An Indirect Self-Tuning Speed Controller Design for DC Motor Using A RLS Principle

Abstract

In this paper, a kind of adaptive controllers that is indirect self-tuning control (ISTC) strategy has been used. It is used as a speed controller of a DC servo motor system to activate the rotor speed holds track of a given trajectory with minimum divergence. The proposed adaptive controllers are PID and radial basis function neural network (RBFNN) where its parameters are adjusted depending on linear adaptive neural network (LANN) parameters. By using system identification, the LANN is generated as the model for DC servo motor on-line. The difference between the DC servo motor output and the identified LANN model output adjusts weights of the LANN model on-line relying on a recursive least squares (RLS) principle by which utilize to update the adaptive controllers parameters. In adaptive control, the comprehensive behavior of the DC servo motor isn’t as sensitive as ordinary controllers with electric drives for the several styles of disturbances over a vast operating zone. Then the adaptive PID and adaptive RBFNN are compared with the conventional PID. MATLAB software used to solve system equations.