Inverse Ann Based Control of DC Motor

This work describes the implementation of an inverse-based neural (ANN) controller to control the speed of a DC motor. Artificial neural networks have been shown to have high learning and fault tolerance capabilities. It has also been shown to offer the optimal answer for all non-linear system estimation and control problems. The DC motor is modelled in state space, and the simulation is performed in the MATLAB environment. The simulation is run for 120 seconds, and the results are compared to those of a typical auto tuned-PI controller. The results reveal that the inverse neural controller outperformed the standard controller.