Neural network PID controller auto-tuning design and application

Abstract

The simple PID controller can't get the satisfied degree-especially for the time-varying objects and non-linear systems-the traditional PID controllers can do nothing for them to non-linear systems-the NN PID controller has a good controller effect in the non-line premature turning and optimizing. The NN PID controller can make both neural network and PID control into an organic whole-which has the merit of any PID controller for its Simple construction and definite physical meaning of parameters, and also has the self learning and adaptive functions of a neural network. Radial basis function neural network (RBFNN) is a kind of three-layer feed forward neural network with single hidden layer, there is Great difference between it's structure and learning algorithms with BP neural network's so, in the Paper, the NN PID is used to achieve PID parameters self adjustments on RBF NN identification, an improved single neural adaptive PID controller is presented and PID control based on BPNN is studied in detail. A new self-adaptive learning model of RBF neural network as established successfully.