Improved adaptation of EMPC with response sampling based prediction correction for the position control of DC motors

The paper describes the development and use of response sampling for prediction correction in Experience Mapping Based Prediction Controller (EMPC). This deviates from the quasi-open-loop approach employed in EMPC which is based on the Human Learning mechanism without any need for a mathematical plant model. The proposed method for Prediction Correction is employed to improve the adaptation of EMPC for the position control of DC Motors. The simulation results are provided for step changes of inertia, static friction torque, applied terminal voltage and applied external active torque. The proposed technique is implemented on a practical DC motor position control system and the results are provided for the same. Constantly changing loads are used to test the robustness of the controller and the obtained implementation results are provided.