Abstract
This paper presents a novel approach of single neuron PID control for switched reluctance motors based on RBF neural network on-line identification. The method is adjusted to the nonlinearity of switched reluctance motors, and use the single neurons capable of self-learning and self-adaption to form the single neuron adaptive controller of switched reluctance motors. It not only has simple structure and strong robustness, but but can adapt to environmental changes. Also we construct a RBF network system to identify the system online, and to build its online reference model, using a single neuron controller to achieve self-learning of its parameters, in order to achieve their online adjustment, and to obtain better control effect.
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