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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 361 Documents
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Applications of Multi-Objective OPF Solutions with Optimal Placement of Multiple and Multi-Type FACTS Units to IEEE System: Comparison of Different Approaches Hakmi, Sultan Hassan
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1472

Abstract

Optimal power flow (OPF) problem and its implications for power system stability and efficiency is investigated in this study. OPF, a restricted optimization query with non-linearity and non-convexity, is one of the most challenging and fascinating problems in the recent power system. Based on these parameters, researchers have been working hard over the past few decades to identify the best solutions to the OPF issue that maintain system stability. This work presents multi-objective OPF solutions utilizing Newton's technique with numerous multi-type FACTS units. First, the GA is applied to identify the perfect size and location of the FACTS units. Next, the generator and FACTS settings are optimized. In this instance, four scenarios are taken into consideration and three OFs are employed to see how the OFs affect the positioning and dimensions of FACTS devices. The OF is suggested to consider the reduction of both generation costs and transmission losses while also optimizing the power transfer capacity of designated corridors. A full analysis relating to the IEEE-30 bus system is presented and analyzed.
Fuzzy Control for Spacecraft Orbit Transfer with Gain Perturbations and Input Constraint Nemmour, Sarah; Daaou, Bachir; Okello, Francis
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1549

Abstract

This paper presents the problem of fuzzy guaranteed cost tracking control for spacecraft orbit transfer with parameter uncertainties and additive controller gain perturbations and subject to input constraints, and guaranteed cost function. The goal is to perform a planar orbit transfer in a circular orbit, focusing on minimizing fuel usage while accounting for uncertainties in both the plant and controller. Spacecraft dynamics is based on the Keplerian two-body problem using polar coordinates, which allows long-distance maneuvers in circular orbit when the well-known Clohessy-Wiltshire (C-W) equation is restricted by limited-distance maneuvers. To approximate the nonlinearities in the dynamical equation of motion, a Takagi-Sugeno (T-S) fuzzy model is proposed and a linearized model is established for the output tracking problem of the orbit transfer process. Issue related to the absence of a single equilibrium point in the nonlinear system, a gain-scheduling technique based on multiple operating points is employed to develop the (T-S) fuzzy model through the fuzzy approach. Based on the parallel distributed compensation (PDC) approach, sufficient conditions for a fuzzy non-fragile guaranteed cost control are derived. Using the Lyapunov theory, the controller objectives are formulated through linear matrix inequality (LMIs) which allows the system to be transferred into a convex optimization problem. The designed controller effectively accomplishes the orbit transfer process with minimal fuel consumption and maintains the performance level below a specified upper bound. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.
Capability of Hybrid Long Short-Term Memory in Stock Price Prediction: A Comprehensive Literature Review Furizal, Furizal; Ma'arif, Alfian; Firdaus, Asno Azzawagama; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1489

Abstract

Stocks are financial instruments representing ownership in a company. They provide holders with rights to a portion of the company's assets and earnings. The stock market serves as a means for companies to raise capital. By selling shares to the public, companies can obtain funds needed for expansion, research and development, as well as various other investments. Though significant, predicting stock prices poses a challenge for investors due to their unpredictable nature. Stock price prediction is also an intriguing topic in finance and economics due to its potential for significant financial gains. However, manually predicting stock prices is complex and requires in-depth analysis of various factors influencing stock price movements. Moreover, human limitations in processing and interpreting information quickly can lead to prediction errors, while psychological factors such as bias and emotion can also affect investment decisions, reducing prediction objectivity and accuracy. Therefore, machine processing methods become an alternative to expedite and reduce errors in processing large amounts of data. This study attempts to review one of the commonly used prediction algorithms in time series forecasting, namely hybrid LSTM. This approach combines the LSTM model with other methods such as optimization algorithms, statistical techniques, or feature processing to enhance the accuracy of stock price prediction. The results of this literature review indicate that the hybrid LSTM method in stock price prediction shows promise in improving prediction accuracy. The use of optimization algorithms such as GA, AGA, and APSO has successfully produced models with low RMSE values, indicating minimal prediction errors. However, some methods such as LSTM-EMD and LSTM-RNN-LSTM still require further development to improve their performance.
Simulation and Arduino Hardware Implementation of ACO, PSO, and FPA Optimization Algorithms for Speed Control of a DC Motor Najem, Adil; Moutabir, Ahmed; Ouchatti, Abderrahmane
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1483

Abstract

This article proposes implementing and comparing the effectiveness of three optimization algorithms (ACO, PSO, and FPA) for tuning a proportional-integral-derivative (PID) controller on an Arduino Mega 2560 board. This relatively unexplored approach aims to evaluate these algorithms through practical experiments. The choice of PID control is due to its design simplicity and widespread industrial use. Similarly, the permanent magnet DC motor (PMDC) was selected because of its crucial role in various industrial sectors. Tuning PID parameters using optimization algorithms has garnered increasing interest due to its demonstrated efficiency. Several studies have validated the stability of ACO, PSO, and FPA algorithms, justifying their selection. In this article, simulation results showed that ACO, with a response time of 0.322s and an overshoot of 0.68%, was more effective than PSO, which had a response time of 0.768s and an overshoot of 13%. FPA had a response time of 0.347s, close to ACO, but a higher overshoot of 6%. In practice, several factors come into play, such as speed ripples caused by the speed sensor, and machine saturation, which must be considered to ensure practical implementation. After adjusting the PID parameters and integrating a low-pass filter in the feedback loop, ACO, with a response time of 0.596s and an overshoot of 1.68%, was very close to FPA, which had a response time of 0.644s and an overshoot of 0.81%. This comparison highlighted the advantages of the FPA algorithm, which is simple to use, requires fewer parameters to adjust, and takes less time than ACO. This study suggests the potential for implementing a hybrid FPA-ACO algorithm, leveraging the strengths of both algorithms.
Adaptive Frequency Control of an Isolated Microgrids Implementing Different Recent Optimization Techniques Hamid, Mohamed Nasr Abdel; Banakhr, Fahd A.; Mohamed, Tarek Hassan; Ali, Shimaa Mohamed; Mahmoud, Mohamed Metwally; Mosaad, Mohamed I.; Albla, Alauddin Adel Hamoodi; Hussein, Mahmoud M.
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1432

Abstract

In recent years, significant improvements have been made in the load frequency control (LFC) of interconnected microgrid (MG) systems, driven by the growing demand for enhanced power supply quality. However, challenges such as low inertia, parameter uncertainties, and dynamic complexity persist, posing significant hurdles for controller design in MGs. Addressing these challenges is crucial as any mismatch between demand load and power generation inevitably leads to frequency deviation and tie-line power interchange within the MG. This work introduces sophisticated optimization techniques (grey wolf optimization (GWO), whale optimization algorithm (WOA), and balloon effect (BE)) for LFC, focusing on the optimal online tuning of integral controller gain (Ki) for controlled loads. The WOA regulates the frequency of the system so variable loads can be accommodated and 6 MW of PV is added to the MG. A PV and a diesel generator-powered isolated single area MGs with electrical random loads are managed by the adaptive controller by regulating the frequency and power of the PV. Online tuning of integral controllers is possible using the WOA. A comparison is carried out between the WOA+BE and three other optimizers, namely the GWO, GWO+BE method, and the WOA. This paper shows the effect of add BE identifier to standard WOA and GWO. MATLAB simulation results prove that the BE identifier offers a significant advantage to the investigated optimizers in the issue of adaptive frequency stability even when disturbances and uncertainties are concurrent.
Control of a Multimode Double-Pendulum Overhead Crane System Using Input Shaping Controllers Hussien, Sharifah Yuslinda Syed; Jaafar, Hazriq Izzuan; Ghazali, Rozaimi; Ramli, Liyana; Johari, Mohd Khairul Azizat
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1520

Abstract

This paper investigates the impact of higher derivative input shaping for minimizing both oscillations, namely hook and payload of a multimode double-pendulum overhead crane (MDPOC) system. The MDPOC has greater nonlinearities and stronger internal couplings, especially when involving two oscillation frequencies with multimode dynamic effects. With a suitable system’s natural frequency and damping ratio of the hook and payload oscillations, multimode zero-vibration (ZV-ZV), multimode zero-vibration derivative (ZVD-ZVD) and multimode zero-vibration derivative-derivative (ZVDD-ZVDD) shapers are successfully designed. More interestingly, two scenarios under a fixed cable length and a payload hoisting are considered which are closer to the real practical crane.  Thus, an average travel length (ATL)-based shaper method is also considered to further verify the effectiveness and robustness of efficient hook and payload oscillation control under payload hoisting. All the multimode input shaping is simulated using the Matlab software. The simulation results of multimode ZVDD-ZVDD shaper successfully reduced in the overall hook and payload oscillations by 97.9% and 97.2%, respectively, compared to the unshaped system, whereas the multimode ATL-ZVDD shaper reduced hook and payload oscillations by 94.8% and 94.0%, respectively. In fact, the multimode ZVDD-ZVDD and multimode ATL-ZVDD shapers demonstrate the superiority in minimizing the hook and payload oscillations compared to the multimode ZV-ZV, multimode ZVD-ZVD, ATL-ZV and ATL-ZVD shapers. This significant reduction in oscillations enhances the precision and safety of real-world crane operations in industrial settings. It has been proven that considering the additional derivative of input shaping results in a higher level of hook and payload oscillations reduction.
Study of the Crowbar's Functioning in Doubly Fed Induction Wind Generators: Towards Achieving Fault Ride Through Capability Alnami, Hashim
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1485

Abstract

This work examines the analysis of temporary behaviors and crowbar hardware layout for enhancing the fault ride-through capability (FRTC) in doubly fed induction wind generators (DFIWGs) A crowbar that is linked in parallel to the rotor side converter (RSC) is a feature found on the majority of DFIWGs these days to safeguard the RSC and DC-bus capacitor (DCBC). Previous studies demonstrated that the crowbar resistance has an impact on the DFIWG transient response's oscillations and peak values. In order to satisfy the FRTC criterion, the article initially methodically examines the DFIWG dynamics with and without a crowbar during a 100% voltage dip and studies the effects of two resistance values on the DCBC. It has been demonstrated that choosing a crowbar resistance greater than the permitted range may cause the DFIG FRT performance to decline. By actively addressing grid faults and improving performance, stability, and dependability, this integrated crowbar shows the potential of state-of-the-art control approaches for the dependable and efficient use of DFIWGs. MATLAB/Simulink is used to run robust simulations, and the results unambiguously show that the proposed model may significantly improve the FRTC of DFIWGs.
Seasonal Electrical Load Forecasting Using Machine Learning Techniques and Meteorological Variables Singh, Bali; Shah, Owais Ahmad; Arora, Sujata
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1446

Abstract

Accurate forecasting of seasonal power consumption is crucial for effective grid management, especially with increasing energy demand and renewable energy integration. Weather patterns significantly influence energy usage, making load prediction a challenging task. This study employs machine learning algorithms, including Random Forest (RF), Artificial Neural Networks (ANN), and Decision Tree (DT) models, to forecast electricity consumption using meteorological variables such as solar irradiance, humidity, and ambient temperature. The impact of weather elements on load prediction accuracy across different seasons is explored using seasonal forecasting techniques. The results demonstrate the superior performance of ANN and RF models in forecasting summer and winter loads compared to the rainy season. This discrepancy is attributed to the abundance of data for the summer and winter seasons, and the ability of the models to capture complex patterns within the data for these particular seasons. The study highlights the potential of machine learning techniques, particularly ANN and RF, in conjunction with meteorological data analysis, for enhancing the accuracy of seasonal electrical load forecasting. This can contribute to more effective power grid management and support the transition towards a more sustainable energy landscape. The findings underscore the importance of data quality, quantity, and appropriate model selection for different seasonal conditions.
Accurate Robot Navigation Using Visual Invariant Features and Dynamic Neural Fields Raoui, Younès; Elmennaoui, Nouzha
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1545

Abstract

Robot navigation systems are based on Simultaneous Localization and Mapping (SLAM) and obstacle avoidance. We construct maps for the robot using computer vision methods requiring high repeatability for consistent feature tracking. Also, the obstacle avoidance method needs an efficient tool for fusing data from multiple sensors. This research enhances SLAM accuracy and obstacle avoidance using advanced visual processing and dy namic neural fields (DNF). We propose two key methods: (1) an enhanced multiscale Harris detector using steerable filters for robust feature extrac tion, achieving around 90% repeatability; and (2) a dynamic neural field algorithm that predicts the optimal heading angle by integrating visual de scriptors and LIDAR data. The first method’s experimental results show that the new feature detector achieves high accuracy, outperforming exist ing methods. Its invariance to the orientation of the image makes it insen sitive to the rotations of the robot. We applied it to the monocular SLAM and remarked that the positions of the robot were computed precisely. In the second method, the results showed that the dynamic neural fields algo rithm ensures efficient obstacle avoidance by fusing the gist of the image and LIDAR data, resulting in more accurate and consistent navigation than laser-only methods. In conclusion, the study presents significant advance ments in robot navigation through robust feature detection for SLAM and effective obstacle avoidance using dynamic neural fields. These advance ments significantly enhance precision and reliability in robot navigation, paving the way for future innovations in autonomous robotic applications.
EEG-Based Lie Detection Using Autoencoder Deep Learning with Muse II Brain Sensing Hermawan, Arya Tandy; Zaeni, Ilham Ari Elbaith; Wibawa, Aji Prasetya; Gunawan, Gunawan; Hartono, Nickolas; Kristian, Yosi
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1497

Abstract

Detecting deception has significant implications in fields like law enforcement and security. This research aims to develop an effective lie detection system using Electroencephalography (EEG), which measures the brain's electrical activity to capture neural patterns associated with deceptive behavior. Using the Muse II headband, we obtained EEG data across 5 channels from 34 participants aged 16-25, comprising 32 males and 2 females, with backgrounds as high school students, undergraduates, and employees. EEG data collection took place in a suitable environment, characterized by a comfortable and interference-free setting optimized for interviews. The research contribution is the creation of a lie detection dataset and the development of an autoencoder model for feature extraction and a deep neural network for classification. Data preparation involved several pre-processing steps: converting microvolts to volts, filtering with a band-pass filter (3-30Hz), STFT transformation with a 256 data window and 128 overlap, data normalization using z-score, and generating spectrograms from power density spectra below 60Hz. Feature extraction was performed using an autoencoder, followed by classification with a deep neural network. Methods included testing three autoencoder models with varying latent space sizes and two types of classifiers: three new deep neural network models, including LSTM, and six models using pre-trained ResNet50 and EfficientNetV2-S, some with attention layers. Data was split into 75% for training, 10% for validation, and 15% for testing. Results showed that the best model, using autoencoder with latent space size of 64x10x51 and classifier using the pre-trained EfficientNetV2-S, achieved 97% accuracy on the training set, 72% on the validation set, and 71% on the testing set. Testing data resulted in an F1-score of 0.73, accuracy of 0.71, precision of 0.68, and recall of 0.78. The novelty of this research includes the use of a cost-effective EEG reader with minimal electrodes, exploration of single and 3-dimensional autoencoders, and both non-pretrained classifiers (LSTM, 2D convolution, and fully connected layers) and pretrained models incorporating attention layers.

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