Design and Simulation of Neuron PID Controller

In order to make up the inadequacies of the traditional PID controller for resistance furnace, a single-neuron controller is designed combined with practical experience based on supervised Hebb learning algorithm in this paper. It analyzes the meaning of each parameter in single-neuron controller and its value principle. Simulation research is conducted for a given step input. The simulation includes connection weights, the impact of K value change on system and its selection method, comparison of anti-interference capability and conditioning performance with single neuron PID controller and conventional PID controller. Simulation results show that the single-neuron controller has a good parameter self-adjusting capability and anti-interference capability and also a small overshot. The control effects in all aspects are better than those of conventional PID controller. This single-neuron controller used for a resistance furnace temperature control system can achieve better control effect than the traditional PID controller, so it has a good prospect.