Simulating turntable control system with neural network

To solve the turntable uncertain partial load and friction disturbance, a turntable control system was designed with neural-proportion-integral-differential (PID) theory. Because of the learning capacity of neural network, the control system showed adaptive capacity to the load disturbance. The basic theory of a self-adaptive PID controller based on back propagation (BP) neural network was described. The mathematic model of the turntable position control system was set up. A thorough analysis on the system was given by simulation and experiments. The simulation and experiment results prove that the turntable with neural-PID controller shows good track performance and capacity against the load disturbance, but the traditional PID controller hasn't. The neural-PID system can regulate the PID parameters dynamically by self-learning to fit for the load changes and make the PID parameters regulation become easier. The controller has a simple structure and can be easily realized in engineering. The results show the effectiveness of the control algorithm.