Adaptive PID control of hydraulic material testing machine system based on neural network
                                                           XU Hang dong*, MAI Yun fei
    (School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China)

Abstract: Aiming at the low precision and unstable in the operation of the material testing machine. Firstly, a mathematical model was established for the electro hydraulic position servo control system. In addition, supervised Hebb neural network learning algorithm was used, the S function by the Matlab was edited and the single neuron PID adaptive controller of Simulink simulation model about the single neuron PID adaptive controller was built. Including stability and robustness of the system, the dynamic performance of the system was evaluated on traditional PID control method or advanced PID control method, the Matlab simulation were tested. The results indicate that the system with neural network control has better stability and robustness and the optimization solves the problem of low precision and unstable in the operation of the material testing machine.

Key words: electro hydraulic position servo system; Ziegler Nichols PID; S function; single neuron PID adaptive control; Simulink simulation model.