Aplikace neuronových sítí v řízení střídavých regulovaných pohonů s asynchronním motorem

Abstract

This doctoral thesis deals with the use of artificial neural networks in the field of control of electric drives. In particular, the thesis focuses on the application of neural networks in systems intended for estimation of the state variables of an induction motor. Four sensorless vector control schemes have been implemented, in which an offline-trained feedforward neural network is utilized. The first solution uses a neural network which directly provides the estimated mechanical angular speed, the rotor flux is determined using the current model. The second solution is based on the use of the RF-MRAS speed observer, in this case, a neural network is used in the place of the reference model, it replaces the voltage model, thereby the problem of pure integration is eliminated. The main focus was on the CB-MRAS observer. Two new modifications of CB-MRAS with a neural network in the place of the current estimator have been proposed. The experimental results show an improvement in the accuracy and stability of CB-MRAS in the regenerating mode. The verification was performed employing an experimental drive equipped with a 2.2 kW induction motor and controlled by a control system which is based on the TMS320F28335 digital signal controller. In order to work with neural networks, the control system has been extended with a communication interface that allows the collection of data needed for designing and testing neural networks. For all implemented methods, the obtained results show a high level of accuracy in the low speed range.