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Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 708 Documents
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Combining Passivity-Based Control and Linear Quadratic Regulator to Control a Rotary Inverted Pendulum Minh-Tai Vo; Van-Dong-Hai Nguyen; Hoai-Nghia Duong; Vinh-Hao Nguyen
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18498

Abstract

In this manuscript, new combination methodology is proposed, which named combining Passivity-Based Control and Linear Quadratic Regulator (for short, CPBC-LQR), to support the stabilization process as the system is far from equilibrium point. More precisely, Linear Quadratic Regulator (for short, LQR) is used together with Passivity-Based Control (for short, PBC) controller. Though passivity-based control and linear quadratic regulator are two control methods, it is possible to integrate them together. The combination of passivity-based control and linear quadratic regulator is analyzed, designed and implemented on so-called rotary inverted pendulum system (for short, RIP). In this work, CPBC-LQR is validated and discussed on both MATLAB/Simulink environment and real-time experimental setup. The numerical simulation and experimental results reveal the ability of CPBC-LQR control scheme in stabilization problem and achieve a good and stable performance. Effectiveness and feasibility of proposed controller are confirmed via comparative simulation and experiments.
Advanced Flowrate Control of Petroleum Products in Transportation: An Optimized Modified Model Reference PID Approach Yaseen, Yaseen Kh.; Mhmood, Ali H.; Subhi, Mohammed R.; Rakan, Amer B.; Mohammed, Hameed A.
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19177

Abstract

Efficient flowrate control is paramount for the seamless operation and reliability of petroleum transportation systems, where precise control of fluid movement ensures not only operational efficiency but also safety and cost-effectiveness. The main aim of this paper is to develop a highly effective modified model reference PID controller, tailored to ensure optimal flowrate control of petroleum products throughout their transportation. Initially, the petrol transportation process is analyzed to establish a suitable mathematical model based on vital factors like pipeline diameter, length, and pump attributes. However, using a basic first-order time delay model for petrol transportation systems is limiting due to inaccuracies, variable delay issues, safety oversights, and real-time control complexities. To improve this, the delay portion is approximated as a third-order transfer function to better reflect complex physical conditions. Subsequently, the PID controller is synthesized by modifying its structure to address flowrate control issues. These modifications primarily focus on the controller’s derivative component, involving the addition of a first-order filter and alterations to its structure. To optimize the proposed controller, the genetic, black hole, and zebra optimization techniques are employed, aiming to minimize an integral time absolute error cost function and ensure that the outlet flow of the controlled system closely follows the response of an appropriate reference model. They are chosen for their proficiency in complex optimization to enhance the controller's effectiveness by optimizing parameters within constraints, adapting to system dynamics, and ensuring optimal conditions. Through simulations, it is demonstrated that the proposed controller significantly enhances the stability and efficiency of the control system, while maintaining practical control signals. Moreover, the proposed modifications and intelligent tuning of the PID controller yield remarkable improvements compared to previous related work, resulting in a 36% reduction in rise time, a 63% reduction in settling time, an 80% reduction in overshoot, and a 98% reduction in cost value.
Smart Robotic Exoskeleton: Constructing Using 3D Printer Technique for Ankle-Foot Rehabilitation Salih, Rafal Khalid; Aboud, Wajdi Sadik
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18429

Abstract

Patients with spinal cord injury (SCI), stroke, and coronavirus patients must undergo a rehabilitation process involving programmed exercises to regain their ability to perform activities of daily living (ADL). This study focuses on the rehabilitation of the foot-ankle joint to restore ADL through the design and implementation of a rehabilitation exoskeleton with three degrees of freedom (abduction/adduction, inversion/eversion, and plantarflexion/dorsiflexion movements). increase the patients cause worker fatigue, emotional exhaustion, a lack of motivation, and feelings of frustration, all contributing to a decrease in work efficacy and productivity. The robotic exoskeleton was developed to overcome this limitation and support the medical rehabilitation section.   The main goal of this study is to develop a portable exoskeleton that is comfortable, lightweight, and has a range of motion (ROM) compatible with human anatomy to ensure that movements outside of this range are minimized, the anthropometric parameters of a typical human lower foot have been considered. In addition, it's a home-based rehabilitation device which means the exoskeleton can be used in any environment due to its lightweight and small size to accelerate the rehabilitation process and increase patient comfort.  The proposed autonomous exoskeleton structure is designed in Solid Works and constructed with polylactic acid (PLA) plastic, the reason PLA was chosen is its lightweight, available, stiff material, and low cost, using 3D printing technology the exoskeleton was manufacturing. Electromyography (EMG) and angle data were extracted using EMG MyoWare and gyroscope sensors, respectively, to control the exoskeleton. It was evaluated on its own then with 2 normal subjects and 17 patients with stroke, spinal cord injury (SCI), and coronavirus. The limitation that has been faced was that the sessions were limited due to the limited time provided for the study. According to the improvement rate, the exoskeleton has a significant impact on regaining muscle activity and improving the range of motion of foot-ankle joints for the three types of patients. The rate of improvement was 300%, 94%, and 133.3% for coronavirus, SCI, and stoke respectively. These results demonstrate that this exoskeleton can be utilized for physiotherapy exercises.
Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model Hidayat, Hidayat; Buono, Agus; Priandana, Karlisa; Wahjuni, Sri
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.18777

Abstract

Path planning is an essential algorithm in autonomous mobile robots, including agricultural robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm is one of the reinforcement learning methods used for path planning. However, for multi-robot system, this algorithm tends to produce the same path for each robot. This research modifies the Q-Learning algorithm in order to produce path variations by utilizing the motivation model, i.e. achievement motivation, in which different motivation parameters will result in different optimum paths. The Motivated Q-Learning (MQL) algorithm proposed in this study was simulated in an area with three scenarios, i.e. without obstacles, uniform obstacles, and random obstacles. The results showed that, in the determined scenario, the MQL can produce 2 to 4 variations of optimum path without any potential of collisions (Jaccard similarity = 0%), in contrast to the Q-Learning algorithm that can only produce one optimum path variation. This result indicates that MQL can solve multi-robots path planning problems, especially when the number of robots is large, by reducing the possibility of collisions as well as decreasing the problem of queues. However, the average computational time of the MQL is slightly longer than that of the Q-Learning.
Exploring ResNet-18 Estimation Design through Multiple Implementation Iterations and Techniques in Legacy Databases Nuntachai Thongpance; Pareena Dangyai; Kittipan Roongprasert; Anantasak Wongkamhang; Ratchanee Saosuwan; Rawiphon Chotikunnan; Pariwat Imura; Anuchit Nirapai; Phichitphon Chotikunnan; Manas Sangworasil; Anuchart Srisiriwat
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19589

Abstract

In a rapidly evolving landscape where automated systems and database applications are increasingly crucial, there is a pressing need for precise and efficient object recognition methods. This study contributes to this burgeoning field by examining the ResNet-18 architecture, a proven deep learning model, in the context of fruit image classification. The research employs an elaborate experimental setup featuring a diverse fruit dataset that includes Rambutan, Mango, Santol, Mangosteen, and Guava. The efficacy of single versus multiple ResNet-18 models is compared, shedding light on their relative classification accuracy. A unique aspect of this study is the establishment of a 90% decision threshold, introduced to mitigate the risk of incorrect classification. Our statistical analysis reveals a significant performance advantage of multiple ResNet-18 models over single models, with an average improvement margin of 15%. This finding substantiates the study’s central hypothesis. The implemented 90% decision threshold is determined to play a pivotal role in augmenting the system’s overall accuracy by minimizing false positives. However, it’s worth noting that the increased computational complexity associated with deploying multiple models necessitates further scrutiny. In sum, this study provides a nuanced evaluation of single and multiple ResNet-18 models in the realm of fruit image classification, emphasizing their utility in practical, real-world applications. The research opens avenues for future exploration by refining these methodologies and investigating their applicability to broader object recognition tasks.
Intelligent Hardware-Software Processing of High-Frequency Scanning Data Mukanova, Zhanna; Atanov, Sabyrzhan; Jamshidi, Mohammad
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.18915

Abstract

The constant emission of polluting gases is causing an urgent need for timely detection of harmful gas mixtures in the atmosphere. A method and algorithm of the determining spectral composition of gas with a gas analyzer using an artificial neural network (ANN) were suggested in the article. A small closed gas dynamic system was designed and used as an experimental bench for collecting and quantifying gas concentrations for testing the proposed method. This device was based on AS7265x and BMP180 sensors connected in parallel to a 3.3 V compatible Arduino Uno board via QWIIC. Experimental tests were conducted with air from the laboratory room, carbon dioxide (CO2), and a mixture of pure oxygen (O2) with nitrogen (N2) in a 9:1 ratio. Three ANNs with one input, one hidden and one output layer were built. The ANN had 5, 10, and 20 hidden neurons, respectively. The dataset was divided into three parts: 70% for training, 15% for validation, and 15% for testing. The mean square error (MSE) error and regression were analyzed during training. Training, testing, and validation error analysis were performed to find the optimal iteration, and the MSE versus training iteration was plotted. The best indicators of training and construction were shown by the ANN with 5 (five) hidden layers, and 16 iterations are enough to train, test and verify this neural network. To test the obtained neural network, the program code was written in the MATLAB. The proposed scheme of the gas analyzer is operable and has a high accuracy of gas detection with a given error of 3%. The results of the study can be used in the development of an industrial gas analyzer for the detection of harmful gas mixtures.
Humanoid Walking Control Using LQR and ANFIS Auzan, Muhammad; Lelono, Danang; Dharmawan, Andi
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.16444

Abstract

Humanoid robots possess remarkable mobility and adaptability for diverse environments. Nonetheless, accurate walking pattern tracking remains challenging, especially when employing the linear quadratic regulator (LQR) due to delays in high-mobility setpoint tracking. We propose a novel control approach to address this limitation by integrating an artificial neuro-fuzzy inference system (ANFIS) with the LQR to enhance pattern tracking. The research contributes to developing a control system that combines LQR and ANFIS to enable humanoid robots to follow various walking patterns with increased precision and efficiency and also the scheme to incorporate LQR and ANFIS. The study involves four experiments: step response, walking phase, static straight walking, and varied straight walking. Each test runs for 5 seconds with a 100-millisecond sampling rate, repeated five times, and employs the Integral Absolute Value (IAE) metric for evaluation. The LQR-ANFIS method exhibits superior performance, achieving a maximum overshoot of 0%, a rise time of 0.3 seconds, a settling time of 0.3 seconds, and a steady-state error of 0% in the step response experiment. The proposed control system also enables stable walking with step periods ranging from 0.15 to 4 seconds and step ranges of 0.05 to 0.03 meters. In conclusion, the integration of ANFIS with the LQR significantly enhances the mobility of humanoid robots, enabling them to navigate diverse environments and accurately track various walking patterns proficiently.
Design and Implementation of an Intelligent Safety and Security System for Vehicles Based on GSM Communication and IoT Network for Real-Time Tracking Marhoon, Hamzah M.; Alanssari, Ali Ihsan; Basil, Noorulden
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19652

Abstract

In recent years, the surge in car theft cases, often linked to illicit activities, has become a growing concern. Simultaneously, countries grappling with oil shortages have shifted towards converting vehicles to run on liquid propane gas, presenting new safety challenges for car owners. This paper introduces a novel integrated intelligent system designed to address the challenges of car theft and safety concerns associated with gas-based vehicles. By seamlessly integrating these concerns into a single system, it aims to achieve significantly improved performance compared to traditional alarm systems. The proposed system consists of three primary parts: the car security subsystem, an Internet of Things (IoT)-based real-time car tracking subsystem, and the car safety subsystem. Utilizing key technologies such as the Arduino Microcontroller, Bluetooth module, vibration sensor, keypad, solenoid lock, GSM module, NodeMCU microcontroller, GPS module, MQ-4 gas sensor, flame sensor, temperature sensor, and Bluetooth module, the system aims to provide a comprehensive solution for the mentioned issues. Furthermore, the vibration sensor plays a crucial role in identifying unauthorized vehicle operations. Its significance lies in detecting the vibrations emanating from the running engine. Concurrently, other modules and sensors are utilized for real-time tracking and enhancing vehicle safety. These measures include safeguarding against incidents like fire outbreaks or gas leaks within the gas container. Finally, after assembling the system, a practical test was conducted, yielding favourable performance results. This paper describes a meaningful step towards improving the protection and safety for the cars, simultaneously addressing the stealing prevention and gas-related accident alleviation.
Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization Nguyen Xuan Chiem; Le Tran Thang
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19661

Abstract

In this study, a controller design method based on the LQR method and BAT algorithm is presented for the Furuta pendulum stabilization system. Determine the LQR controller, it is often based on the designer's experience or using trial and error to find the Q, R matrices. The BAT search algorithm is based on the characteristics of the bat population in the wild. However, there are advantages to finding multivariate objective functions. The BAT algorithm has an improvement for the LQR controller to optimize the linear square function with fast response time, low energy consumption, overshoot, and a small number of oscillations. Swarm optimization algorithms have advantages in finding global extrema of multivariate functions. Therefore, with a large number of elements of the Q and R matrices, they can also be quickly found and these matrices still satisfy the Riccati equation. The controller with optimal parameters is verified through simulation results with different scenarios. The performance of the proposed controller is compared with a conventional LQR controller and implemented on a real system.
A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height Tran, Duc Thien; Hoang, Nguyen Minh; Loc, Nguyen Huu; Truong, Quoc Thanh; Nha, Nguyen Thanh
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19448

Abstract

This paper proposes a fuzzy LQR PID control for a two-legged wheeled balancing robot for keeping stability against uncertainties and variant heights. The proposed control includes the fuzzy supervisor, LQR, PID, and two calibrations. The fuzzy LQR is conducted to control the stability and motion of the robot while its posture changes with respect to time. The fuzzy supervisor is used to adjust the LQR control according to the robotic height. It consists of one input and one output. The input and output have three membership functions, respectively, to three postures of the robot. The PID control is used to control the posture of the robot. The first calibration is used to compensate for the bias value of the tilting angle when the robot changes its posture. The second calibration is applied to compute the robotic height according to the hip angle. In order to verify the effectiveness of the proposed control, a practical robot with the variant height is constructed, and the proposed control is embedded in the control board. Finally, two experiments are also conducted to verify the balancing and moving ability of the robot with the variant posture.

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