PID control of nonlinear motor-mechanism coupling system using artificial neural network

The basic assumption that the angular velocity of the input crank is constant in much mechanism synthesis and analysis may not be validated when an electric motor is connected to driven then mechanism. First, the controller-motor-mechanism coupling system is studied in this paper, numerically simulation result demonstrate the crank angular speed fluctuations for the case of a constant voltage supply to DC motor. Then a novel algorithm of motor-mechanism adaptive PID control with BP neural network is proposed, using the approximate ability to any nonlinear function of the neural network. The neural network are used to predicted models of the controlled variable, this information is transferred to PID controller, through the readjustment of the pre-established set. The simulation results show that the crank speed fluctuation can be reduced substantially by using feedback control.