High Performance Vector Control of Linear Induction Motors Using Single Neuron Controller

This paper presents an intelligent single neuron (SN) vector control scheme for the linear induction motors (LIM) in order to achieve a higher performance. The dynamic model of LIM taking into account end-effects is derived. The supervisory Delta study rule was combined with non-supervisory Hebb study rule and the improved study and control algorithms are adopted to achieve the parameter optimum and automatic adjustment on line in order to enhance the self-study capability and self-adaptation of the single neuron controller on the base of analyzing the construction and control principle of the indirect vector control. Matlab software was used to build the indirect vector control models using single neuron PI controllers and traditional PI controllers. The emulation results of the model with the single neuron PI controllers prove its superiority over the conventional.