Performance Analysis of DTC-SVM Sliding Mode Controllers-Based Parameters Estimator of Electric Motor Speed Drive

Abstract

This paper is concerned with a framework which unifies direct torque control space vector modulation (DTC-SVM) and variable structure control (VSC). The result is a hybrid VSC-DTC-SVM controller design which eliminates several major limitations of the two individual controls and retains merits of both controllers. It has been shown that obtained control laws are very sensitive to variations of the stator resistance, the rotor resistance, and the mutual inductance. This paper discusses the performances of adaptive controllers of VSC-DTC-SVM monitored induction motor drive in a wide speed range and even in the presence of parameters uncertainties and mismatching disturbances. Better estimations of the stator resistance, the rotor resistance, and the mutual inductance yield improvements of induction motor performances using VSC-DTC-SVM, thereby facilitating torque ripple minimization. Simulation results verified the performances of the proposed approach.

Torque Ripple Reduction in DTC Induction Motor Drive

“…Fuzzy logic and/or neural networks based DTC-SVM. 4. Variable-structure control (VSC) [8] based DTC-SVM.…”

“…The Direct torque control of induction motors based on discrete space vector modulation using adaptive sliding mode control was proposed by Ben Salem and Derbel [3], the results shows the effectiveness and the robustness of the DTC-discrete SVM adaptive sliding mode control of induction motors. The variations of induction motor parameters is shown by Ben Salem and Derbel in their subsequent two publications namely Performance Analysis of DTC-SVM Sliding Mode Controllers-Based on Estimator of Electric Motor Speed Drive [4], and DTC-SVM Based Sliding Mode Controllers with Load Torque Estimators for Induction Motor Drives [5] respectively. The advantages of this direct torque control method is improved efficiency and fast response of torque in dynamic conditions [6,7].…”

Online estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter

“…Considering the studies (Bednarz and Dybkowski, 2019; Ben Salem and Derbel, 2014; Hasan and Husain, 2009; Mapelli et al, 2017; Teja et al, 2012; Yang et al, 2017, 2019) focusing on the parameter estimation of IM, Hasan and Husain (2009) present a Luenberger-sliding mode observer, a fourth-order observer estimating R r and R s , to compensate for the effects of parameter variations, while Teja et al (2012) introduce the stator voltage and current fictitious quantity ( X )-based MRAS (X-MRAS) approach for R s estimation of IM when rotor mechanical speed ( ω m ) is available. To investigate the effect of parameter variations on the drive system, a sliding mode controller–based parameter estimator is designed in Ben Salem and Derbel (2014) performing online estimation of R r , R s , and magnetizing inductance ( L m ) in simulations. Mapelli et al (2017) present a reactive power– and a motor torque–based two different MRAS algorithms carried out in terms of sensitivity analysis under different load and speed conditions, and compare the robustness of these metho...…”

“…Bednarz and Dybkowski (2019) present online estimation of R r and R s by using MRAS algorithms, which are active power–based MRAS (P-MRAS) and reactive power–based MRAS (Q-MRAS). These deterministic-based approaches (Bednarz and Dybkowski, 2019; Ben Salem and Derbel, 2014; Hasan and Husain, 2009; Mapelli et al, 2017; Teja et al, 2012; Yang et al, 2017, 2019) are sensitive to parameter variations, and their performances are affected by noise conditions, which can be classified as process ( Q ) and measurement ( R ) noises. On the other hand, stochastic-based methods such as EKF and UKF, compatible with the uncertainties related to the IM model (Yildiz et al, 2020a), are also widely used for the state and parameter estimations of IMs since they take into account Q and R by using stochastic IM model (Zerdali, 2020) and provide simultaneously high-performance estimation of both states and parameters of the IMs (Bednarz and Dybkowski, 2019).…”

Direct Torque Control Strategies of Induction Machine: Comparative Studies

“…The constant switching frequency DTC using the space vector modulation (DTC-SVM) is a well discussed solution; in order to improve the DTC-SVM performances, hysteresis comparators of electromagnetic torque and stator flux have been replaced by PI controllers, [5,6]. The main drawbacks of DTC-SVM using PI controllers are the sensitivity of the performances to the system-parameter variations and the inadequate rejection of external disturbances and load changes [7][8][9][10][11]. To cope with this disadvantage, it is suggested to replace the conventional regulators used for the speed control, flux, and electromagnetic torque by intelligent controllers by fuzzy logic to make the controls more robust against the disturbances of the parameters of the machine.…”