Sensorless finite-state predictive torque control of induction motor fed by four-switch inverter using extended Kalman filter

Abstract

Purpose Direct Torque Control (DTC) of induction motor drives is a well-established technique owing to features such as fast dynamic and insensibility to motor parameters. However, conventional DTC scheme, based on comparators and the switching table, suffers from large torque and flux ripples. To improve DTC performance, this study aims to propose and implement a sensorless finite-state predictive torque control using extended Kalman Filter in dSPACE environment. Design/methodology/approach This paper deals with the design of an extended Kalman filter for estimating the state of an induction motor model and for sensorless control of systems using this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. Findings Simulation and experimental results reveal that the drive system, associated with this technique, can effectively reduce flux and torque ripples with better dynamic and steady state performance. Further, the proposed approach maintains a constant switching frequency. Originality/value The proposed speed observer have been developed and implemented experimentally under different operating conditions such as parameter variation, no-load/load disturbances and speed variations in different speed operation regions.

Optimization of Linear Switched Reluctance Motor for Single Neuron Adaptive Position Tracking Control based on Fruit Fly Optimization Algorithm

“…Realizing high-performance dynamic control of motors under multifactor constraints has been widely investigated to overcome complex factors, such as strong coupling, nonlinear, and multitime variation [8][9][10][11][12][13][14][15][16][17][18][19][20] integral-derivative (PID) controllers occupy a dominant position in the existing industrial control field because its structure is simple and easy to implement. Maslan et al [8] applied a PID controller to an LSRM by combining three different heuristic optimization techniques namely, PIDparticle swarm optimization (PSO), fuzzy PSO, and genetic algorithm (GA)-PSO.…”

“…The control performance is poor when the rules are excessively few, whereas the performance requires a long time period when the rules are excessive. Modern control methods, such as adaptive, sliding mode, backstepping, and passive control, have been applied to the position tracking control of motors [11][12][13][14][15][16][17][18][19]. Ammar et al [11] adopted an adaptive control strategy, namely, model reference adaptive controller (MRAC) to adjust the torque and stator flux of asynchronous motors to ensure robust control and small sensitivity of machine parameters compared with traditional PI controllers.…”

“…This method heavily depends on the resistance, inductance, and other parameters of the motor. Chebaani et al [19] proposed and implemented a sensorless finite state predictive torque control system based on direct torque control (DTC) to improve its performance. The system uses DTC to extend Kalman filter as driver for induction motor model state estimation and system sensorless control.…”

Extended Kalman filter-based state estimation of MOSFET circuit

“…EKF algorithm is extensively used for state estimation of nonlinear systems (Chebaani et al, 2018;Zhao et al, 2017;Bansal and Majumdar, 2017;Nadarajan et al, 2016;Xiong et al, 2014;Janiszewski and Muszy nski, 2007). It is a recursive estimator.…”

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

“…Nowadays, model predictive control (MPC) has attracted the attention of researchers in power electronics and electrical drives field . MPC uses the dynamic model of the controlled process within the controller to anticipate in real‐time the future behavior of the process . The model predictive control in the electrical drives field can be divided into continuous state and finite‐state model predictive control.…”