DSP-based sliding mode speed control of induction motor using neuro-genetic structure

In this paper, a novel approach is presented for optimization of sliding mode controller parameters. The main purpose is to optimize sliding surface slope and thickness of the boundary layer. The tuning of the electrical drive controller is a complex problem due to the many non-linearities of the machines, power converter and controller. Therefore, it is difficult to develop mathematical models of the system accurately because of unknown and unavoidable parameter variations due to saturation temperature variations and system disturbance. To solve that problem artificial neural network (ANN) is used. That is, the whole system is modeled by using ANN. Then, sliding surface slope and thickness of the boundary layer is optimized using genetic algorithms. The proposed method is applied to an induction motor. Experimental results verify that the proposed control approach is very good for complex and non-linear systems.

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