Abstract:
The aim of adaptive control is to adjust unknown or changing manipulated variables. This is achieved by either changing adjustable parameters in the controller in order to minimize error, or by using the plant parameters to estimates the change in controlsignal. There are many different approaches to adaptive control such as self-tuning and Model Reference Adaptive Control (MRAC). Among adaptive control methods the MRAC has earned wide respect since its effectiveness is sufficiently illustrated in real time applications.
The main objective of this study is to design a speed control system for a DC Motor by using Model Reference Fuzzy Adaptive Control (MRFAC). The objective of the MRFAC is to change the rules definition in the direct Fuzzy Logic Controller (FLC) and rule base table according to the comparison between the reference model output signal and system output. The MRFAC is composed by the fuzzy inverse model and a knowledge base modifier. Because of its improved algorithm, the MRFAC has fast learning features and good tracking characteristics even under severe variations of system parameters. The learning mechanism observes the plant outputs and adjusts the rules in a direct fuzzy controller, so that the overall system behaves like a reference model, which characterizes the desired behavior.
In the proposed scheme, the error and error change measured between the motor speed and output of the reference model are applied to the MRFAC. The MRFAC is applied to a separately excited DC motor. High performances and robustness have been achieved by using the MRFAC. This is illustrated by simulation results and comparisons with other controllers such as PIDcontroller,conventional MRACand PD fuzzy controller.
