Abstract
The paper is focused on presenting possibilities of applying artificial neural networks (ANN) at creating a speed controller of an induction motor drive. The presented method of control takes advantage of approximating properties of multi-layer feedforward networks. The availability of the proposed neurocontroller is verified through the Matlab simulation. The effectiveness of the controller is demonstrated for different operating conditions and motor parameter changes of the drive system.
Highlights
Most of the technical systems in practice are non-linear
The paper deals with the design of a neural controller for induction motor drive control based on a quasi-inverse model of the system
The design of the controller is based on sensor information relating to angular speed of an induction motor
Summary
Most of the technical systems in practice are non-linear. many systems can be represented without any significant loss of accuracy by an equivalent linear representation. The important nonlinear diversity is the main reason why no systematic and generally applicable theory for non-linear control design has been developed yet. It is the ability of the artificial neural networks to model nonlinear systems that can be the most readily exploited in the synthesis of non-linear controllers. The main idea of the design was to design an adaptive neuro controller of induction motor only on based input-output motor parameters. These parameters were defined as the stator voltages and currents and rotor speed. Last part of the paper demonstrates sensitivity of the speed controller to motor parameters changes
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