Intelligent Control of a Permanent Magnet DC Motor

Abstract

This paper presents the use of artificial intelligence for the control of a permanent magnet DC motor. The system can be found especially in robotic systems that perform repetitive operations. The control law is generated by an intelligent Reinforcement Learning algorithm. From the numerous variants of this type of algorithm, the Policy Iteration type algorithm was chosen. The algorithm was experimentally implemented using a data acquisition system and Matlab/Simulink software. The control system was tested for several variants of the load (by changing its inertia moment). The simulation and experimental results show that the intelligent control method based on reinforcement learning has better trajectory tracking and vibrations suppression.