Martin Kuchar

Estimation Techniques for Sensorless Speed Control of Induction Motor Drive

Rotor position and speed sensors are required for vector control of induction motor. These sensors are sources of trouble, mainly in hostile environments, and their application reduces the drive robustness. The cost of the sensors is not also negligible. All the reasons lead to development of different sensorless methods for rotor position and mechanical speed estimation in electrical drives. The paper deals with the speed estimators for applications in sensorless induction motor drive with vector control, which are based on application of Kalman filter and artificial neural network. The development and DSP implementation of the speed estimators for applications in sensorless drives with induction motor are described in the paper

Sensorless induction motor drive with neural network

The development and DSP implementation of AI-based speed estimator for applications in sensorless AC electrical drives are described in the paper. Rotor position and speed sensors are mostly required for vector control of induction motor. But there exist some applications in industry, where these sensors cannot be used. In the situations sensorless control technique should be used. In this paper rotor speed estimator are considered, which is based on a feedforward artificial neural network. It is demonstrated that such estimator, in contrast to conventional model-based approaches, does not depend on a knowledge of machine parameters. It is further shown that accurate estimates can be obtained in situation of varying load without the need for explicit load monitoring.

Sensorless control of variable speed induction motor drive using RBF neural network

Abstract High power of modern digital signal processors and their decreasing prices enable practical implementation of different speed estimators which are used in the sensorless control of AC drives. The paper describes application possibilities of artificial neural networks for the sensorless speed control of the A.C. induction motor drive. In the sensorless control structure of the A.C. drive, there is implemented the speed estimator which uses two different artificial neural networks for speed estimation. The first speed estimator uses a multilayer feedforward artificial neural network. Its properties are compared with the speed estimator using a radial basis function neural network. The sensorless A.C. drive was simulated in program Matlab-Simulink. The main goal of many simulations was finding suitable structure of the artificial neural network with required number of neuron units which will ensure good control characteristics and simultaneously will enable a practical implementation of the artificial neural network in the digital signal processor control system.

Induction motor drive with PWM direct torque control

This paper presents an induction motor drive using a method of direct torque and stator flux control. One disadvantage of the algorithm is a variable switching frequency of a voltage source inverter although it is used a constant period for the direct torque control processing in a digital signal controller. It is difficult to carry out a good filtration of the variable switching frequency; therefore it finally leads to higher level of interference. It is possible to avoid the problem and ensure the constant switching frequency of the voltage source inverter by the application of a pulse-width modulator in the control structure of the induction motor drive. In the paper it is described basic mathematical equations of the direct torque control of the induction motor and it is shown simulation results which confirm theoretical assumptions of the induction motor drive properties.

LiFePo battery charger with controlled rectifier

The paper focuses on the charging of LiFePO batteries with big output voltage. This voltage is necessary to feed a frequency inverter circuit with approx. output of 7.5 kW. This is a part of the solution applied during the Pre-seed project solved in the past at VSB — TUO Ostrava. Due to the unavailability of standard chargers, we provide a solution based on the Siemens 6RA80 controlled rectifier.

PI-Based Fuzzy Speed Controller with PWM Direct Torque Control for Induction Motor Drive

The paper describes a fuzzy speed controller with direct torque control for induction motor drive. Classical PI speed controller with fixed proportional and integral coefficients is not appropriate for exact motor speed regulation in a wide range of reference speed. Fuzzy logic, one of simplest soft computing techniques, can make PI controllers more flexible. In the paper, a simple fuzzy algorithm is proposed for online update coefficients of PI speed controller. The use of a pulse-width modulator in the direct torque control structure is to ensure the constant switching frequency. The control structures of the induction motor drive are implemented into a control system with digital signal processor. Experimental results confirm that speed controller with proposed fuzzy algorithm gives response of actual motor speed with lower overshoot, shorter settling time, and smaller speed accuracy than classical speed controller does.

Load torque impact on DC motor current control accuracy

The paper presents dynamic properties of a current controlled DC motor drive supplied by a transistor four-quadrant pulse converter. The contribution deals with accuracy of a torque producing armature current control loop from the view of the load torque. There is shown fundamental mathematical description for the control structure of a DC motor drive with respect to the armature current. The derivations of the steady-state current deviation for the control scheme is presented. The properties are verified by MATLAB-SIMULINK program. The simulation results are presented and discussed in the end of the paper.

Rotor flux estimation using voltage model of induction motor

The paper deals with mathematical models of an induction motor which are used for control algorithms of AC drives with induction motors. The first part of the paper describes a current model of the induction motor which is used in the model reference adaptive system as a reference model for a stator resistance estimator. The next part deals with a voltage model, which uses for the determination of the individual rotor flux vector components integration without feedback, whose basic disadvantage is time dependent offset of output quantity. In the paper, there is described a solution of the integrator with the offset elimination. The voltage model of the induction motor works with the voltage drop on the stator resistance which is depending on temperature changes. Therefore there is described the stator resistance estimator which uses the model reference adaptive system. The correct activity of the described rotor flux estimator and stator resistance estimator is confirmed by simulations in Matlab-Simulink.

Application of RBF Neural Network in Sensorless Control of A.C. Drive with Induction Motor

The paper deals with application of artificial neural networks in a speed control structure of A.C. drive with an induction motor. The sensorless control structure of the A.C. drive contains a radial basis function neural network for speed estimation. This speed estimator was compared with the speed estimator using multilayer feedforward artificial neural network. The sensorless A.C. drive was simulated in program Matlab with Simulink toolbox. The main goal was to find suitable structures of artificial neural networks with required number of neuron units which will provide good control characteristics. It was realized important simulations which confirm the rightness of proposed structures and good behavior of developed speed estimators.