Since the parameters in control system models are usually unknown in industrial applications

this paper tries to identify the system parameters by using the modified recursive prediction error algorithm for neural networks

and then design a self tuning PID controller via the pole-assignment method. Compared with the neural network identification based on the gradient learning algorithm and conventional PID

the method in this paper has simple structure of parameters

sustainable adjustment of neuron weights and quick calculation speed. Furthermore

this digital PID controller also enjoys good performance and easy application. And the simulation results verify that the effectiveness of this identification algorithm as well as the controller in this paper.