Performance improvement of induction motor drives with model-based predictive torque control

Abstract

One of the most important advantages of using modeling and simulation software in design and control engineering is the ability to predict system behavior within specified conditions. This paper presents a novel error vector-based control algorithm that aims to reduce torque ripples predicting flux and torque errors in a conventional vector-controlled induction motor. For this purpose, a new control model has been developed that envisages flux change by applying probabilistic space vectors' torque and flux control. In the proposed predictive control algorithm, flux and torque errors are calculated for each candidate voltage vector. Thus, the optimal output voltage vector that minimizes the error is determined for the next sampling time. A MATLAB/Simulink model of the proposed control algorithm was formed and experimental studies were conducted to test the applicability and effectiveness of the proposed method using a dSpace 1104 controller board. The results obtained by comparing the effects of the conventional torque control and the proposed predictive direct torque control methods on the motor performance are presented under various speed and loading conditions. The experimental results prove that the proposed algorithm reduced the torque ripples of the motor remarkably when compared to the conventional torque control. The proposed method stands out with its simple structure due to simplified cost function with the flux model prediction approach and easy applicability with satisfactory results.

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

“…The speed controller is of PI-type. The input voltage vector (i.e., v s,αβ ) of the AFEKF observer is derived by using the optimal switching vector S opt and dc-link voltage V dc , as given in (2) and (3).…”

“…However, DTC has variable switching frequency and high torque ripples that need to be overcome. For this purpose, different approaches based on artificial intelligence, modulator inclusion, and different inverter topologies have been proposed in the literature [2][3][4][5], which increase the complexity of DTC. As the complexity of inverter topology increases, control becomes more challenging, and additional control objectives should be considered for high-performance control [6].…”

Improvements of torque ripple reduction in DTC IM drive with arbitrary number of voltage intensities and automatic algorithm modification

“…The average torque ripple T av. ripple was calculated using the following expressions (7), where m stands for number of torque samples. The obtained results are shown in Figure 12.…”

“…For this reason, a lot of scientific effort has been invested in solving this problem [4]. In [5][6][7][8] several modifications of the switching tables and the flux and torque regulators have been put forward, which should provide the ripple reduction and thus eliminate the torque error in the steady state. The key for performance enhancement of ST-DTC algorithms, i.e.…”

Finite Horizon Robust Nonlinear Model Predictive Control for Wheeled Mobile Robots

“…In recent years, the model predictive control (MPC) has been considered as an effective approach to deal with state/ input constraints along with guaranteeing many control objectives under choosing the equivalent cost functions [18,19]. e main difference between MPC and conventional nonlinear control approaches is that we give out the control sequence by solving optimization problem at each sampling time [20,21].…”