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Alfian Ma'arif
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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 26 Documents
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Search results for , issue "Vol 4, No 2 (2024)" : 26 Documents clear
Using Active Filter Controlled by Imperialist Competitive Algorithm ICA for Harmonic Mitigation in Grid-Connected PV Systems Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.; Moubayed, Nazih
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1365

Abstract

Solar energy has been gaining momentum recently, with a focus on maximizing its investment potential due to its reputation as the most sustainable and efficient energy source. This shift towards solar power could potentially reduce the reliance on oil-based fuels in the future. As a result of the integration of photovoltaic (PV) energy sources into the grid, the reliability of power distribution and maintaining its quality in these systems has become increasingly important. The presence of non-linear loads in these grids causes distortion of both voltage and current waves on the grid side, so it is necessary to implement effective reduction techniques to reduce the distortions in these waves. The research contribution is TO introduce the integration of an active filter on the dc side of grid-connected PV systems, along with a control circuit for the filter switches. The control switches were operated using a Sinusoidal Pulse Width Modulation (SPWM) control scheme, while the controller parameters were tuned using the Imperialist Competitive Algorithm (ICA). The proposed system was simulated in the MATLAB/Simulink environment with variations in solar radiation and temperature. The simulation results demonstrated a reduction in the total harmonic distortion factor (THD) for voltage and current waveforms on the grid side, which are within the permissible limits. This confirms the effectiveness of the proposed filter and the efficiency of the control strategy and algorithm for parameter adjustment.
Deep Learning-Based Automated Approach for Classifying Bacterial Images Abougarair, Ahmed Jaber; Oun, Abdulhamid A.; Sawan, Salah I.; Ma’arif, Alfian
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1423

Abstract

Identifying and classifying bacterial species from microscopic images is crucial for medical applications like prevention, diagnosis, and treatment. However, because of their diversity and variability in appearance, manually classifying bacteria is difficult and time-consuming. This work suggests employing deep learning architecture to automatically categorize bacterial species in order to overcome these difficulties and raise the accuracy of bacterial species recognition. We have evaluated our suggested approach using the Digital Images of Bacteria Species (DIBaS), a publicly accessible resource of photographs of tiny bacteria.  This work uses a dataset that differs in terms of bacterial morphology, staining methods, and imaging circumstances. This paper aims to enhance the accuracy and reduce the computational requirements for Convolutional Neural Networks (CNN) based classification of bacterial species using GoogLeNet and AlexNet to train the models. This paper focuses on employing transfer learning to retrain pre-trained CNN models using a dataset consisting of 2000 images encompassing 12 distinct bacteria species known to be harmful to human health.  The concept of transfer learning was utilized to expedite the network's training process and enhance its categorization performance.  The results are promising, with the method achieving an accuracy of 98.7% precision, recall of 99.50%, and an F1-score of 99.45%   with classifier speed. Furthermore, the proposed bacteria classification approach demonstrated strong performance, irrespective of the size of the training data used.  This paper contributes by automating bacterial classification to facilitate faster and more accurate identification of bacterial species, which facilitates the treatment of infections and related diseases, in addition to monitoring public health, and promoting the wise use of antimicrobial drugs. To improve outcomes in the future, researchers can also integrate deep learning techniques with other machine learning methods.
Intelligent PID Controller Based on Neural Network for AI-Driven Control Quadcopter UAV Sahrir, Nur Hayati; Basri, Mohd Ariffanan Mohd
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1374

Abstract

Unmanned Aerial Vehicle (UAV), specifically a quadcopter is publicly popular which it provides services in different applications such as aerial delivery, aerial photography, military, weather forecasting and more examples to date. A Proportional-Integral-Derivative (PID) controller is one of the control techniques that can provide stabilization and reliable trajectory tracking. However, proper PID gains are needed to ensure a stable flight and it should be hybridized or improved to increase the robustness, reliability, and stabilization during flight. In this paper, an intelligent PID controller using neural network is proposed based on Levenberg-Marquardt feedforward neural network training method. The PID gains are initialized using different ranges according to the optimal gains generated by Particle Swarm Optimization, and this contributes towards a good training performance using Mean Square Error (MSE) evaluation. The trained network takes desired output and references as input data to calculate the required combination of PID gains as the output. The including of the response characteristics as the input data for the network, together with reference, error, and control input is the significance of the work. The performance of this work is presented using MSE performances, attitudes and altitude stabilization, and trajectory tracking reliability through error index performances. The simulation results graphically prove that the proposed controller provides better stability with reduced overshoot and settling times. Disturbance rejection is also enhanced by 1.7% compared to manual tuned PID controller. The reliability of the proposed controller highlights avenues for further exploration in AI-driven control strategies for quadcopter systems.
Adaptive Controller Based on Estimated Parameters for Quadcopter Trajectory Tracking Srey, Sophyn; Srang, Sarot
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1342

Abstract

This paper presents a trajectory control system design for a quadcopter, an unmanned aerial vehicle (UAV), which is based on estimated parameters that are assumed to exhibit random walk behavior. Initially, the rotational dynamic model of the UAV is formulated using the Newton Euler method in terms of angular velocity about the x, y, and z axes. This model is then simplified into three separated-first-order linear differential equations, with coefficients derived from the combined effects of inertia, aerodynamic drag, and gyroscopic effects, referred to as lumped parameters. A Proportional-Integral (PI) controller with feed-forward design is then developed to control this simplified model. To adapt the controller to the lumped parameters that exhibit random walk behavior, each simplified equation is restructured into a processing and measurement model. The states of these models are estimated by using the Unscented Kalman Filter (UKF). These estimated values are then utilized to adjust the PI gains and compensate the signal of the designed angular velocity controller, transforming it into an adaptive controller. The entire UAV controller comprises two main parts, an inner loop for adaptive angular rate control and an outer loop serving as an attitude-thrust controller. The proposed controller is simulated using Simulink, with circular and square trajectories. The simulation results demonstrate that the quadcopter successfully follows the desired circular and square paths. The steady-state error for the x and y axes in the square trajectory is less than 0.05 meters within 5 seconds, and for the z axis, it is less than 0.02 meters within 2.5 seconds. The controller gains do not require adjustment when changing trajectories. Moreover, the estimated parameters remain nearly constant at steady state.
Adaptive Load Frequency Control in Microgrids Considering PV Sources and EVs Impacts: Applications of Hybrid Sine Cosine Optimizer and Balloon Effect Identifier Algorithms Hassan, Ahmed Tawfik; Banakhr, Fahd A.; Mahmoud, Mohamed Metwally; Mosaad, Mohamed I.; Rashwan, Asmaa Fawzy; Mosa, Mohamed Roshdi; Hussein, Mahmoud M.; Mohamed, Tarek Hassan
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1448

Abstract

The negative impacts of microgrids (µGs) on the load frequency highlight the importance of implementing a robust, efficient, and adaptable controller to ensure stability. This work introduces an adaptive load frequency control (LFC) for an isolated µG that includes a PV system and electric vehicles (EVs), which have a significant impact on frequency. This control utilizes a combination of sine cosine optimization (SCO) and balloon effect identifier (BEI) algorithms. The controller presented in this work transforms the LFC process into an optimization problem that is highly compatible with various random situations encountered in the control process. The suggested control method is a novel approach by utilizing SCO+BEI for adaptive LFC application, resulting in a highly efficient response. The effectiveness of the proposed adaptive controller is assessed under the conditions of 17 MW variable load, system parameters uncertainties, and installed PV systems of 6 MW.  MATLAB / Simulink package is rummage-sale as a digital test environment. According to simulation results, the proposed adaptive controller succeeds in regulating the frequency and power of an islanded µG. To measure the efficiency of the proposed control scheme, a comparison between other control techniques (such as adaptive controller using Jaya+BEI and classical integral controller) is done. The findings of the studied scenarios assured that the not compulsory control method using (SCO+BEI) has an obvious superiority over other control methods in terms of frequency solidity in case of random load instabilities and parameter uncertainties. Finally, it can be said that the proposed controller can better ensure the safe operation of the µGs.
Power Regulation of a Three-Phase L-Filtered Grid-Connected Inverter Considering Uncertain Grid Impedance Using Robust Control Yay, Socheat; Soth, Panha; Tang, Heng; Cheng, Horchhong; Ang, Sovann; Choeung, Chivon
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1406

Abstract

Uncertain grid impedance is often common in power distribution networks; therefore, it is crucial to design an efficient controller in this situation.  An issue that frequently occurs is the problem of unpredictable grid impedance, which can cause voltage fluctuations, power quality problems, and potential damage to equipment. This work provides a systematic control strategy to tackle these issues by supplying well-regulated power from a DC source to an AC power grid. A linear matrix inequality (LMI)-based robust optimal control is proposed in this paper to provide stability to the inverter system without offset error at the output side. The convergence time to steady state is minimized by solving the LMI problem to maximize the eigen value of the closed-loop system with the inclusion of the uncertainty of the filter parameter and grid impedance. Furthermore, the uncertainties in this study include the potential variation of values for the filters and the grid's impedance. These uncertainties occur because the grid impedance can fluctuate fast in the event of a fault or termination of a transmission line, while the filter's impedance can also be affected by changes in operating temperature. The simulation study of this proposed control includes a comparison between wide and narrow uncertainty ranges, as well as a performance comparison under uncertain parameters. Furthermore, this approach exhibits a lower power ripple in comparison to existing PI control method.
A Novel Sea Horse Optimizer Based Load Frequency Controller for Two-Area Power System with PV and Thermal Units Cenk Andic; Sercan Ozumcan; Metin Varan; Ali Ozturk
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1341

Abstract

This study introduces the Sea Horse Optimizer (SHO), a novel optimization algorithm designed for Load Frequency Control (LFC) in two-area power systems including photovoltaic and thermal units. Inspired by the interactive behaviors of seahorses, this population-based metaheuristic algorithm leverages strategies like Brownian motion and Levy flights to efficiently search for optimal solutions, demonstrating quicker and more stable identification of global and local optima than traditional algorithms. The proposed SHO algorithm was tested in a two-region power system containing a photovoltaic system and a reheat thermal unit under three different scenarios. In the first scenario, the frequency response of the algorithm to a 0.1 p.u. load change in both regions was examined. In the second scenario, the algorithm's frequency response to sudden load changes from 0.1 p.u. to 0.4 p.u. was tested. Finally, the algorithm's frequency response was examined against different levels of solar irradiance for sensitivity analysis. This study compared the performance of the SHO-optimized controller with the optimization algorithms reported in the literature, including the Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA).  In this context, the optimization of PI controller gain parameters based on the ITAE metric resulted in SHO algorithm achieving the best performance with values of 2.5308, followed by WOA at 4.1211, FA at 7.4259, and GA at 12.1244. In tests, SHO significantly outperformed these algorithms in key performance metrics, such as Settling Time, Overshoot (M+), and Undershoot (M-). Specifically, SHO achieved 98.94% better overshoot and 85.25% reduced undershoot than GA, and concluded settling times 52.79% faster than GA in the first scenario. Similar superior outcomes were noted in subsequent tests. These results underline SHO's efficacy in enhancing system stability and control performance, marking it as a significant advancement over conventional LFC methods.
A Combination of INC and Fuzzy Logic-Based Variable Step Size for Enhancing MPPT of PV Systems Ouassa Mohammed Lamine; Noureddine Bessous; Borni Abdelhalim; Fahd A. Banakhr; Mohamed I. Mosaad; Mammeri Oussama; Mohamed Metwally Mahmoud
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1428

Abstract

The significance of using the variable step Incremental Conductance (INC) technique in Maximum Power Point monitoring (MPPT) of photovoltaic (PV) systems resides in its capacity to improve the efficiency of energy conversion. This is accomplished through the constant measurement and comparison of incremental changes in current and voltage, precisely monitoring the maximum power point amidst changing environmental conditions. This traditional INC-MPPT approach has two primary disadvantages. Initially, it employs a predetermined scaling factor that necessitates human adjustment. Furthermore, it adjusts the inclination of the PV characteristics curve to modify the step size. This implies that even little changes in PV module voltage will have a substantial impact on the total step size. As a result, it shifts the operating point away from the intended reference maximum power point. The objective of this work is to improve the efficiency of traditional INC by overcoming the constraints associated with step size modifications. This is achieved by using a fuzzy logic (FL) technique to adjust the step size adaptively in response to environmental changes. The presented INC-FL-MPPT successfully achieves MPPT for a PV system under enhanced steady-state and transient-state settings. The results demonstrate the superiority of the suggested approach compared to three distinct MPPT strategies, namely Perturb and Observe (PO), Classical INC, and PO-FL technique.
Enhancing Hybrid Power System Performance with GWO-Tuned Fuzzy-PID Controllers: A Comparative Study Meetpal Singh; Sujata Arora; Owais Ahmad Shah
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1388

Abstract

This study explores the implementation of a novel control strategy within hybrid power systems, leveraging a Grey Wolf Optimization (GWO)-tuned Fuzzy Proportional-Integral-Derivative (Fuzzy-P.I.D.) controller to enhance the integration of renewable energy sources. By addressing the critical challenge of grid frequency deviations, this approach significantly bolsters the stability and efficiency of power flow, ensuring a more reliable electricity supply. Employing MATLAB simulations, the research underscores the superior performance of the GWO-tuned Fuzzy-P.I.D. controller, which necessitates fewer control interventions and yields lower oscillation frequencies than its conventional P.I.D. and Fuzzy-P.I.D. counterparts. The robustness of this optimized controller is further validated through extensive tests, demonstrating its resilience across a spectrum of parameter adjustments and operational scenarios, including the hypothetical removal of system components. The findings reveal that this advanced control method markedly surpasses traditional solutions in maintaining stable electricity flow and enhancing the system's overall resilience and adaptability to the variable nature of renewable energy. Thus, the GWO-tuned Fuzzy-P.I.D. controller emerges as a significant innovation in hybrid power system management, heralding a new era of optimization and efficiency in renewable energy integration.
Dynamic Performance Evaluation of a Brushless AC Motor Drive Using Different Sensorless Schemes Mohamed A. El Sawy; Omar Makram Kamel; Yehia S. Mohamed; Mahmoud A. Mossa
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1306

Abstract

The presented study concerns with evaluating the dynamic performance of an isotropic sinusoidal brushless motor drive while utilizing different sensorless schemes. Three estimation algorithms are considered: the first depends on extracting the speed and position via comparing two values of motor's voltage in two co-ordinate systems; the second extracts the speed and position signal via comparing two different values of motor's current defined in two co-ordinates; while the third depends on estimating the motor's flux and use it to get the speed and position. The vector control is adopted to manage the drive dynamics. The detailed mathematical derivations for all system components are presented to facilitate the performance analysis. The theoretical base of each sensorless scheme is also described in detail. The target of the provided comparative analysis is to outline the weakness and strength points of each adopted sensorless schemes while estimating the speed and rotor position for a wide operating speed range. The judgment is measured in terms of the speed and rotor position estimation errors and the dynamic response as well. The performance evaluation process is carried out using MATLAB/Simulink software in which all system parts are simulated using their mathematical models. The findings from the study state that when it comes to dynamic speed behaviour, the voltage-based sensorless technique dominates, while the current-based sensorless approach gives stability in speed estimate priority. Alternatively, the third adopted sensorless scheme offers an acceptable high-speed performance and respectable performance at lower speeds. Statistically, it is found that the voltage-based estimation technique gives respectively lower speed and position estimation errors with percentages of 35% and 10% lower than their values under the current-based estimation technique, and with percentages of 35% and 30% lower than their values under the third adopted scheme.

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