Single Neuron PID Model Reference Adaptive Control Based on RBF Neural Network

Radial basis function (RBF) neural network (NN) is powerful computational tools, which have been used extensively in the areas of pattern recognition, systems modeling and identification due to the advantages of simple construction, adaptability and robustness. This paper presents a novel approach of single neuron PID model reference adaptive control (MRAC) control based on RBF neural network on-line identification. A RBF network is built to identify the system on-line, and then it constructs the on-line reference model, implements self-learning of controller parameters by single neuron controller, and thus achieves on-line regulation of controller´s parameters. The simulation result shows that the proposed method can construct processing model through on-line identification and then give gradient information to neuron controller, it can achieve on-line identification and on-line control with high control accuracy and good dynamic performance