Investigation on SVM-Backstepping sensorless control of five-phase open-end winding induction motor based on model reference adaptive system and parameter estimation

This paper deals with a new control technique applied to five-phase induction motor under open-end stator winding (FPIM-OESW) topology using the backstepping nonlinear control. The main objective is to improve the dynamics of this kind of machine, which is intended to be used in high power industrial application, where the maintenance is difficult and the fault tolerant is needed to ensure the continuous motor operating mode with minimized number of interruption. This control technique is combined with the Space Vector Pulse Width Modulation (SVPWM) to maintain a fixed switching frequency. In addition, the Model Reference Adaptive System (MRAS) concept is used for the estimation of the load torque, the rotor flux and the rotor speed to overcome the main drawbacks presented with the previous sensorless systems concepts. However, the great sensitivity to the changes of the motor internal parameters and it operating instability problems, especially in low-speed operating region, that causes an estimation error of the rotor speed, is the most disadvantage of the MRAS technique. Therefore, to solve this problem, an estimation method of the motor internal parameters such as the rotor resistance, the stator resistance and the magnetizing inductance, is proposed in this paper. Where, the main aim is to improve furthermore the control performance, to reduce the computational complexity and to minimize the rotor speed estimation error. The obtained simulation results confirm the enhanced performance and the clear efficacy of the proposed control technique under a variety of cases of different operating conditions. - 2019 Karabuk UniversityScopu

Model Reference Adaptive System (MRAS) Based Speed Sensorless Vector Control of Induction Motor Drive

No Abstrac

Sensorless Control of Two-Phase Induction Machine using MRAS Techniques

The paper presents most commonly usedcontrol techniques based on the Model Reference AdaptiveSystem (MRAS) for the Two-Phase Induction Machine(TPIM). The theoretical and experimental results areobtained using the Rotor Flux, Back EMF, and Reactivepower estimators. The main characteristic of this research istheir performance during start-up and reverse conditions.The experimental results were obtained at the no loadoperation. The estimated values of the angular speed arecompared with the data from the incremental encoder. TheMatlab/Simulink simulation software was utilized toperform the simulation results. The control techniquesimplementation and data acquisition were done by thetechnical computing device Dspace DS1103

New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

MRAS Based Speed Identification for Sensorless Field Oriented Controlled Induction Motor with online Identification of Stator Resistance

This paper presents a new online method of estimating the stator resistance of an induction motor simultaneously with the motor rotor speed for effective implementation of rotor field oriented control technique. Knowledge of stator resistance is required for the correct operation of speed sensorless control of the induction motor in low speed region. Since stator resistance varies with drive operating conditions, stable operation in low speed requires an appropriate on-line estimator for the stator resistance. The paper proposes the stator resistance and rotor speed estimation algorithm based on rotor flux based MRAS in a systematic manner. It enables the correct speed estimation and stable drive operation at low speed. The proposed parallel speed with stator resistance estimator is verified by MATLAB/SIMULINK simulation. A simulation result shows the robustness and accuracy of the proposed method and good speed tracking capability and fast responses have been achieved

New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

Speed Sensorless Vector Control of Induction Motors Using Rotor Flux based Model Reference Adaptive System

Vector Control (VC) schemes are increasingly used in Induction Motor (IM) drive systems to obtain high performance. However, in order to implement the vector control technique, the induction motor speed information is required. Different speed sensors are used to detect the speed. But in most applications, speed sensors have several problems. These problems are eliminated by speed estimation by using different speed estimation algorithms. Out of which, Model Reference Adaptive System (MRAS) techniques are one of the popular methods to estimate the rotor speed due to its good performance and simplicity. In this paper, the induction motor with Rotor Flux based Model Reference Adaptive System (RF-MRAS) rotor speed estimator is designed and validated through MATLAB/SIMULINK software package. The results of simulations show that the performance of the speed estimation is very good under different operation condition

Rotor speed estimation for indirect stator flux oriented induction motor drive based on MRAS scheme

In this paper, a conventional indirect stator flux oriented controlled (ISFOC) induction motor drive is presented. In order to eliminate the speed sensor, an adaptation algorithm for tuning the rotor speed is proposed. Based on the model reference adaptive system (MRAS) scheme, the rotor speed is tuned to obtain an exact ISFOC induction motor drive. The reference and adjustable models, developed in stationary stator reference frame, are used in the MRAS scheme to estimate induction rotor peed from measured terminal voltages and currents. The IP gains speed controller and PI gains current controller are calculated and tuned at each sampling time according to the new estimated rotor speed. The proposed algorithm has been tested by numerical simulation, showing the capability of driving active load; and stability is preserved. Experimental results obtained with a general-purpose 1-kW induction machine are presented showing the effectiveness of the proposed approach in terms of dynamic performance

A PI/Backstepping Approach for Induction Motor Drives Robust Control

International audienceThis paper presents a robust control design procedure for induction motor drives in case of modeling errors and unknown load torque. The control law is based on the combination of nonlinear PI controllers and a backstepping methodology. More precisely, the controllers are determined by imposing flux-speed tracking in two steps and by using appropriate PI gains that are nonlinear functions of the system state. A comparative study between the proposed PI/Backstepping approach and the feedback linearizing control is made by realistic simulations including load torque changes, parameter variations and measurement noises. Flux-speed tracking results show the proposed method effectiveness in presence of strong disturbances

Contribution to the Artifical Neural Network Speed Estimator in a Degraded Mode for Sensor-Less Fuzzy Direct Control of Torque Application Using Dual Stars Induction Machine

Recently one of the major topic of research is the involvement of the intelligence artificial in the control system. This paper deals with application of a new combination between two-control strategy known as fuzzy direct control of torque and then an adaptive Neuronal Speed estimator utilizing dual starts induction motor. The research discussed consist to replace the switching table used in the conventional direct control method and adaptive mechanism of the classic MRAS estimator with fuzzy controller and new neural network accordingly, both strategies can manage the degraded and normal modes. The neural networks used are the back-propagation, to reduce the training patterns and increase the execution speed of the training process. As results we achieved can be summarised as follows: 1) high degree of reliability of speed estimation even with using only one start voltages and currents and parameters; 2) Minimization of the torque and flux ripples; and                3) Minimization of the current total harmonic distortion