Improvement of DC Motor Velocity Estimation Using a Feedforward Neural Network

This paper proposes a method for improving the DC motor velocity estimations and the estimations obtained from the state observer, when the system operates with large moments of inertia. First, the state observer for estimating velocity and DC motor position, is designed. Then, the variable structure controller is formed using estimated position and velocity values. State observer and designed controller are implemented in default system control logic. Dependences between estimated velocities and moments of inertia are established and presented by experimental results. It is noted that velocity time responses of the designed controller are not as expected when the system operates with large moments of inertia on the motor shaft. The feedforward neural network is empirically designed and implemented in control logic with purpose to solve poor velocity estimations and to improve overall system performances. It is experimentally shown that an artificial network improves estimation quality of the observer and overall control of the system for different input signals.