Study of Adaptive PID Controller Based on Single Neuron and Genetic Optimization

It is necessary to select the proportion weights and study speeds in the design of single neuron PID controller, and in order to improve its performance, a method utilizing genetic algorithm to optimize these parameters of single neuron PID controller is presented. And some developed genetic algorithm methods are proposed, such as combining roulette wheel selection with elitist selection, using adaptive crossing and mutation operator and great mutation probability strategy, so the efficiency of optimal is improved. The simulation results of an electro-hydraulic position servo control system using adaptive PID controller based on neuron optimization show that the genetic optimize algorithm can get better control characteristics, the problem that it is difficult to select parameters of single neuron PID controller is solved.