Adaptive PID Control Based On Rbfnn Identification

The character of PID controller is simple structure , good adaptability and great robustness. But the simple PID controllers 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 control effect in the on-line parameter 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 .The NN system structure must be high precision of identification to provide a more accurate object mode. Radial basis function neural network (RBFNN) is a kind of three-layer feedforward neural network with single hidden layer, there is great difference between it's tructure and learning algorithms with BP neural network's. So, in the paper, the NN PID is used to achieve PID parameters self-adjustment on RBFNN identification .The works are listed as follows:(1) PID control and its basic parameters auto-tuning methods and the NN PID controller are introduced, then an improved single neural adptive PID controller is presented and PID control based on BPNN is studied in detail.(2) The improved adaptive Gradient-descent algoritms is proposed to RBFNN identification. At the same time , three kinds of controllers is studied. There the adaptive Gradient-descent algoritms, a single neural element and a BP neural network is utilized to achive PID parameters self-adjustment, a RBF neural network is used to identify the controlled plant on-line.(3) Hybrid hierarchy genetic algorithms is introduced to configure the structure and parameters of of RBFNN, and the RBFNN identification results are compared with which produced by Gradient-descent algorithms. The simulation results show the identification effects of RBFNN which is optimized by hybrid hierarchy genetic algorithms is better than those of which is optimized...