Design and Simulation of a Model Reference Adaptive Control System Using the Recursive Least Squares Method with Forgetting Factor for Gain Adjustment

Abstract

This article coupled the Recursive Least Squares Method with Forgetting Factor (RLS-FF) to a Model Reference Adaptive Control (MRAC) system and described an analysis for a second-order plant with variable and unknown parameters. In the industrial context, manufacturing processes demand to be controlled, however there are variant and even unknown parameters, a consequence of non-modeled dynamics. Thus, an algorithm capable of estimating the controller gains from the RLS-FF was proposed. Next, the MRAC simulation was carried out and the numerical results were obtained, regarding the target parameters of the control system. Through mathematical description and computational simulation, the results were promising, such as the convergence of controller gains. Therefore, this article aims to contribute with students and professionals in the field of Control and Automation, who are looking for models of adaptive control systems, in order to check, compare and implement new embedded technologies.