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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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A Performance Evaluation of Repetitive and Iterative Learning Algorithms for Periodic Tracking Control of Functional Electrical Stimulation System Kurniawan, Edi; Pratiwi, Enggar B.; Adinanta, Hendra; Suryadi, Suryadi; Prakosa, Jalu A.; Purwowibowo, Purwowibowo; Wijonarko, Sensus; Maftukhah, Tatik; Rustandi, Dadang; Mahmudi, Mahmudi
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20705

Abstract

Functional electrical stimulation (FES) is a medical device that delivers electrical pulses to the muscle, allowing patients with spinal cord injuries to perform activities such as walking, cycling, and grasping. It is critical for the FES to generate stimuli with the appropriate controller so that the desired movements can be precisely tracked. By considering the repetitive nature of the movements, the learning-based control algorithms are utilized for regulating the FES. Iterative learning control (ILC) and repetitive control (RC) are two learning algorithms that can be used to accomplish accurate repetitive motions. This study investigates a variety of ILC and RC designs with distinct learning functions; this constitutes our contribution to the field. The FES model of ankle angle, which is in a class of discrete-time linear systems is considered in this study. Two learning functions, i.e., proportional, and zero-phase learning functions, are simulated for the second-order FES model running at a sampling time of 0.1 s. The results indicate the superior performance of the ILC and RC in terms of convergence rate using the zero-phase learning function. ILC and RC with a zero-phase learning function can reach a zero root-mean-square error in two iterations if the model of the plant is correct. This is faster than proportional-based ILC and RC, which takes about 40 iterations. This indicates that the zero-phase learning function requires two iterations to ensure that the patient's ankle angle precisely tracks the intended trajectory. However, the tracking performance is degraded for both control methods, especially when the model is subject to uncertainties. This specific problem can lead to future research directions.
Design and Analysis of IO and FO Controllers to Investigate the Effects of Process Parameter Perturbations on Lag-Dominant Time Delay Systems Patil, Diptee; Jadhav, Sharad
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21101

Abstract

This paper focuses on the design, analysis and implementation of Integer-order (IO) and Fractional-order (FO) controllers for systems characterized by lag-dominant time delays. The existing literature has been examined to analyze the methodology employed in tuning IO and FO controllers for first-order time delay system for perturbations in process parameters. It is observed that there is scope to investigate better controllers for lag-dominant time delay systems. The five different structures of controllers are chosen. The paper proposes IO and FO controllers tailored for a test group comprising 16 first-order systems with time delays. These IO and FO controllers are designed to fulfil design specifications: phase margin, peak overshoot, IAE, ITAE and ISE using Modified Bode’s Ideal Loop Transfer Function with delay method. For comparison conventional IO tuning method, Gain-Phase Margin Tester (GPMT) and Fractional Ms Constrained Integral Gain Optimization Method (F-MIGO) is used. The simulation results and performance evaluation for both IO and FO controllers are obtained for a range of values of relative dead time of the system represented by τ. The τ value is obtained by varying conditions of delay (L) and time constant (T). Two scenarios are taken into account: the first involves varying L while keeping T constant, and the second involves keeping L constant while varying T. The main objective of the paper is to analyze IO and FO controllers based on time and frequency domain parameters, performance error indices, disturbance rejection, gain variations, Total Variation (TV) and control efforts for perturbations in process parameters. The simulation results indicate that FO controllers show superior tolerance to perturbations in L and T when compared to IO counterparts. This observation was noted during the analysis of the control system by varying values of L and T to obtain a consistent value of τ . Thus, the extensive simulation studies demonstrate that the FO controller tailored for lag-dominant time delay systems outperforms its IO counterpart in terms of robustness, closed-loop stability and error performance metrics.
Model Predictive Control Design under Stochastic Parametric Uncertainties Based on Polynomial Chaos Expansions for F-16 Aircraft Purnawan, Heri; Asfihani, Tahiyatul; Kim, Seungkeun; Subchan, Subchan
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21366

Abstract

Parametric uncertainty in a dynamical system has the potential to undermine the performance of a closed-loop controller designed through classical techniques. This paper presents a novel approach to stochastic model predictive control (SMPC) by employing the polynomial chaos expansion (PCE) method called PCE-based model predictive control (PCE-MPC). This method offers a more robust and efficient solution to tackle parameter uncertainties in dynamic systems. The PCE method is utilized to propagate uncertainties through orthogonal polynomials, and the Galerkin projection approach is employed to compute PCE coefficients via intrusive spectral projection (ISP). In Galerkin projection, the inner product involves an integration term, and the integration values are approximated using the Gauss-Legendre quadrature. This quadrature method precisely integrates the p-th order polynomial using 2p-1 points. The numerical case study focuses on the short-period mode of the F-16 aircraft model. Simulation results demonstrate the robust performance of the proposed method in the presence of parameter uncertainties, with system states converging to the original points for each parameter realization under various initial conditions. Comparison results indicate negligible differences between MPC and PCE-MPC, showcasing nearly identical performance. However, further investigation is warranted in other cases and more complex systems involving parameter uncertainties.
Securing Communication in Internet of Vehicles using Collaborative Cryptography and Intelligent Reflecting Surfaces Ahmed, Aljumaili; Trabelsi, Hafedh; Jerbi, Wassim; Hazim, Rafal
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21813

Abstract

The Internet of Vehicles (IoV) is revolutionizing transportation systems by enabling seamless communication and collaboration among vehicles, roadside units (RSUs), and cloud servers. However, the dynamic and diverse nature of IoV environments raises significant concerns regarding security vulnerabilities and operational efficiency. In response to these challenges, this study proposes an innovative approach that integrates collaborative cryptographic techniques with intelligent reflecting surfaces (IRS). Our approach leverages advanced encryption methods, such as the Advanced Encryption Standard (AES), to ensure secure data transmission, while intelligent reflecting surfaces dynamically adjust their reflective properties to enhance signal propagation and reception. We present a comprehensive network model and algorithmic framework for implementing our proposed strategy, with a specific emphasis on cryptographic protocols and the role of intelligent reflecting surfaces in enhancing both communication security and efficiency. Through theoretical analysis and discussion, we highlight the potential advantages of integrating intelligent reflecting surfaces into secure physical layer (PL), IoV networks, including expanded network coverage, reduced communication overhead, and enhanced energy efficiency. Moreover, we address security threats and vulnerabilities in IoV environments, including potential attacks such as eavesdropping, data tampering, and denial of service. We discuss strategies for mitigating these security risks through the combined use of cryptographic techniques and intelligent reflecting surfaces, thereby bolstering the resilience and robustness of IoV systems.
An Application of Modified T2FHC Algorithm in Two-Link Robot Controller Ngo, Thanh Quyen; Tran, Thanh Hai; Le, Tong Tan Hoa; Lam, Binh Minh
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.v4i4.18943

Abstract

Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems.
Advanced Threat Detection Using Soft and Hard Voting Techniques in Ensemble Learning Jabbar, Hanan Ghali
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22005

Abstract

This study addresses the challenge of detecting network intrusions by exploring the efficacy of ensemble learning methods over traditional machine learning models. The problem of network security is exacerbated by sophisticated cyber-attack techniques that standard single model approaches often fail to counter effectively. Our solution employs a robust ensemble methodology to improve detection rates. The research contribution lies in the comparative analysis of individual machine learning models—K-Nearest Neighbors (KNN), Decision Trees (DT), and Gradient Boosting (GB)—against ensemble methods employing soft and hard voting classifiers. This study is one of the first to quantify the performance gains of ensemble methods in the context of network intrusion detection. Our methodological approach involves applying these models to the WSNBFSF dataset, which consists of traffic types including normal operations and various attacks. Performance metrics such as accuracy, precision, recall, and F1-score are calculated to assess the effectiveness of each model. The ensemble methods combine the strengths of individual models using voting systems, which are tested against the same metrics. Results indicate that while individual models like DT and GB achieved near-perfect accuracy scores (99.95% and 99.9%, respectively), the ensemble models performed even better. The soft voting classifier achieved an accuracy of 99.967%, and the hard voting classifier reached 100%, demonstrating their superior capability in network traffic classification and intrusion detection. In conclusion, the integration of ensemble methods significantly enhances the detection accuracy and reliability of network intrusion systems. Future research should explore additional ensemble techniques and consider scalability and class imbalance issues to further refine the efficacy of intrusion detection systems.
Road Object Detection using SSD-MobileNet Algorithm: Case Study for Real-Time ADAS Applications Bouazizi, Omar; Azroumahli, Chaimae; El Mourabit, Aimad; Oussouaddi, Mustapha
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21145

Abstract

Object detection has played a crucial role in Advanced Driver Assistance Systems (ADAS) applications, particularly with integrating deep learning techniques. These advancements have improved ADAS applications by enabling more precise object identification, thereby enhancing real-time decision-making. Object detection models can be categorized into two main groups: two-stage and one-stage models. While prior studies reveal that one-stage detectors generally achieve higher frames per second (FPS) at the expense of some accuracy, they remain better suited for real-time ADAS applications. Our study aims to analyze the performance of an object detection model created using SSD-MobileNet, a one-stage detector approach. We focused on identifying road-related objects such as vehicles, and traffic signs. The contribution of our work lies in developing an object detection model using a pre-trained SSD-MobileNet and employing transfer learning. This process involves introducing a new fully connected layer tailored for the specific identification of objects in road scenes. The retraining of the SSD-MobileNet model is executed through GPU-accelerated transfer learning on the MS COCO dataset, incorporating appropriate pre-processing to exclusively include road-related objects. Our findings indicate promising results for the retrained SSD-MobileNet model, achieving an F1 score of 0.801, and a Mean Average Precision (mAP) of 65.41 at 71 FPS. A comparative analysis with other one-stage and two-stage detectors demonstrates the model's performance, surpassing some existing works in the literature related to road object detection. Notably, our model exhibits improved mAP while maintaining a higher FPS, rendering it more apt for ADAS applications.
Development of Adaptive PD Control for Infant Incubator Using Fuzzy Logic Kholiq, Abd; Lamidi, Lamidi; Amrinsani, Farid; Triwiyanto, Triwiyanto; Mahdy, Hafizh Aushaf; Nazila, Ragimova; Abdullayev, Vugar
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21510

Abstract

This research aims to design an innovative fuzzy logic auto-tuning PD algorithm to control the temperature in a baby Incubator. The proposed Fuzzy-PD method combines fuzzy logic with PD control using the Arduino Mega 2560 microcontroller. The Proportional and Derivative parameters are adjusted by fuzzy logic based on feedback of error values and rate of change of error. The temperature setting range used in data collection is 32-37°C. When the temperature setting is higher, the time required to reach the specified temperature setting becomes longer. The overshoot tends to be low, as the system is designed to respond to temperature changes with high precision. The temperature inside the baby Incubator can be maintained with a low steady-state error value. The adaptive fuzzy-PD system can restore the temperature inside the baby Incubator to the set temperature after a disturbance. Compared to the x device, the average error value is 0.0013%. Independent sample t-tests show no significant difference between the baby Incubator and the Incu analyzer device. It can be concluded that the combination of fuzzy logic and PD control system works well in maintaining temperature stability with low error values. The results are better than previous research focusing on designing a PD algorithm with a maximum rise time of 480 seconds. Furthermore, there is potential for further development with a fuzzy logic auto-tuning PID algorithm to achieve better results.
Tracking Control for Affine Time-Varying Nonlinear Systems with Bounds Nguyen, Nam H.; Vu, Tung X.; Nguyen, Hung V.
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22077

Abstract

In practice, there exist systems with high nonlinearity and time-varying functions. Time-varying nonlinear systems (TVNS) present inherent challenges due to their high nonlinearity and time-varying nature, especially when unknown input disturbance and model uncertainties occur. In this work, a class of single input single output (SISO) uncertain affine TVNS is considered for tracking controller design in the presence of unknown disturbance, in which both the disturbance and model uncertainties are assumed to be bounded. Based on these bounds, a tracking controller will be proposed for first-order uncertain TVNS with unknown input disturbance, and then it is extended for second-order uncertain affine TVNS with unknown input disturbance. Unlike other existing works, the proposed controller does not use fuzzy systems, neural networks or any adaptive mechanism to cope with uncertainties and disturbances. It only uses the bounds of disturbance and model uncertainties, the information of tracking error to compute the control signal, and Lyapunov stability theory is applied to analyze stability of the closed-loop system. In addition, the convergence rate of tracking error can be adjusted by tuning parameters. Some numerical simulations with a first-order system and a model of inverted pendulum are given to verify the developed controller. These systems are uncertain and disturbed by unknown external signals and the proposed controller does not know this information but the tracking error still converges to a small circle containing the origin. The proposed controller can be extended for higher-order systems or MIMO systems such as robotic manipulators.
Techno-Economic and Environmental Analysis of an On-Grid and Off-Grid Renewable Energy Hybrid System in an Energy-Rich Rural Area: A Case in Indonesia Umam, Faikul; Wahyu, Fiki Milatul; Efendi, Mochamad Yusuf; Amir, Nizar; Gozan, Misri; Asmara, Yuli Panca
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22633

Abstract

Developing a dedicated renewable energy hybrid system is a viable option for extending access to electrical energy in energy-rich rural areas. This study conducted a feasibility analysis of using a hybrid energy system, combining solar photovoltaic, wind, and biogas, to generate electricity and meet the energy needs of the rural area. West Waru Village is selected as the case study area for this research because it has abundant renewable energy sources. The Hybrid Optimization of Multiple Energy Resources (HOMER) tools is employed for modeling and optimizing the hybrid energy system, offering a comprehensive analysis encompassing technical, economic, and environmental aspects. Furthermore, the study's findings were further analyzed through a sensitivity analysis, considering unpredictable factors such as village load consumption, solar radiation, wind speed, and biomass availability. Additionally, the study’s results reveals that the renewable energy hybrid system can meet nearly 80% of the rural area's electrical energy requirements at a cost of $0.16 per kWh, resulting in the reduction of 8.4 million kg of carbon dioxide emissions. These findings can serve as a baseline for stakeholders in developing renewable energy systems in rural areas.

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