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

Santos, Thiago Baldim Dos; Oliani, Igor; Figueiredo, Rafael; Albieiro, Daniel; Pelizari, Ademir; Filho, Alfeu J. Sguarezi

doi:10.1109/access.2022.3223385

Cited by 11 publications

(2 citation statements)

References 32 publications

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“…The PM flux will reduce 20% of the initial flux per 100 • C increase in the ambient temperature [12]. The parameter perturbation brings about the parameter mismatches of the FCS-MPC [13], which greatly affects the performance of PMSM drive systems [14].…”

Section: Introductionmentioning

confidence: 99%

Robust Model-Free Fault-Tolerant Predictive Control for PMSM Drive System

Cheng,

Zhou,

et al. 2024

IEEE Access

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Section: Introductionmentioning

confidence: 99%

Robust Model-Free Fault-Tolerant Predictive Control for PMSM Drive System

Cheng,

Zhou,

et al. 2024

IEEE Access

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“…However, during the application process, model parameter uncertainty is widespread. Various factors, such as changes in induction motor temperature, and magnetic saturation in the stator winding during operation, lead to a mismatch in stator resistance and inductance parameters 18 . Additionally, in electric vehicle applications employing the induction motor drive, significant variations in load torque frequently occur.…”

Section: Introductionmentioning

confidence: 99%

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Kasri,

Ouari,

Belkhier

et al. 2024

Sci Rep

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Enhancing the efficiency of the electric vehicle’s powertrain becomes a crucial focus, wherein the control system for the traction motor plays a significant role. This paper presents a novel electric vehicle traction motor control system based on a robust predictive direct torque control approach, an improved version of the conventional DTC, where the traditional switching table and the hysteresis regulators are substituted with a predictive block based on an optimization algorithm. Additionally, a robust predictive speed loop regulator is employed instead of the proportional-integral regulator, which integrates a new cost function with a finite horizon, incorporating integral action into the control law based on a Taylor series expansion. This technique’s primary benefit is its independence from the necessity to measure and observe external disturbances, as well as uncertainties related to parameters. The effectiveness of the suggested system was confirmed through simulation and experimental results under the OPAL-RT platform. The findings indicate that the proposed approach outperforms the conventional method in terms of rejecting disturbances, exhibiting robustness to variations in parameters, and minimizing torque ripple.

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Lyapunov-based neural network model predictive control using metaheuristic optimization approach

Stiti,

Benrabah,

Aouaichia

et al. 2024

Sci Rep

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This research introduces a new technique to control constrained nonlinear systems, named Lyapunov-based neural network model predictive control using a metaheuristic optimization approach. This controller utilizes a feedforward neural network model as a prediction model and employs the driving training based optimization algorithm to resolve the related constrained optimization problem. The proposed controller relies on the simplicity and accuracy of the feedforward neural network model and the convergence speed of the driving training based optimization algorithm. The closed-loop stability of the developed controller is ensured by including the Lyapunov function as a constraint in the cost function. The efficiency of the suggested controller is illustrated by controlling the angular speed of three-phase squirrel cage induction motor. The reached results are contrasted to those of other methods, specifically the fuzzy logic controller optimized by teaching learning-based optimization algorithm, the optimized PID with particle swarm optimization algorithm, the neural network model predictive controller based on particle swarm optimization algorithm, and the neural network model predictive controller using driving training based optimization algorithm. This comparative study showcase that the suggested controller provides good accuracy, quickness and robustness due to the obtained values of the mean absolute error, mean square error root mean square error, enhancement percentage, and computing time in the different simulation cases, and it can be efficiently utilized to control constrained nonlinear systems with fast dynamics.

show abstract

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

Cited by 11 publications

References 32 publications

Robust Model-Free Fault-Tolerant Predictive Control for PMSM Drive System

Robust Model-Free Fault-Tolerant Predictive Control for PMSM Drive System

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Lyapunov-based neural network model predictive control using metaheuristic optimization approach

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