Development and experimental realization of an adaptive neural-based discrete model predictive direct torque and flux controller for induction motor drive

Sahu, Abhimanyu; Mohanty, Kanungo Barada; Mishra, Rabi Narayan

doi:10.1016/j.asoc.2021.107418

Cited by 14 publications

(3 citation statements)

References 26 publications

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“…In this work, the proposed ANNbased controller shows better performance and lower THD of the output voltage compared to that of the conventional FCS-MPC scheme, considering different linear and nonlinear loading conditions. Soon afterward, the ANN learning technique was investigated with more complex systems such as modular multilevel converters (MMC) [33], [34], flying capacitor multi-level inverters (FCMLIs) [32], [35], DC microgrid applications [36], and drive systems [37], [38]. Furthermore, several popular neural network architectures, such as time-delay neural networks (TDNNs) and recurrent neural networks (RNNs), have been widely applied to various applications due to their capabilities to effectively learn the temporal dynamics of the signal even from short-term feature representations [39], [40].…”

Section: Introductionmentioning

confidence: 99%

From MPC-Based to End-to-End (E2E) Learning-Based Control Policy for Grid-Tied 3L-NPC Transformerless Inverter

et al. 2022

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Section: Introductionmentioning

confidence: 99%

From MPC-Based to End-to-End (E2E) Learning-Based Control Policy for Grid-Tied 3L-NPC Transformerless Inverter

et al. 2022

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“…The amplitude and frequency of the sinusoidal reference is governed by the controller. A number of methods are proposed for speed control of induction motors, including rotor-flux-oriented control [1,2], vector control [3], direct torque control [4,5], sliding mode control [6], neural network based control methods [7,8] and a model predictive control method [9]. Furthermore, various speed sensorless control methods have been proposed, among them are a method based on DC-link measurements [10], speed measurement based on rotor-slot-related harmonic detection in machine line current [11], field-orientation control with a speed estimation scheme [11] and speed estimation using adaptive nonlinear observers [12] and sliding mode observer [13].…”

Section: Introductionmentioning

confidence: 99%

Online Efficiency Optimization and Speed Sensorless Control of Single-Phase Induction Motors

2021

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Single phase induction motors (SPIM) are widely used in residential and commercial applications. Enhancement of efficiency of SPIMs can lead to huge energy savings. This paper presents a novel mechanical sensorless control method for SPIM drives. In this method, a machine learning algorithm is used to estimate the slip based on the ratio of main and auxiliary winding currents. To enhance the efficiency, the terminal voltage is reduced under light load conditions. The optimal operating voltage is implicitly obtained by equating the ratio of main and auxiliary winding currents to its optimum value. This optimal operating point is first calculated based on the frequency from a lookup table and then updated by using gradient descent algorithm. This way, the optimal operating point is realized despite motor parameter variations. The proposed scheme is suitable for low-power applications where working at different speeds and load torques is demanded, such as ventilation systems and various household appliances. Simulation results are presented to verify the efficacy of the proposed method.

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“…Sabzevari et al [18] proposed state-space recurrent neural network controller for three phase power converter and showed that the control scheme is more robust compared to conventional MPC. Sahu et al [19] developed neural network based dicrete model predictive controller for induction motor drive based on direct torque and flux and reported reduction of ripples in flux, torque and current compared with conventional PI direct torque and flux control. Further, the authors used various intelligence techniques in power converters [20]- [24].…”

mentioning

confidence: 99%

Application of machine learning controller in matrix converter based on model predictive control algorithm

Gounder,

Subramanian

2023

IJPEDS

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Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.

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Development and experimental realization of an adaptive neural-based discrete model predictive direct torque and flux controller for induction motor drive

Cited by 14 publications

References 26 publications

From MPC-Based to End-to-End (E2E) Learning-Based Control Policy for Grid-Tied 3L-NPC Transformerless Inverter

From MPC-Based to End-to-End (E2E) Learning-Based Control Policy for Grid-Tied 3L-NPC Transformerless Inverter

Online Efficiency Optimization and Speed Sensorless Control of Single-Phase Induction Motors

Application of machine learning controller in matrix converter based on model predictive control algorithm

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