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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ghali Ahmad Nezar M. Al-Yazidi Md Shafiullah Mutaz M. Hamdan Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia IRC for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia IRC for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia K.A.CARE Energy Research & Innovation Center King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman Jordan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper proposed a load frequency control (LFC) to ensure power quality of a two-area interconnected hybrid power system (PS) involving integrated electric vehicles (EVs) and communication delay. The control framework consists of three optimization-based proportional-integralderivative (PID) controllers named genetic algorithm (GA-PID), particle swarm optimization (PSO-PID), and grey wolf optimization (GWO-PID) controllers, and fractional order proportional-integral-derivative (FOPID) for improving the system's stability and performance. The performance of the proposed controllers for handling the challenges arising from EV integration, communication delay, and renewable energy sources (RESs) intermittency has been assessed. A thorough performance analysis is subsequently carried out according to the simulation results and various error indices, including integral square error (ISE), integral absolute error (IAE), and integral time absolute error (ITAE). The findings show that the proposed GA-PID controller performs better dynamically than the PSO-PID, GWO-PID, and FOPID controllers in terms of robustness and convergence rate. The results suggest the improvement of LFC in power systems through optimization algorithms.

关键词： Simulation Power quality Stability criteria Power system stability Hybrid power systems Delays Particle swarm optimization Optimization Frequency control Genetic algorithms

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Jamming-induced stiffness modulation using 3D-printed hexagonal microfibers in cylindrical structures

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Materials and Design 2025年 255卷

作者： Zheng, Deyi Jung, Woojun Lee, Seonghyeon Ma, Yunhao Park, Jaewon Hwang, Yongha Department of Control and Instrumentation Engineering Korea University Sejong30019 Korea Republic of Department of Electro-Mechanical Systems Engineering Korea University Sejong30019 Korea Republic of

Active stiffness modulation is essential for soft robotics, wearable devices, and minimally invasive instruments, in which adaptability and mechanical stability are required. However, most existing fiber jamming systems use circular fibers, which provide a limited contact area and reduce inter-fiber friction, thereby limiting their ability to modulate stiffness. This study investigated how the cross-sectional geometry of fibers affects the jamming performance, specifically in terms of contact behavior and resulting mechanical resistance. We fabricated microscale fibers with circular, square, and hexagonal cross sections using a 3D printer and integrated them into polydimethylsiloxane hollow cylinders produced via water-soluble mold casting. Their mechanical responses were evaluated under both unjammed and vacuum-jammed conditions. Hexagonal fibers showed superior performance, achieving a jammed-state stiffness of 342.1 N/mm—56% and 22% higher than those of circular and square fibers, respectively—due to their face-to-face contact and high packing density. The hexagonal structure also sustained a stable stiffness over 10,000 cycles and enabled accurate force sensing in the range of 320–910 mN. This work demonstrates that the cross-sectional geometry serves as an effective design parameter for tuning the stiffness in jamming systems, providing a scalable and accessible route toward programmable stiffness devices for compact robotic and biomedical applications. © 2025

关键词： Microfibers

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Enhancing Traffic Flow Forecasting with Spatio-Temporal Information: A Case Study on Highway Networks in Belgium

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

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International Conference on instrumentation control and Automation (ICA)

作者： Muhammad Farhan Fathurrahman Arne Catteeuw Sidharta Gautama Angel Lopez Aguirre Endra Joelianto Herman Y. Sutarto Department Of Industrial Systems Engineering and Product Design Ghent University Ghent Belgium Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia FlandersMake@UGent–Corelab ISyE Lommel Belgium Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant.

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Optimal Power Generation of Variable Wind Turbine System Using Linear Quadratic Regulator

Optimal Power Generation of Variable Wind Turbine System Usi...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Khalid Abdullah Khan Md Muzakkir Quamar Muhammad Majid Gulzar Muhammad Khalid Electrical Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran KSA IRC for Renewable Energy and Power Systems KFUPM Dhahran KSA SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran KSA Department of Control & Instrumentation KFUPM Dhahran KSA IRC for Smart Mobility and Logistics KFUPM Dhahran KSA

Wind energy source is considered as a viable alternative power generation for establishing a sustainable electricity grid. However, numerous challenges associated with variable and unpredictable output power generation hinder its potential. In this paper, regulation of output power generation of a multi-input-multi-output variable speed wind turbine is performed using linear quadratic regulator (LQR). Considering different wind speed scenarios, the operating range of the wind turbine is achieved. The optimal operation is determined based on the trade-off between the power conversion based on the mechanical fatigue due to the variable loading conditions experienced by the wind generation system. Based on the results obtained the developed LQR controller proves to regulate optimal performance of the variable speed wind turbine.

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Exact Time Distributed Secondary Frequency control for Microgrid with Communication Delays

Exact Time Distributed Secondary Frequency Control for Micro...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Syed Muhammad Amrr Mohamed Zaery S.M. Suhail Hussain Mohammad A. Abido Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) KFUPM Dhahran Saudi Arabia K.A.CARE Energy Research & Innovation Center KFUPM Dhahran Saudi Arabia Electrical Engineering Department KFUPM Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

This paper introduces exact time (ExT) distributed secondary control for an autonomous microgrid (MG) that suffers from diverse time delays. It focuses on adjusting the output states of various distributed generators (DGs) to achieve their defined targets at the desired arbitrary ExT. The developed ExT controller not only restores the MG's frequency but also optimizes the distribution of load power across all DGs. Utilizing Artstein's transformation strategy, the designed control effectively alleviates the adverse impacts of time delays, ensuring ExT convergence. The validity of this ExT convergence is confirmed through a detailed Lyapunov stability analysis. Additionally, comprehensive simulation results showcase the superiority of the proposed control approach over the state-of-the-art techniques.

关键词： Simulation Delay effects Industry applications Power system control Microgrids Generators Delays Frequency control Convergence Lyapunov methods

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Biologically Inspired Algorithm for Adaptive Convergence in Multi-Agent System

Biologically Inspired Algorithm for Adaptive Convergence in ...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Muhammad Majid Gulzar Muhammad Maaruf Khalid Khan Fahad Alismail Muhammad Khalid Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) King Fahd University of Petroleum & Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals

Numerical realization of optimal consensus control is important for multi-agent systems. In this study we formulate a nonlinear multi-agent system based on Cucker-Smale model and develop consensus control to resolve the dynamic problem of flocking. The validation of the framework is performed with random initial position and velocities considering two cases, that is, 5 and 13 birds. Based on the results obtained and presented for 40 iterations it is observed that the agent's position and velocities have successfully converged.

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Optimal control Method for LLC Resonant Convertor of EV Battery Chargers

Optimal Control Method for LLC Resonant Convertor of EV Batt...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Abdullah Memon Muhammad Majid Gulzar Noman Bashir Salman Habib Adnan Shakoor Farheen Ehsan Department of Control & Instrumentation Engineering Interdisciplinary Research Center for Sustainability Energy Systems (IRC-SES) Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML) Interdisciplinary Research Center for Biosystems and Machines (IRC-BSM) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia School of International and Public Affairs Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This research provides a thorough analysis of the LLC resonant converter's mathematical modeling, design, and control using MATLAB Simulink (R2024a). Distinct control strategies, Linear Active Disturbance Rejection controller (LADRC), Proportional-Integral-Derivative (PID), PI and PI-PD controller, have been implemented and rigorously analyzed under varying operational conditions. The investigation encompassed dynamic response, stability, and disturbance rejection capabilities of each controller. The results consistently demonstrated the superiority of the LADRC in comparison to PID family controllers, highlighting enhanced transient response, reduced overshoot, and improved robustness across diverse scenarios. The conclusion drawn from this study supports the efficacy of LADRC in LLC resonant converters, providing valuable insights into the selection of control strategies for optimal performance in practical applications.

关键词： Dynamic response Transient response Renewable energy sources Software packages Optimal control Resonant converters Stability analysis Robustness PD control MATLAB

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Machine Learning based Design of Energy Management System using Non-Intrusive Load Monitoring Strategy

Machine Learning based Design of Energy Management System us...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Kamal Shahid Muhammad Majid Gulzar Muhammad Ali Raza Salman Habib Hasnain Ahmad Institute of Electrical Electronics and Computer Engineering University of the Punjab Lahore Pakistan Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainability Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings.

关键词： Meters Load monitoring Reactive power Artificial neural networks Smart homes Machine learning User interfaces

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Fault Detection in Inertial Measurement Unit and Global Navigation Satellite System of an Unmanned surface vehicle

Fault Detection in Inertial Measurement Unit and Global Navi...

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International Conference on control, Automation and systems ( ICCAS)

作者： Gyeongsub Song Nak Yong Ko Hyun Taek Choi Joono Sur Department of Control and Instrumentation Engineering Interdisciplinary Program in IT-Bio Convergence Systems Chosun University Gwangju Korea Department of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence Systems Chosun University Gwangju Korea Autonomous & Intelligent Maritime Systems Research Division Korea Research Institute of Ships and Ocean Engineering Daejeon Korea Agency for Defense Development PM Research Institute for Advanced Defense Science and Technologies Daejeon Korea

This paper presents a method which detects faults in the measurement required for navigation of an unmanned surface vehicle. The measurement includes acceleration and angular rate by an inertial measurement unit (IMU), and the location by global navigation satellite system (GNSS). The method uses Kalman filter to estimate the state variables which represent the amount of faulty measurements. The method is tested by the experiment in which a boat equipped by commercial IMU and GNSS navigates in a sea trial. The result verifies that faults in acceleration and angular rate measurements are detected robustly and the GNSS position fault is detected when there is considerable location error.

关键词： Accelerometers Global navigation satellite system Sea surface Measurement units Sea measurements Inertial navigation Sensors

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DATASISMO: A model for the forecast of health needs via analysis of the demand for supplies and medicines

DATASISMO: A model for the forecast of health needs via anal...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： Ignacio Chang Humberto R. Álvarez Orlando Aguilar Antony García Víctor López Cabrera Juan Marcos Castillo Department of Control and Instrumentation Engineering Univversidad Tecnológica de Panamá Panama Panama Industrial. Engineering Department Univversidad Tecnológica de Panamá Panama Panama Mechanical Engineering Department Univversidad Tecnológica de Panamá Panama Panama Electrical Engineering Department Ingeniería Eléctrica Univversidad Tecnológica de Panamá Panama Panama Departament of Information Control Systems and Evaluation of Informatics Resources Ingeniería Eléctrica Univversidad Tecnológica de Panamá Panama Panama CIDITIC/CIDETYS AIP Ingeniería Eléctrica Univversidad Tecnológica de Panamá Panama Panama

At the end of 2019, the disease later known as COVID-19 emerged, causing the recent pandemic. The objective of this study was to propose an adaptive mathematical model with the capacity to forecast the health needs of the conntry in the face of the pandemic situation, based on the health needs and the demands for supplies, medicines and other resources identified in the database of the Ministry of Health from the Republic of Panama. In addition, information from the GitHub Repository of the John Hopkins University Center for systems Science and engineering was considered. A quantitative methodology was used from existing data, based on a mixed approach that incorporated dynamic simulation, machine learning and Six-Sigma design. A monitoring model has been developed focused on reducing health risk due to shortages of medicines and medical supplies in the event of a pandemic or similar situation. This research has laid the foundations for the development of epidemiological models in Panama to facilitate data processing, providing a complementary tool for decision-making based on short-term predictions, which is expected to achieve a better distribution of health resources in the country in cases of national health emergency.

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