Optimazion of DC Motor 054B-2 By Method LQR and LQT in MATLAB SIMULINK

Abstract

System identification is a method of modeling a dynamic system using input and output signals from the system to be modeled (Rohman & Izaty, 2021). Basically a mathematical model can be built through a data observation process and there are two methods that can be used to obtain a mathematical model of a dynamic system, where in this practicum the system to be controlled is a DC motor. The speed of DC motors often decreases due to the existing load, so that the speed becomes not constant so that a controller design or optimization of the system is needed. One of the software tools that can be used in learning system optimization is Matrix Laboratory (MATLAB). In the MATLAB (Matrix Laboratory) software there is a tool called Simulink. At Simulink, researchers can assemble circuits from SISO, SIMO, MISO, and MIMO after which researchers can also see the results in the form of graphs. This practicum aims to prove between calculation theory and datasheets from DC motors with
simulation results from MATLAB Simulink. LQR is the optimal input which results in the form of initial state feedback, thus, it can be represented as constant feedback gain at the state of the LQR system. LQT(Linear Quadratic Tracking ) is used as another method besides LQR. LQT itself is a linear control system whose output follows (tracking) the path that has been implemented through the input.