the rectifier stage. At the same time, based on the unified prediction model, the auxiliary diagonal matrix was constructed, and the weight coefficients were solved offline by using the Lyapunov stability theory to ensure the convergence of each error term in the continuous control period. The proposed FCS-MPC strategy not only solves the problem of weight coefficient setting, but also makes it possible for a multi-motor synchronization system to expand the number of motors. The simulation and experimental results verified the effectiveness and feasibility of the control strategy. In addition, the proposed FCS-MPC strategy can ensure good torque tracking performance and synchronization performance of each motor.

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Bridgeman, Bruce. "Applications of predictive control in neuroscience." Behavioral and Brain Sciences 36, no. 3 (May 10, 2013): 208. http://dx.doi.org/10.1017/s0140525x12002282.

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AbstractThe sensory cortex has been interpreted as coding information rather than stimulus properties since Sokolov in 1960 showed increased response to an unexpected stimulus decrement. The motor cortex is also organized around expectation, coding the goal of an act rather than a set of muscle movements. Expectation drives not only immediate responses but also the very structure of the cortex, as demonstrated by development of receptive fields that mirror the structure of the visual world.

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Alsofyani, Ibrahim Mohd, and Sadeq Ali Qasem Mohammed. "Experimental Evaluation of Predictive Torque Control of IPMSM Under Speed Sensor and Sensorless Extended EMF Method." Electronics 12, no. 1 (December 24, 2022): 68. http://dx.doi.org/10.3390/electronics12010068.

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This study investigates the impact of the position information on the torque performance for an interior permanent magnet synchronous motor (IPMSM) driven by finite set–predictive torque control (FS-PTC). Unlike induction machines, IPMSMs are significantly sensitive to the rotor position during the motor operation owing to the permanent magnet of the rotor. Any misalignment or displacement of the rotor frame (d-q) can lead to poor prediction of the motor drive performance because the predictive control requires speed/position information during the prediction and evaluation by the cost function. Hence, the performance of the FS-PTC, which uses a speed encoder and senseless extended electromotive force (EMF) estimation, is evaluated and compared with respect to the speed and load conditions. Based on the investigation, the sensorless FS-PTC using the EMF method has superior torque performance and THD reduction compared to the measured speed-based PTC, particularly under large load torque. The performance evaluation of IPMSM was carried out through experimental results.

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Favato, Andrea, Paolo Gherardo Carlet, Francesco Toso, Riccardo Torchio, and Silverio Bolognani. "Integral Model Predictive Current Control for Synchronous Motor Drives." IEEE Transactions on Power Electronics 36, no. 11 (November 2021): 13293–303. http://dx.doi.org/10.1109/tpel.2021.3081827.

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Aurora, C. "Discussion on: Cascaded Nonlinear Predictive Control of Induction Motor." European Journal of Control 10, no. 1 (January 31, 2004): 81–83. http://dx.doi.org/10.3166/ejc.10.81-83.

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Hong Quang, Nguyen, Nguyen Phung Quang, Nguyen Nhu Hien, and Nguyen Thanh Binh. "Min Max Model Predictive Control for Polysolenoid Linear Motor." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 4 (December 1, 2018): 1666. http://dx.doi.org/10.11591/ijpeds.v9.i4.pp1666-1675.

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<p><span lang="EN-US">The Polysolenoid Linear Motor (PLM) have been playing a crucial role in many industrial aspects because it provides a straight motion directly without mediate mechanical actuators. Some control methods for PLM based on Rotational Motor are applied to obtain several good performances, but position and velocity constraints which are important in real systems are ignored. In this paper, we analysis control problem of tracking position in PLM under state-independent disturbances via min-max model predictive control. The proposed controller brings tracking position error converge to zero and satisfies state including position and velocity and input constraints. The simulation results validity a good efficiency of the proposed controller.</span></p>

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Witney, Alice G., Philipp Vetter, and Daniel M. Wolpert. "The influence of previous experience on predictive motor control." Neuroreport 12, no. 4 (March 2001): 649–53. http://dx.doi.org/10.1097/00001756-200103260-00007.

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Aurora, C. "Discussion on: “Cascaded Nonlinear Predictive Control of Induction Motor”." European Journal of Control 10, no. 1 (January 2004): 81–83. http://dx.doi.org/10.1016/s0947-3580(04)70330-1.

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Wang, Liuping, and Lu Gan. "Integral FCS Predictive Current Control of Induction Motor Drive." IFAC Proceedings Volumes 47, no. 3 (2014): 11956–61. http://dx.doi.org/10.3182/20140824-6-za-1003.00753.

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Verne, Santiago, Sergio Gonzalez, and Maria Ines Valla. "Induction Motor Driven by a CAMC Using Predictive Control." IEEE Latin America Transactions 12, no. 5 (August 2014): 883–88. http://dx.doi.org/10.1109/tla.2014.6872900.

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Ubare, P., and D. N. Sonawane. "Performance Assessment of the BLDC Motor in EV Drives using Nonlinear Model Predictive Control." Engineering, Technology & Applied Science Research 12, no. 4 (August 7, 2022): 8901–9. http://dx.doi.org/10.48084/etasr.4976.

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In this paper, the Nonlinear Model Predictive Control (NMPC) technique is proposed for the control of BrushLess Direct Current (BLDC) motors to address the problem of over-excitation, specifically in Electric Vehicle (EV) applications. This over-excitation increases the overall energy consumption of the machine and eventually reduces the vehicle’s driving range. The developed NMPC incorporates a nonlinear model of the BLDC motor with EV load and obtains the optimal current through the optimal voltage applied to the machine to regulate the motor torque. The proposed NMPC is compared with three conventional control techniques, the Field-Oriented Control (FOC), the Direct Torque Control (DTC), and the hybrid (the combination of DTC and FOC) control. It is observed from the simulation results that the proposed NMPC controller is more energy efficient while maintaining performance. This paper also discusses the selection of the motor based on the specified vehicle requirements. This has been done by matching the vehicle speed-torque characteristic curve with the motor’s one.

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Azab, Mohamed. "Comparative Study of BLDC Motor Drives with Different Approaches: FCS-Model Predictive Control and Hysteresis Current Control." World Electric Vehicle Journal 13, no. 7 (June 24, 2022): 112. http://dx.doi.org/10.3390/wevj13070112.

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The control techniques of the brushless DC (BLDC) motor have gained a large amount of interest in recent years, with their use being implemented in order to achieve a high-performance drive, including quick transient response and high-quality waveforms at the steady state. This paper provides a comparative study between three control schemes of BLDC motors: the direct power control scheme using a finite control set model predictive control (FCS-MPC) approach, the stator current controlled scheme using an FCS-MPC approach, and the stator current controlled scheme using ON–OFF hysteresis current controllers. The three systems were studied and investigated under the same operating conditions. The comparative study included investigating the performance of the BLDC drive in both steady state and transient operations. Qualitative and quantitative analyses were performed on the results obtained with each control scheme. The obtained results demonstrate the validity and effectiveness of the three investigated schemes in controlling the motor speed to the desired value under sudden load changes and achieving satisfactory quick transient responses. However, the results indicate the superiority of the direct power control scheme using an FCS-MPC approach over the others in terms of its minimum torque ripple, lowest torque and speed pulsations, minimum active and reactive power ripples, and high-quality waveforms of the stator currents drawn by the motor with minimum THD.

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