Speed control of DC-DC converter fed DC motor using robust adaptive intelligent controller

DC motors have been extensively used in many industrial applications. The control of the speed of a DC motor is therefore an important issue and has been studied since the early decades of the last century. This paper presents a novel neural network (NN)-based model reference adaptive control (MRAC) to improve the trajectory tracking performance of a DC motor. In this scheme, the controller is designed using a parallel combination of the conventional MRAC scheme and an NN controller. The controller is used to change the duty cycle of the converter and, thereby, the voltage fed to the armature of the separately excited motor to regulate the speed. The operating characteristics of the proposed drive system are compared with MRAC control to verify the effectiveness under various conditions by investigating the transient responses for the step change of the speed command and the load torque. Finally, simulated and experimental results show that on the one hand the proposed controller provides high-performance dynamic characteristics, and on the other hand that this scheme is robust with respect to load disturbances.