Article Per Year (5 Year)
p-Index From 2021 - 2026
2.768
P-Index
This Author published in this journals
All Journal JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) SAGA: Journal of Technology and Information Systems Mein: Journal of Mechanical, Electrical & Industrial Technology Sainstech Nusantara Maritime in Community Service and Empowerment
Rama Arya Sobhita
Unknown Affiliation
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Physics Transportation

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search
Journal : Sainstech Nusantara

 1 
Analysis and Implementation of LQR and LQT Control Strategies for the Maxon RE36 DC Motor Using MATLAB Simulink Environment Nugraha, Anggara Trisna; Mukhammad Jamaludin; Rama Arya Sobhita; Dimas Eka Saputra
SAINSTECH NUSANTARA Vol. 2 No. 2 (2025): May 2025
Publisher : Nusantara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71225/jstn.v2i2.97

Abstract

The field of automatic control plays a crucial role in advancing science and technology. Among various actuators, DC motors are widely used but are often prone to overshoot due to their high initial torque and generally unstable performance characteristics. This study aims to determine the most effective control approach for optimizing the performance of the Maxon RE36 DC motor. Two control strategies are evaluated: the Linear Quadratic Regulator (LQR) and the Linear Quadratic Tracking (LQT) method. In a first-order system analysis, the motor's output significantly deviated from the target setpoint of 1, exhibiting an overshoot of approximately 0.505%. The application of the LQR method in output response modeling effectively reached the setpoint without any occurrence of overshoot or undershoot. Conversely, the LQT method achieved the setpoint but introduced a 5.851% undershoot and a 0.7% overshoot, although it demonstrated a rapid response time, achieving steady-state within approximately ±0.5 seconds.Experimental results on the Maxon RE36 DC motor revealed that while the LQT method offered faster settling times, the LQR method produced a cleaner response with no overshoot or ripple, making it more suitable for precision optimization of the motor's dynamic performance.
System Optimization Using LQR and LQT Methods on 42D29Y401 DC Motor Nugraha, Anggara Trisna; Akhmad Azhar Firdaus; Rama Arya Sobhita; Zaki Wicaksono
SAINSTECH NUSANTARA Vol. 2 No. 2 (2025): May 2025
Publisher : Nusantara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71225/jstn.v2i2.108

Abstract

Control systems are critical for managing, commanding, and regulating the behavior of dynamic systems. A DC motor converts direct current electrical energy into kinetic energy, requiring positive and negative voltage terminals for operation. Due to their ease of control across wide speed ranges, DC motors are widely applied in various industrial sectors. Speed regulation is typically achieved using control devices tailored to specific system requirements. To optimize DC motor performance, this study employs mathematical modeling and control strategies using MATLAB software. The 42D29Y401 DC motor is modeled and simulated using the Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT) methods. Simulation results show that the first-order DC motor achieved a stable step response with an amplitude of 3.40, a rise time of 3.11 seconds, and minor overshoot and undershoot values of 0.501% and 1.98%, respectively. The LQR-optimized system improved performance with an amplitude close to 1, a faster rise time of 1.1 seconds, and reduced overshoot and undershoot at 0.505%. Comparatively, the LQR-based system demonstrated better overall performance than the unoptimized model, while the LQT-based system yielded the highest level of performance among all configurations.
Optimization Control in MG-16 DC Motor Using LQR and LQT Configurations Nugraha, Anggara Trisna; Muhammad Bilhaq Ashlah; Rama Arya Sobhita; Dhadys Ayu Juli Anjhani
SAINSTECH NUSANTARA Vol. 2 No. 3 (2025): August 2025
Publisher : Nusantara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71225/jstn.v2i3.105

Abstract

DC motors are widely used electronic components commonly found in everyday applications. Typically, when a load is applied, a DC motor tends to decelerate and fails to maintain a constant speed. To address this, motor speed can be controlled by adjusting the input voltage. However, to maintain consistent speed under varying loads, a control system is necessary. LQR works by adjusting the motor response to closely approach the desired setpoint, while minimizing both overshoot and undershoot within the system. On the other hand, LQT is a linear control strategy designed to ensure that the system output closely follows a time-varying reference or setpoint. When implemented, LQR yields a motor response that aligns with the target setpoint without any overshoot or undershoot. In contrast, if LQR is not applied, the motor response deviates significantly from the desired target and takes a longer time to settle. Meanwhile, the LQT method produces a quicker response reaching steady state in approximately ±0.5 seconds although it does introduce some overshoot and slight ripple in the signal. Despite these minor drawbacks, LQT is often favored over LQR for applications involving the MG-16B DC motor due to its superior speed in reaching the setpoint.
Simulation Analysis of System Optimization Using an EC-Max 40 Type DC Motor Plant Muhammad Izzul Haj; Nugraha, Anggara Trisna; Rama Arya Sobhita; Rony Dwi Kristiawan
SAINSTECH NUSANTARA Vol. 2 No. 3 (2025): August 2025
Publisher : Nusantara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71225/jstn.v2i3.107

Abstract

A control system functions to regulate one or more variables, ensuring they remain at specific values or within desired limits. The primary aim is to achieve optimal system performance through effective control strategies. In this study, system optimization is explored within a closed-loop configuration using a DC motor as the plant. The motor selected for this analysis is the EC-Max 40, a direct current motor that converts electrical energy into mechanical motion. Utilizing the motor's datasheet, a first-order mathematical model is developed and implemented in Matlab Simulink for simulation purposes. The system design incorporates both Linear Quadratic Regulator (LQR) and Linear Quadratic Tracker (LQT) methods to evaluate and compare their performance. The analysis focuses on the step response of the system observing how the output behaves in response to input variations both under ideal conditions and in the presence of noise. The simulations reveal that both LQR and LQT methods produce similarly effective results; however, the LQT approach demonstrates a faster convergence to stability compared to the LQR method.
Comparison of System Optimization Methods: LQR vs. LQT on the Output Response of IG-42CRGM DC Motor Muhammad Bilhaq Ashlah; Nugraha, Anggara Trisna; Rama Arya Sobhita; Geniari Nastiti
SAINSTECH NUSANTARA Vol. 2 No. 4 (2025): November 2025
Publisher : Nusantara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71225/jstn.v2i4.99

Abstract

DC motors are widely utilized in industrial applications for their reliability and efficiency. To optimize their performance, it is crucial to employ control systems supported by mathematical modeling to predict motor responses under varying conditions. This study investigates the first- and second-order models of DC motors and examines the impact of internal disturbances (noise) on system performance. The output responses of two DC motors, the 42BLFX02 and Maxon EC-I 40 (70W), are compared under both undisturbed and noisy conditions using simulations. The results reveal that the second-order model offers a more stable response and better aligns with the desired target compared to the first-order model. Furthermore, the application of the Linear Quadratic Regulator (LQR) control method significantly enhances the speed and accuracy of reaching the motor set point. However, when noise is introduced, the LQR method fails to maintain stability, and the motor's output starts to mirror the disturbance pattern. These findings highlight that while LQR is effective under ideal conditions, its performance diminishes when exposed to disturbances. Therefore, additional strategies are necessary to ensure stability and optimal performance in real-world conditions, particularly in environments with significant noise or disturbances.  
Enhancing LQR and LQT Control Strategies for the Output Performance of PG36M555 DC Motors Akhmad Azhar Firdaus; Nugraha, Anggara Trisna; Rama Arya Sobhita; Dhadys Ayu Juli Anjhani
SAINSTECH NUSANTARA Vol. 2 No. 4 (2025): November 2025
Publisher : Nusantara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71225/jstn.v2i4.101

Abstract

A DC motor is commonly utilized as an actuator due to its ability to produce high torque. Controlling the motor's speed is one of the primary methods to manage its performance. Among various wireless communication options, radio waves are preferred since they do not require a clear line of sight between the transmitter and receiver. Employing multiple antennas offers benefits such as enhanced reliability and increased data transmission rates. This study focuses on designing and simulating four types of systems: SISO, SIMO, MISO, and MIMO. The performance of these configurations is evaluated and compared using Signal-to-Noise Ratio (SNR) and channel capacity, with variations in antenna count. Simulations were carried out in MATLAB to analyze how different antenna quantities (4, 8, and 16) affect channel capacity across an SNR range of 0 to 30 dB. The simulation outcomes reveal a substantial rise in system capacity, reaching up to 214 bits/Hz/sec when a 16x16 MIMO setup is applied at 30 dB SNR.
Co-Authors Ahmad Raafi Fauzi Akhmad Azhar Firdaus Ananda Ismul Azam Anggara Trisna Nugraha Anisa Fitri Santosa Ary Pratama Paluga Asri, Purwidi Dhadys Ayu Juli Anjhani Dimas Bayu Dwi Saputra Dimas Eka Saputra Epyk Sunarno Fahmi Yahya Saputra Geniari Nastiti Hendro Agus Widodo Isa Rachman Ivan Azwar Septiadi Joesianto Eko Poetro Mohammad Abu Jamiin Mohammad Basuki Rahmat Muhamad Rifqi Anugrah Syafa’at Muhammad Bilhaq Ashlah Muhammad Fikri Fathurrohman Muhammad Iham Fatqurrochman Muhammad Izzul Haj Muhammad Naufal Maulana Mukhammad Jamaludin Nugraha S.T M.T, Anggara Trisna Rachma Prilian Eviningsih Raimundus Risa Arnestanta Rony Dwi Kristiawan Sindy Yurisma Sheila Urip Mudjiono Yulian Fatkur Rohman Zaki Wicaksono