Artificial intelligent speed control strategies for permanent magnet DC motor drives

The paper presents a prototype laboratory implementation and control system validation for two artificial intelligence (AI) based speed control strategies to be used with the permanent magnet (PM) DC motor drives. These two AI strategies include a fuzzy logic based controller (FLC) and an online tunable artificial neural network (ANN) based controller. The use of these two AI based speed controllers is motivated by motor drive system parameter uncertainties and the unknown nonlinear mechanical load characteristics over the extended range of operating conditions. The experimental results validate the good dynamic speed tracking performance of the two proposed speed controllers. Since the controlled motor drive system precise parameter values are not required in the control system implementation, the controlled motor drive system is robust and insensitive to the system parameters, load excursions, and operating condition changes