Journal of Marine Electrical and Electronic Technology

jomeet editor — 2025-08-15

editor jomeet

Optimization of Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT) Systems

Muhammad Izzul Haj — 2025-05-15

The increasing demand for high performance systems has triggered the emergence of optimal control problems which have become a hot topic recently. Related to the concept of how the condition of an optimal control system is the concept of optimizing a control system which shows a balance in the form of a meeting point between selecting a performance index and engineering with the aim of creating an optimal control system within the limits of physical constraints. In creating these conditions, it is necessary to have control system decision rules that can reduce the savings value from ideal behavior. In compiling this report, the author will explain the application of the LQR and LQT DC Motor Plant processes which are equipped with a datasheet. The use of MATLAB is also needed in the preparation of this report to include datasheets in the MATLAB script which will
then be simulated with MATLAB Simulink. The purpose of this process is to see how the step response is generated. Meanwhile, the type of DC motor used is type C24-L50, equipped with values for moment of inertia, damping ratio, inductance, resistance, and motor constants. in carrying out the experiment there is no series that is designed to reach the desired set point of the scope graph, because the application of a mathematical model from an existing data sheet does not use damping so that the graph cannot reach the set point.

Comparison of LQR and LQT Control of Uncertain Nonlinear Systems

Febri Setya Budi — 2025-05-14

Optimal controls have been applied in this time. One of simple optimal control which will be analyzed in this research is planar arm model dynamic. Linear quadratic regulator (LQR) and linear quadratic tracker (LQT) are two well-known control techniques that have been widely applied in various fields such as engineering, robotics, and economics. LQR is a control method that aims to minimize the cost function of a linear system by adjusting the control input. On the other hand, LQT is a control technique that aims to track the reference input of a linear system while minimizing the cost function. Both LQR and LQT are optimal control methods that have good stability and performance characteristics. In this paper, we present a comparison of LQR and LQT control
techniques and discuss their applications in various domains. We also highlight the advantages and limitations of these methods and suggest some possible future research directions.

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

arya adiansyah — 2025-05-14

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.

DC Motor A-max 108828 and Noise using LQR and LQT Methods

Rachma Prilian Eviningsih — 2025-05-15

Technology is continuously evolving in this era to meet various needs and facilitate human work, making tasks more efficient and automated. One of the significant developments in the field of electrical engineering is the Direct Current (DC) motor, which has become an essential component in many industrial applications. Due to its flexibility, controllability, and efficiency, DC motors remain a crucial subject of research and development in the industrial sector. Their ability to provide precise speed and torque control makes them ideal for applications ranging from robotics and automation to transportation and manufacturing. This paper focuses on the implementation of DC motors using two advanced control methods: Linear Quadratic Regulator (LQR) and Linear
Quadratic Tracking (LQT). The LQR method is widely used in control engineering due to its ability to optimize system stability and minimize control effort while maintaining desired performance. Meanwhile, the LQT method is an extension of LQR, designed to further enhance tracking accuracy by reducing errors that occur during the tracking process. By applying the LQT approach, the system is expected to achieve a more accurate response, minimizing deviations from the reference trajectory even in the presence of external disturbances and noise. In this study, a DC motor model A-max 108828 is used as the plant, representing the real-world system to be controlled. The motor system is first mathematically modeled to establish its dynamic behavior and then
transitioned into a simulation environment using Matlab Simulink software. The simulation evaluates the performance of the LQR and LQT controllers, comparing their ability to maintain system stability, reduce steady state errors, and respond to external disturbances effectively. Additionally, the effect of noise on system performance is analyzed to assess the robustness of each control strategy. The results obtained from the simulations demonstrate that both LQR and LQT methods significantly improve the performance of the DC motor system, with LQT exhibiting better tracking precision due to its enhanced error correction mechanism. The study also highlights the advantages of optimal control techniques in achieving efficient and stable motor operations, making them highly applicable for real-world industrial automation.

Image processing with the thresholding method using MATLAB R2014A

Muhammad Bilhaq Ashlah — 2025-05-15

Today, technology and control systems are developing so rapidly that electronic devices have become an integral part of our daily life. One of them uses a DC motor rotation as the driving force. Develop control systems for motors, especially DC motors. Direct current (DC) motors are a type of motor that is commonly used in industrial fields such as air compressors, elevators, etc. This is because direct current (DC) motors have several advantages, such as high starting torque and simple control methods. In utilizing this DC motor, optimization is also needed so that maximum performance can be obtained from the DC motor. In this practicum, SISO, MISO, SIMO and MIMO modeling is carried out on a 12 V DC motor with a diameter of 42mm with a LQR (Linear Quadratic
Regulator) control system which will be simulated in Simulink MATLAB Simulation, and also in this modeling there are 2 conditions, namely without noise and in the presence of noise then the two things are compared. Noise is interference in sound signals (electrical and electronic) that limit the range of a system and are unwanted in electronic circuits. Noise occurs during signal transmission from sender to receiver.

Analysis of Circuit with LQR and LQT Control Systems on DC Motor Type C34-L70 with Noise

avada rifqi editor — 2025-05-14

The rapid advancement of technology to meet various human needs has significantly facilitated various aspects of daily life, including industrial processes. One notable technological innovation in this regard is the DC motor, which serves as a critical actuator in numerous industrial applications. DC motors are valued for their simplicity, precision, and controllability, making them essential in automation, robotics, and other mechanical systems. However, despite their advantages, a common challenge encountered with DC motors is the inability of the output to consistently reach the desired target or setpoint. This issue is often attributed to various factors such as system instability, external disturbances, and the limitations of the control methods used. To mitigate these challenges, system optimization strategies are implemented to improve the performance of DC motors and ensure they can achieve precise and reliable outputs. System optimization is vital in overcoming the inherent limitations of DC
motors, particularly when external factors, such as noise, affect the system's performance. Noise, or external disturbances, can significantly impact the output of a DC motor, causing fluctuations and reducing its efficiency. To address these issues, various control techniques, such as Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT) controllers, have been explored in this paper. These methods are designed to stabilize the motor's performance by minimizing errors between the actual output and the desired setpoint. LQR and LQT are advanced control strategies that help optimize the system by adjusting control inputs to achieve better performance, particularly in environments with high levels of disturbance or noise. In this study, noise is
introduced into the DC motor system using different circuits, and its effects on the output are carefully analyzed. The noise is applied to each output through various configurations, and the resulting disturbances are evaluated to understand their impact on the motor's performance. The findings from these experiments are crucial in identifying the specific factors that influence the motor's behavior under noisy conditions. The goal is to minimize the interference caused by noise, thereby preserving the motor's ability to maintain high-quality performance. The application of LQR and LQT controllers is tested to see how effectively they can suppress the negative effects of noise and enhance the motor's response to disturbances, ensuring it can still reach the target output with minimal deviation. The investigation into the impact of noise on DC motors is essential for improving the reliability and efficiency of these systems, especially in industrial environments where noise and disturbances are common. By
implementing advanced control methods such as LQR and LQT, it is possible to optimize the motor's performance and achieve greater precision in its operation. The results of this study will contribute to the development of more robust control systems that can mitigate the impact of external disturbances, ensuring that DC motors continue to perform efficiently and accurately in real-world applications. Ultimately, this research provides valuable insights into the importance of noise management and the role of control strategies in enhancing the performance and reliability of DC motors.

Modeling of LQR and LQT Control on DC Motor C34-L-60

Abimanyu Manap — 2025-05-11

Control systems play a crucial role in the industrial world, ensuring optimal performance of a plant or process. A well-designed control system is characterized by its ability to produce a responsive and accurate output that closely aligns with the desired setpoint. This responsiveness is essential for maintaining efficiency and precision in industrial operations. In this research, the control methodology begins with the modeling of the system using MATLAB software. The study focuses on two advanced control techniques: Linear Quadratic Regulator (LQR) and Linear Quadratic Tracker (LQT). These techniques are applied to a DC Motor model, specifically the C34-L-60 motor, which serves as the primary plant in this investigation. The system modeling process involves creating a detailed representation of the motor dynamics and integrating the control algorithms to simulate its performance. By leveraging MATLAB's powerful simulation tools, the study aims to derive optimal control parameters for the motor under various operating conditions. This step is critical to achieving a comprehensive understanding of how each control method influences the system's behavior. Once the models are established, simulations are conducted to evaluate the performance of both the LQR and LQT control strategies. The simulations allow for a detailed comparison of their respective responses to the desired setpoints. Key performance metrics such as settling time, overshoot, and steady-state error are analyzed to determine the effectiveness of each control method. The data obtained from these simulations highlights the differences between the LQR and LQT methods. LQR focuses on minimizing the quadratic cost function for state variables and control inputs, while LQT extends this approach by considering tracking performance for specific reference trajectories. These distinctions provide valuable insights into the applicability of each technique for different industrial scenarios. Overall, this research emphasizes the importance of selecting an appropriate control method to achieve optimal system performance. By comparing the simulation results of LQR and LQT, the study contributes to a deeper understanding of their capabilities and limitations in controlling DC motors. This knowledge serves as a foundation for further advancements in industrial control system design.

Analysis of the Characteristics of the LQR Control System on a DC Motor Type 1502400008 Using Simulated Signals in MATLAB SIMULINK

Anggara Trisna Nugraha — 2025-05-15

This study aims to analyze the characteristics of the control system Linear Quadratic Regulator LQR and Linear Quadratic Tracking LQT on DC motor type 1502400008 using simulation on MATLAB SIMULINK, in this study will use the Multi Antenna Types signal system namely SISO, SIMO, MISO and MIMO which will implemented on a DC motor, before collecting data the first thing to do is look for a DC motor Data Sheet and with these data we can make a mathematical model of a DC motor in the form of a Transfer Function, after creating a Transfer Function model continue to make a simulation modeling in MATLAB SIMULINK, the data taken is comparative data for each control system and a comparison of the control systems that have been paired with the LQT and
LQR systems, after collecting all the data an analysis and comparison of the results of the SISO, SIMO, MISO and MIMO signal control systems will be carried out. The expected results of this study are an understanding of the differences between the control systems of the Linear Quadratic Regulator LQR and Linear Quadratic Tracking LQT and being able to analyze the characteristics generated using the Multi Antenna Types signal system.