An efficient DC servo motor control based on neural noncausal inverse modeling of the plant

This study introduces an efficient speed controller for a DC servomotor based on neural noncausal inverse modeling of the motor. For this mission; first, motor mathematical model is obtained in digital form. Secondly, to be able to generate necessary inputs which drive the plant, open loop control signals, the inverse model of the system is identified by an ANN structure. Then, a neural controller is introduced immediately, which is trained by a composite error signal. During the identification and control process, an efficient numerical computing based on Newton-Raphson method simulates the dynamic of the motor. The success of the designed control system is tested by a simulation study considering real conditions to be able to occur in real-time running of the system.