A Model-Free Predictive Current Control of Induction Motor Based on Current Difference

Wang, Xing; Zhang, Yongchang; Yang, Haojin; Zhang, Boyue; Rodríguez, José; Garcia, Cristian

doi:10.1109/ipemc-ecceasia48364.2020.9368240

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…The effectiveness of the MPC scheme is highly dependent on the system modeling and parameter mismatch [8]. A model-free PCC (MF-PCC) scheme has been proposed in [9] to achieve a good steady-state performance of induction motor (IM) drive against parameter variation. Its performance is satisfactory compared to the PCC scheme, even though the used model parameters are inaccurate.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of a robust MF-PTC strategy for induction motor drive

Hossen,

Habibullah

2024

IJPEDS

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The load model-based conventional predictive torque control (PTC) strategy performs as a high-performance controller at transient and steady-state conditions. However, its performance is poor on the issue of parameters variation. A robust model-free PTC (MF-PTC) strategy has been proposed in this paper to overcome the aforementioned drawback. An auto-regressive exogenous (ARX) model instead of a load model has been used to establish a model-free controller. This model is usually formed based on their input-output transfer function. A recursive least square algorithm has been employed to estimate the unknown parameters of the ARX model. Then, an observable canonical state-space model uses those estimated parameters to achieve an accurate prediction of the control variables. The performance of the proposed scheme can be affected by the variation of resistance. A resistance estimator based on the model reference adaptive system observer has been applied to improve the robustness of the system against variation of the stator and rotor resistance, and inductance measurement uncertainty. Simulation results show that the proposed MF-PTC scheme is robust against parameters uncertainty and works well at transient and steady-state conditions.

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Section: Introductionmentioning

confidence: 99%

Performance analysis of a robust MF-PTC strategy for induction motor drive

Hossen,

Habibullah

2024

IJPEDS

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“…The whole controller only needs the input and output data without the need for the motor parameters. In [17], a model-free control is also proposed and the control signal is determined by the sampled current and the current error. In [18] and [19], linear extended state observers are employed to this task.…”

Section: Introductionmentioning

confidence: 99%

Robust Finite Control Set Model Predictive Current Control for Induction Motor Using Deadbeat Approach in Stationary Frame

et al. 2023

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The model predictive control increased its prominence in the field of induction machine drives. However, the performance of this strategy depends on the accuracy of the machine model parameters. In order to overcome this deficiency, this paper proposes a robust model predictive current control employing indirect field-oriented control in stationary reference frame using the stator current and the rotor flux vectors as state and stator voltage vector as the input. The control algorithm combines the classical model predictive control with the deadbeat approach in order to calculate the applied stator voltage vector in two components: one element considers the stator current reference, and another employs the disturbances caused due to the parameter errors, which allows to compensate the parameter mismatches in the plant model. The minimized cost function employs the predicted stator voltage vector to select the voltage vector to be applied to the stator terminals of the motor. The control method performance was verified using an experimental test bench analyzing the system steady-state and dynamic actions. In this way, the results corroborate the proposed controller robustness against parametric variations.

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“…Solutions that do not use the mathematical model of the motor can also be found in the literature. A method for predicting IM stator currents using the difference between the present and historical values of current samples is presented by Wang et al (2020). The method is robust to changing machine parameters, which is important in real-world conditions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Stator Current Prediction Capabilities in Induction Motor Drive Using the LSTM Network

Teler

Orłowska-Kowalska

2023

Power Electronics and Drives

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In modern areas of knowledge related to electric drive automation, there is often a need to predict the state variables of the drive system state variables, such as phase current and voltage, electromagnetic torque, stator and rotor flux, and others. This need arises mainly from the use of predictive control algorithms but also from the need to monitor the state of the drive to diagnose possible faults that have not yet occurred but may occur in the future. This paper presents a method for predicting stator phase current signals using a network composed of long-short-term memory units, allowing the simultaneous prediction of two signals. The developed network was trained on a set of current signals generated by software. Its operation was verified by simulation tests in a direct rotor flux-oriented control (DRFOC) structure for an induction motor drive in the Matlab/Simulink environment. An important property of this method is the possibility of obtaining a filtering action on the output of the network, whose intensity can be controlled by varying the sampling frequency of the training signals.

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A Model-Free Predictive Current Control of Induction Motor Based on Current Difference

Cited by 8 publications

References 9 publications

Performance analysis of a robust MF-PTC strategy for induction motor drive

Performance analysis of a robust MF-PTC strategy for induction motor drive

Robust Finite Control Set Model Predictive Current Control for Induction Motor Using Deadbeat Approach in Stationary Frame

Analysis of the Stator Current Prediction Capabilities in Induction Motor Drive Using the LSTM Network

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