Efficient position control of DC Servomotor using backpropagation Neural Network

The increasing growth in the use of DC Servomotors for multiple industrial applications in the last few decades have made them one of the most important system's drives. Therefore, developing an intelligent DC Servomotor position control scheme and, in particular, a DC Servomotor Neural Model based on a well-defined mathematical model that can be used for off-line simulation are very important tools for this type of system's drives. Multiple non-linear parameters and dynamic factors, such as Dead Zone, Saturation, Coulomb Friction, Backlash and load changes, are of most concern when controlling servomotors' angular position. Due to these nonlinearities and dynamic factors, conventional control schemes such as PID control may not be the best solution for some applications because they result in low system efficiency. To reduce the effect of these nonlinearities and dynamic factors and to improve the system's efficiency, an intelligent Neural Network (NN) Controller is proposed. In this paper we are reporting the use of a combination of a DC Servomotor Neural Model and a Neural Network controller. The proposed NN combination is able to deal with the non-linear parameters and dynamic factors found in the original system, and is able to improve the overall system efficiency. Off line simulation using MATLAB
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SIMULINK was used to show the final results of the controller and to compare them to a conventional PID controller.