Optimization of Application Control Using LQR and LQT Approaches: A Study on Community-Based Development Programs

Abstract

The rapid advancements in technology today have led to a growing reliance on automated tools over manual human labor. One of the widely used actuators across various fields is the DC Motor. This paper focuses on integrating tools with Linear Quadratic Regulator (LQR) and Linear Quadratic Tracker (LQT) approaches. LQR is an optimal control method applied to state-space-based systems. The LQR controller requires the definition of two parameters, namely the Q and R weighting matrices, which must be carefully determined to achieve optimal control actions as desired. Unlike the Proportional-Integral-Derivative (PID) controller, which features systematic tuning methods like Ziegler-Nichols and Cohen-Coon, the LQR controller lacks a dedicated systematic tuning methodology for determining the Q and R weighting matrices. The implementation of these approaches in this study aims to produce more efficient and effective outcomes, particularly in the context of community-based development programs. By optimizing the control systems used in community projects, this research contributes to enhancing the reliability and sustainability of technological solutions applied to improve societal well-being.