Neural Inverse Optimal Control Implementation for Induction Motors via Rapid Control Prototyping

Quintero-Manriquez, Eduardo; Sánchez, Edgar N.; Harley, R.G.; Li, Sufei; Felix, Ramon A.

doi:10.1109/tpel.2018.2870159

Cited by 30 publications

(10 citation statements)

References 29 publications

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“…1) Generating the optimal torque command [140] or the optimal flux level/d-axis current reference [14], [141]- [143]; 2) Achieving robust controller response for induction machines against load disturbances [144], [145] and parameter variations [146]; 3) Synthesizing PWM signals for two-level [114], [137], [147] or three-level [148] voltage-fed induction machine drives; 4) Producing optimal air gap flux distribution with harmonic current injection for nontriplen multi-phase induction machines [149]; 5) Formulating an MRAS for sensorless vector-controlled IM drives based on the stator current error [150], [151], as well as the instantaneous and steady state reactive power [152]; 6) Developing full-order and reduced-order speed observers with a total least squares technique based on the minor component analysis EXIN + neuron [153]- [155]; 7) Accomplishing maximum power point tracking in induction-machine-based wind generators [156], [157]; 8) Correcting the estimated rotor speed in a sensorless nonlinear control scheme [158] of induction motors [159]; 9) Optimizing an extended Kalman filter for speed and rotor flux estimation of IM drives using particle swarm optimization [160]. 10) Performing online identification and parameter estimation of induction motors [161]- [168].…”

Section: F Othersmentioning

confidence: 99%

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Zhang¹

2021

Preprint

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This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.

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Section: F Othersmentioning

confidence: 99%

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Zhang¹

2021

Preprint

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“…Diverse important speed controllers for induction motors based on Neural Networks (NNs) have been proposed in the literature (see, e.g., [9,[36][37][38][39] and references therein). However, robust induction motor controllers are predominantly implemented by commercial conventional power converters, which provoke undesirable harmonic distortion of voltage and current waveforms [22].…”

Section: Difference With Other Important Induction Motor Controllers Based On Neural Networkmentioning

confidence: 99%

“…System identification, trajectory tracking, and state estimation of induction motors are addressed in [39]. The results are based on neural inverse optimal controller, where the control objective is to track references for rotor speed and square rotor flux magnitude.…”

Section: Neural Adaptive Control Gainsmentioning

confidence: 99%

Adaptive neuronal induction motor control with an 84‐pulse voltage source converter

Beltrán-Carbajal

Tapia‐Olvera

Valderrabano‐Gonzalez

et al. 2020

Asian Journal of Control

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This paper deals with the problem of harmonic distortion in velocity control drives for large horsepower three-phase induction motors. A new solution alternative to considerably reduce harmonic distortion in controlled large-capacity induction motors is introduced. An adaptive neural velocity reference trajectory tracking control scheme based on an 84-pulse voltage source converter for large horsepower three-phase induction motors is proposed. Desired flux modulus control is simultaneously performed. Adaptive controller parameters are adjustable online using B-spline artificial neural networks. Variable load torque of the handled mechanical dynamic system is assumed to be uncertain. Real-time estimations or measurements of dynamic load torque are unnecessary. Bézier curves are used for desired smooth motion planning to take a large induction motor from its starting towards an operating velocity. Moreover, motion planning is exploited for evasion of harmful mechanical oscillations as well as large peak values of voltages and currents. Closed-loop efficient velocity profile tracking is confirmed on a 500-hp induction motor. Comparisons with 6-pulse and 12-pulse conventional voltage source converters are also included to highlight the superior energy efficiency of the proposed 84-pulse ac motor drive. A very low total harmonic distortion of the multiple-pulse reconstructed three-phase control voltage signals is also proved. Analytical and numerical results prove the effectiveness and efficiency of the introduced 84-pulse adaptive neuronal dynamic tracking control strategy.

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“…The PI controller output will be a reference signal for the Parker DC drive which is the actuator of the speed control system. It is important to indicate that although several advanced control techniques could be used to improve the performance of the speed regulation [24], [25], the standard PI controller was selected due to its well-known simplicity and reliability for this type of application. In this work, any possible decrease in the alternator generating performance in terms of torque and dynamic response [26] due to the performance of the DC machine is neglected in the present work.…”

Section: Speed Regulationmentioning

confidence: 99%

Design and Building of an Automatic Alternator Synchronizer Based on Open-Hardware Arduino Platform

et al. 2019

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In power generation systems the synchronization of an alternator with the electrical network are an important and critical task. This paper presents the design and building of an automatic three-phase alternator synchronizer based on a low-cost open-hardware Arduino platform. The synchronization is performed by means of measuring a phase voltage of both the synchronous generator and the electrical grid; then the measured voltages are conditioned and sent to the Arduino board, which is programmed to determine the correct moment to connect the alternator to the grid. To validate the prototype, a laboratory setup has been built with the alternator driven by a commercial dc drive. The design and practical implementation of the system is proposed as a project-based learning (PBL) activity for undergraduate final year students at the University of Bío-Bío, Chile. A description of the constructed prototype and the results obtained are presented and discussed.

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Neural Inverse Optimal Control Implementation for Induction Motors via Rapid Control Prototyping

Cited by 30 publications

References 29 publications

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Adaptive neuronal induction motor control with an 84‐pulse voltage source converter

Design and Building of an Automatic Alternator Synchronizer Based on Open-Hardware Arduino Platform

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