The system model is crucial for the design of control systems, as it serves as a reference for developing control strategies. An accurate system model is necessary to ensure effective performance. This experiment focuses on designing an online system identification technique based on MATLAB to obtain models for Single Input, Single Output (SISO), Multi Input, Single Output (MISO), and Multi Input, Multi Output (MIMO) systems. The system models were simulated using the 054B-2 DC Motor model for both Order 1 and Order 2 configurations. In control systems, noise is often present, acting as a disturbance that affects the system's output. Noise can cause undesirable effects such as high overshoot, impacting system performance. Therefore, noise was introduced into the SISO, SIMO, MISO, and MIMO systems to observe its impact. The online system identification process was carried out using Simulink, a MATLAB-based tool for modeling and simulation. To optimize control performance, the experiment utilized the Linear Quadratic Regulator (LQR), a widely used optimal control method in fields like industry, robotics, and engineering. The advantage of LQR lies in its ability to provide optimal solutions for system control problems by using the state-space approach. Additionally, the Linear Quadratic Tracking (LQT) method was implemented as an alternative to LQR. LQT is a linear control system that tracks the path specified by the input, ensuring that the output follows a desired trajectory. The results of the experiment demonstrate the effectiveness of these control strategies in handling various system models with noise disturbances.