Predictive direct torque control with reduced ripples for induction motor drive based on T‐S fuzzy speed controller

Abstract

Finite-state model predictive control (FS-MPC) has been widely used for controlling power converters and electric drives. Predictive torque control strategy (PTC) evaluates flux and torque in a cost function to generate an optimal inverter switching state in a sampling period. However, the existing PTC method relies on a traditional proportional-integral (PI) controller in the external loop for speed regulation. Consequently, the torque reference may not be generated properly, especially when a sudden variation of load or inertia takes place. This paper proposes an enhanced predictive torque control scheme. A Takagi-Sugeno fuzzy logic controller replaces PI in the external loop for speed regulation. Besides, the proposed controller generates a proper torque reference since it plays an important role in cost function design. This improvement ensures accurate tracking and robust control against different uncertainties.The effectiveness of the presented algorithms is investigated by simulation and experimental validation using MATLAB/Simulink with dSpace 1104 realtime interface. KEYWORDS direct torque control (DTC), dSpace 1104, induction motor (IM), predictive torque control (PTC), Takagi-Sugeno fuzzy logic controller (TS-FLC)

Weighting Factors Optimization of Model Predictive Torque Control of Induction Motor Using NSGA-II With TOPSIS Decision Making

“…The authors reported a reduction of torque ripple from 2 Nm to 0.5 Nm for the slow speed region. One other advantage of using the fuzzy logic controller was it drastically reduces the time of speed reversal also established experimentally in [43]. Lately, switching variation associated with FCS-MPTC, due to the absence of any switching frequency modulator, was studied in [44].…”

“…The proposed algorithm not only suggested the major improvement over the conventional FCS-MPTC technique but also considerably reduces the computational time owing to the fact that only four vectors are used instead of seven for MPTC cost function. To counter the torque ripple in steady-state or during load variation, A. Ammar et al replaced the conventional PI modulator for external speed regulation loop with a fuzzy-logic controller [43]. The authors reported a reduction of torque ripple from 2 Nm to 0.5 Nm for the slow speed region.…”

Advanced stabilizing control for electric scooters

“…Fuzzy logic control has been a common approach for controlling highly uncertain nonlinear systems in recent years. For example, a Takagi–Sugeno fuzzy logic controller is designed for speed regulation and reference torque generation to improve the performance of a predictive torque control scheme 23 . To cope with the highly nonlinear relationship between wheels and longitudinal/lateral dynamic behaviors and adapt to the critical riding conditions, we propose a three‐layer fuzzy controller and classify it according to the on‐road condition of the vehicle so that the vehicle would be driven by appropriate control commands.…”

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

“…However, classical controllers can only guarantee an acceptable regulation of nonlinear motors around a specific operating equilibrium point. Lack of robustness of classical controllers under the presence of uncertainty and exogenous perturbations is a serious issue as well [5,11,12]. Moreover, conventional controllers could demand an accurate knowledge of the mathematical model, its parameters and load torque [12].…”

“…Lack of robustness of classical controllers under the presence of uncertainty and exogenous perturbations is a serious issue as well [5,11,12]. Moreover, conventional controllers could demand an accurate knowledge of the mathematical model, its parameters and load torque [12]. Nevertheless, system parameters and demanded mechanical load torque depend on the specific application of the induction machine [7].…”