Keywords: Image processing, Intelligent and AI based control, Autonomous Systems
Abstract: Evaluating pedestrians is critical when designing new systems for more sophisticated video monitoring and innovative computer vision-based security. Although convolutional neural networks successfully learn different features from images, modeling the pedestrians to the details needed for such tasks remains an issue. In this paper, we present a novel approach based on the multi-head deep learning model to identify the pedestrian attributes, which has merits in this method. Thus, the proposed approach yields better accuracy, robustness, and efficiency in comparison to state-of-the-art methods on a novel database consisting of pedestrian images. We also incorporate further pre-processing steps whereby data augmentation, transfer learning, and model ensembling are additional methods that are used to improve the generality and applicability of the approach proposed in this study. This framework empirically achieves promising accuracy and outperforms existing approaches, proving the usefulness and robustness of our idea in terms of pedestrian attribute recognition.

Keywords: Autonomous Systems, Control applications, Optimal control
Abstract: This paper proposes a model predictive control (MPC) strategy for planning and dynamic obstacle avoidance in autonomous vehicles. The problem consists of two vehicles with different speeds sharing the same road. The objective is to safely allow the faster vehicle to overtake the slower one. The proposed strategy integrates path planning and control into a single optimization problem. Moreover, it formulates nonlinear constraints for lateral deviation and collision avoidance and linearizes them into half spaces to tackle the optimization problem more easily using linear inequality constraints. The proposed approach ensures a safe overtaking maneuver by utilizing information about the predicted positions of the slower vehicle over the MPC prediction horizon of the faster one. Simulation results demonstrate that the proposed MPC strategy can successfully execute safe and smooth overtaking maneuvers on roads with various speeds and curvature.

Keywords: Observer design, Control algorithms implementation
Abstract: In this work, a novel output feedback control strategy for Euler-Lagrange systems that addresses the inherent uncertainties of nonlinear dynamics and feedback limitations is presented. The proposed method employs the Prescribed Performance Control (PPC) technique to ensure that the system’s output tracks the desired reference trajectory within predefined performance specifications. Additionally, we introduce a novel tracking differentiator with adaptive gains to estimate the derivative of the tracking error, effectively overcoming practical challenges where the derivatives of the state and reference trajectory are unavailable. The effectiveness of the proposed scheme is validated through simulation results as well as a real-life experiment.

Keywords: Autonomous Systems, Control algorithms implementation, Motion control
Abstract: Unmanned aerial vehicles (UAV), frequently referred to as drones, are rapidly being used for military and civilian applications. Surveillance and target tracking are two of the newer applications for unmanned systems. Drones are used to track targets, however the pilot often has to operate the drone while also observing the area. The drone must be made autonomous during tracking in order to prevent damage and free up the operator to concentrate on the situation. Using the AR.Drone 2.0, we created a prototype for target tracking via visual servoing. To accomplish this, we used the Robot Operating System (ROS) development platform and the Target Tracking Library (OpenTLD) to develop proportional, integral, and derivative (PID) controllers. This platform enabled us to model our solution in the gazebo simulator and then do tracking tests in a real environment. The tracking is carried out without the use of GPS, using only the drone’s front camera, and the computational work is done on a laptop.

Keywords: Control algorithms implementation, Control applications, Guidance and Control Theory
Abstract: This paper proposes an advanced approach for Quadrotor attitude control using robust adaptive control based on the property of almost passivity. The method addresses the inherent challenges of Quadrotor systems, particularly their lack of strict passivity, as well as the stability issues in the presence of uncertainties and external disturbances. To mitigate these challenges, a Parallel Feedforward Compensator (PFC) is introduced to render the Quadrotor Almost Strictly Passive (ASP), and an Adaptive Synergetic Control (ASC) method is implemented. The combination of PFC and ASC facilitates the stable implementation of the control system. The performance of the proposed ASC is evaluated through simulations and compared with PID and an adaptive backstepping controller.

Keywords: Modeling and simulation, Control applications, Process control and instrumentation
Abstract: Irrigation canal networks play a pivotal role in satisfying the water demands of farmlands around the world. In a time of global warming, automatic irrigation systems are crucial in order to minimize water losses caused by human error and complex dynamics. This work serves as a holistic approach towards the problem of modeling, simulating and controlling the fluid dynamics of irrigation canal networks. The PDEs describing the flow dynamics are briefly illustrated, along with the dynamics introduced by the canal hydraulic structures. The PDEs are simulated using a 2nd order Finite Volume Scheme. The scheme utilizes an HLL Riemann Solver for the spatial discretization and an SSPRK2 time integrator for the temporal discretization. The simulator is coupled with a respective model-based control scheme. The full flow dynamics are approximated using various control-oriented lumped-parameter LTI models derived from literature. The LTI models are compared against the full system dynamics in the time and frequency domains. Subsequently, a centralized MPC master-slave controller is utilized for stabilizing the water levels of the irrigation canal network under changes in the flow regime. Finally, the response of the irrigation canal control system is exhibited in a simulated version of the Corning Canal in California, USA, which serves as Test Case 2 according to the benchmarks set by ASCE for developing canal control algorithms.

Keywords: Renewable Energy, Autonomous Systems, Discrete-continuous time design
Abstract: In grid-forming inverter topologies, droop control typically generates the reference voltage for the capacitor in inverter-based LC filters. However, nonlinear and unbalanced loads often introduce inaccuracies in power calculation, compromising voltage waveform quality. To address this challenge and improve both voltage and current control, precise reference inductor current generation is essential. The proposed method innovatively employs multiple second-order generalized integrators (MSOGIs) and a frequency-locked loop (FLL) to accurately extract positive-sequence load current components, which are used to generate the reference inductor filter current. Furthermore, an enhanced finite control set model predictive control (FCS-MPC) is introduced for the parallel operation of neutral point clamped (NPC) inverters within AC islanded microgrids. The cost function design within the FCS-MPC framework incorporates multiple control objectives, optimizing the performance of the inverter system. The effectiveness of the proposed control strategy is validated using MATLAB Simulink under various load conditions. Compared to the conventional approach, the proposed FCS-MPC control reduces response time by up to 47%, settling time by nearly 60%, and overshoot by approximately 50%, while achieving a THD load voltage of 0.89%. These quantified improvements demonstrate the efficacy of the proposed control strategy.

Keywords: Renewable Energy, Modeling and simulation, Control applications
Abstract: This research explores the sustainable solution to the escalating global water crisis by examining the inventive Employment of green power sources for the treatment of brackish water. Given the frequent criticism of conventional desalination processes for their environmental impact and energy consumption, the incorporation of alternative energy sources, including solar, provides an auspicious resolution to these issues. This article explores the motivation behind implementing Extremum Seeking Control (ESC) in photovoltaic reverse osmosis desalination systems to maximize efficiency and sustainability. By dynamically adjusting system parameters in real-time based on feedback signals, ESC aims to find the operating point that maximizes energy efficiency and water production rate. Meanwhile, Lyapunov is used to regulate the DC bus. This work comprehensively articulates the control methodology and comprehensive modeling of the entire system. MATLAB/Simulink performs the design and regulation of the entire system.

Keywords: Renewable Energy, Embedded Systems, Modeling and simulation
Abstract: This paper presents a comparative analysis of hardware resource utilization for three Maximum Power Point Tracking (MPPT) techniques: Perturb and Observe (P&O), Incremental Conductance (IC), and Artificial Neural Network (ANN). The evaluation uses Processor-in-the-Loop (PIL) testing on the STM32F407 microcontroller. The study aims to provide insights into the efficiency and practicality of each MPPT method when implemented on an embedded system. Key performance metrics, including volatile memory consumption and task execution time, are measured and compared. The results which indicate significant differences in resource utilization between the classical P&O and IC algorithms and the ANN algorithm, highlight the trade-offs between computational complexity and tracking accuracy. These findings have practical implications for engineers and researchers in the field, as they quantify the impact of using intelligent techniques on hardware requirements in real world implementations, thereby engaging the reader with the potential applications of the research.

Keywords: Renewable Energy, Fractional order systems, Optimization
Abstract: This paper presents an optimized method using Fractional Order and Integral Sliding Mode Control (FO-ISMC) to improve power quality in micro-grid-connected PV and wind systems. The DC side integrates PV and wind sources, while the AC side features a three-phase VSI connected to the grid via an RLC filter, supplying linear loads. FO-ISMC manages the DC link voltage and ISMC controls current loops to reduce harmonics and optimize power delivery. MPPT with Incremental Conductance maximizes power under varying weather. MATLAB/ Simulink validation shows the proposed method outperforms conventional PI regulator in terms of performances, robustness, and efficiency.

Keywords: Renewable Energy, Fuzzy and neural systems, Control applications
Abstract: This study presents a novel fixed-time convergent, singularity-free adaptive fuzzy controller for a variable-speed doubly-fed induction generator-driven wind turbine. The primary objective is to optimize power generation. By combining adaptive fuzzy control with fixed-time convergence principles, the proposed controller ensures rapid tracking error convergence, precise tracking, and robustness against system uncertainties. A comprehensive demonstration of the closed-loop system's stability is provided. Simulation tests are performed to illustrate the relevance of the suggested approach.

Keywords: Renewable Energy, Modeling and simulation, Optimization
Abstract: This work explores the impact of several parameters on PEM electrolyzer performance. The investigated parameters were divided into two sections. The first section studies the influence of operating conditions, the second section aims to analyze the effect of manufacturing materials and electrolyzer cell design. Besides, a novel analytical expression has been proposed and integrated in ohmic resistance model, the expression provides a comprehensive description of the number of channels within the flow-field structure, offering a novel correlation between this latter and ohmic resistance, which gives a more comprehensive ohmic model.

Keywords: Control applications, Control algorithms implementation, Process control and instrumentation
Abstract: Recent advances in control theory yield closed-loop neurostimulations for suppressing epileptiform seizures. These advances are illustrated by computer experiments which are easy to implement and to tune. The feedback synthesis is provided by an intelligent proportional-derivative (iPD) regulator associated to model-free control. This approach has already been successfully exploited in many concrete situations in engineering, since no precise computational modeling is needed. iPDs permit tracking a large variety of signals including high-amplitude epileptic activity. Those unpredictable pathological brain oscillations should be detected in order to avoid continuous stimulation, which might induce detrimental side effects. This is achieved by introducing a data mining method based on the maxima of the recorded signals. The real-time derivative estimation in a particularly noisy epileptiform environment is made possible due to a newly developed algebraic differentiator. The virtual patient is the Wendling model, i.e., a set of ordinary differential equations adapted from the Jansen-Rit neural mass model in order to generate epileptiform activity via appropriate values of excitation- and inhibition-related parameters. Several simulations, which lead to a large variety of possible scenarios, are discussed. They show the robustness of our control synthesis with respect to different virtual patients and external disturbances.

Keywords: Control applications, Optimal control, Real time systems
Abstract: This paper presents an advanced feedback color control mechanism for multichannel LED lighting systems, leveraging a multilayer perceptron network. This feedback control ensures the maintenance of the desired lighting color by compensating for external light sources and mitigating disturbances. The system incorporates a multi-spectral sensor, a MIMO PI controller, an anti-windup algorithm, a multilayer perceptron network model, and an array of colored LEDs. The results of the color control demonstrate the precision and efficacy of the proposed scheme.

Keywords: Control applications, Observer design, Guidance and Control Theory
Abstract: This paper presents an innovative application of Active Disturbance Rejection Control (ADRC) to a foldable quadrotor with rotating arms. The study addresses challenges posed by variations in moments of inertia and changes in the center of gravity (COG), demonstrating ADRC’s capability to enhance the quadrotor’s stability and robustness. Internal disturbances due to parameter uncertainties and external disturbances from simulated perturbations were effectively rejected by the ADRC, ensuring stable flight and precise state tracking. Results highlight ADRC’s potential to improve the performance and resilience of foldable quadrotors in dynamic environments.

Keywords: Control applications, Multivariable control, Power systems
Abstract: This article addresses a sensorless control design based on an extended Kalman Filter (EKF) to estimate the state of a doubly-fed induction motor (DFIM) model. This kind of motor is operating as an actuator in electric vehicle (EV) with optimal performance. Thus, a nonlinear model is adopted to simultaneously allow a simpler observability system analysis and a more effective state estimation. The obtained results clearly show both better performance of this suggested control, and the structure of power provided to the machine.

Keywords: Embedded Systems, Control of telecommunications systems, Real time systems
Abstract: Recently, LoRa (Long Range) wireless communication technology has gained prominence as a leading LPWAN (Low Power Wide Area Network) solution in various applications of IoT (Internet of Things). Leveraging Chirped Spread Spectrum (CSS) modulation, LoRa's bit rate per symbol is directly influenced by the spreading factor (SF), which ranges from 7 to 12. This study focuses on an indoor environment, where the RSSI (Received Signal Strength Indicator) parameter is thoroughly examined. Utilizing two HTCC AB01 LoRa transceivers, we assessed the impact of adjustable spreading factors SF9 and SF12 on RSSI performance. Our research operated within the 868 MHz frequency band and a 125 kHz bandwidth, systematically measuring RSSI in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios over short distances. Temperature data from a DHT22 sensor was transmitted to evaluate signal strength under varying conditions. The empirical results demonstrate the practical application of LoRa technology in indoor environments, emphasizing the significant influence of the spreading factor on RSSI performance. Additionally, the study highlights the increased signal power losses experienced under NLOS conditions compared to LOS scenarios.

Keywords: Observer design, Linear and nonlinear systems
Abstract: This paper aims to introduce a robust interval observer for linear continuous-time systems with unknown but bounded uncertainties. Firstly, we present a continuous-time TNL approach, named after the notation for the different used matrices, that incorporates weighting matrices alongside the traditional observer gain. This approach enhances the observer’s performance. Secondly, we propose a novel stability analysis for the resulting error system, leveraging the L1 norm. The design conditions are formulated as a linear programming (LP) problem. Our method not only offers relaxed design conditions but also enhances the accuracy of interval estimation. Through a numerical example, we demonstrate the effectiveness and superiority of the proposed design approach.

Keywords: Renewable Energy, Modeling and simulation, Optimization
Abstract: In this paper, we present a detailed numerical investigation aimed at identifying the efficiency limitations of AgInSe2 solar cells by focusing on the optimization of the electron transport layer (ETL) parameters. Our study investigates how varying the ETL thickness, doping concentration, bandgap, and electron affinity affects the overall performance of the solar cell. We propose an optimized set of geometrical and electrical parameters for the ETL to enhance device efficiency. Through our comprehensive analysis, we demonstrate that by fine-tuning these ETL parameters in conjunction with the properties of the AgInSe2 layer, it is possible to achieve a maximum efficiency of up to 10.45%. The optimized design achieves notable performance metrics, including a high JSC of 21.65 mA/cm², a VOC of 0.67 V, and an FF of 49.64%. This work not only highlights the critical role of ETL parameters in influencing solar cell performance but also provides a framework for addressing degradation factors and guiding further design improvements.

Keywords: Power systems, Renewable Energy, Modeling and simulation
Abstract: This paper presents a dual-stage grid-tied photovoltaic (PV) system designed to power a nonlinear load while simultaneously providing power factor correction on the AC mains using a shunt active power filter (SAPF). The system employs perturbation and observation control for maximum power point tracking (MPPT) from the PV generator, adapting to varying solar irradiance levels. On the grid side, a novel instantaneous active and reactive power (PQ) algorithm, utilizing a self-tuning filter (STF), extracts harmonic references even under non-ideal grid conditions. These references are used by an innovative predictive current control (PCC) strategy to generate the switching signals for a three-phase voltage source inverter (VSI). Additionally, a PI controller maintains the DC-link voltage of the SAPF at its reference value. The performance of the proposed system was evaluated under different conditions of grid harmonics, and unbalanced grid scenarios. Simulation findings indicate that the proposed PCC, coupled with the STF, performs effectively under these test conditions.

Keywords: Renewable Energy, Optimization, Modeling and simulation
Abstract: In this paper, we conduct thorough theoretical investigation with the objective of identifying the efficiency hindering factors in ALL 2D perovskite solar cells. The objective of our analysis is twofold; first we investigate the influence of a variety of parameters on the efficacy of the design and second to optimize its performance. The optimized design surpasses the baseline, achieving a high JSC of 20.86 mA/cm², Voc of 0.99 V, and FF of 74.38%, thus demonstrating the potential to achieve an efficiency of up to 15.49% by strategically optimizing the properties of 2D materials. The proposed study not only enables the identification of responsible degradation factors but also establishes the foundation for additional design optimization.

Keywords: Renewable Energy, Fuzzy and neural systems, Power systems
Abstract: In this paper, a Solar Energy System (SES) is studied. The system consists of a 400 kW PV array connected to a 6.6 kV grid via a DC/DC boost converter and a three-phase VSC (Voltage Source Converter). The boost converter is equipped with a simple and efficient MPPT bloc that allows tracking the optimal power from PV array to make the system delivers the maximum possible power depending to the temperature and irradiance conditions; this MPPT control uses an additional solar cell of the same type configured in open circuit voltage configuration.

Next, a FCS (Fuzzy Control System) is used to control the produced energy and inject it into a grid. Modeling of the systems’ components has been done following the electrical equations. Simulations have been carried out with Matlab; the obtained results validate the proposed model and show the positive reaction of the system with and without MPPT control.

Keywords: Industrial control, Control applications, Linear and nonlinear systems
Abstract: This paper presents an adaptive sliding mode control strategy

for a step down converter operating in continuous conduction

mode. The proposed regulator effectively addresses the

nonlinear, time-varying nature of the converter while ensuring

robust performance against disturbances. Theoretical analysis

is complemented by detailed simulations conducted in

MATLAB/Simulink, demonstrating the regulator's effectiveness

in maintaining stable output voltage under varying load

regimes.

Keywords: Linear and nonlinear systems, Modeling and simulation, Control applications
Abstract: This paper introduces a novel controller, the PIImprove Funnel, which combines two controllers, the Improved Funnel controller from [2] with a proportional-integral (PI) controller.

The PI-Improve Funnel controller is designed to achieve two key objectives: ensuring “tracking error develops inside a specified performance funnel” and enhancing “steady state accuracy” through the integration of the PI controller.

The effectiveness of this new controller is demonstrated through simulations of an example on a mechanical system with a higher relative degree, such as “a mass-spring system mounted on a car”[1].

Comparative analysis with other control strategies validates the superior performance and efficiency of the proposed PI-Improve Funnel controller.

Keywords: Multivariable control, Process control and instrumentation, Control applications
Abstract: It is known that many industrial processes are multiple input multiple output (MIMO) in nature. The main challenge in controlling such processes is the interaction between the loops. In this article, the data-driven virtual reference feedback tuning method VRFT scheme has been applied to control a coupled quadruple tank process. Minimum/Nonminimum phase characteristics may appear based on the valve opening ratios. Simulation results are presented to assess the performance of the data-based tuned PID controllers. Maximum sensitivity is used as a robustness specification tool for the tuning method. Moreover, the gain margin and phase margin are used to asses the robustness of the VRFT based controller due to a plant gain variations.

Keywords: Optimal control, Intelligent and AI based control, Control algorithms implementation
Abstract: Algebraically speaking, linear time-invariant (LTI) systems can be considered as modules. In this framework, controllability is translated as the freeness of the system module. Optimal control mainly relies on quadratic Lagrangians and the consideration of any basis of the system module leads to an open-loop control strategy via a linear Euler-Lagrange equation. In this approach, the endpoint is easily assignable and time horizon can be chosen to minimize the criterion. The loop is closed via an intelligent controller derived from model-free control, which exhibits excellent performances concerning model mismatches and disturbances. The extension to nonlinear systems is briefly discussed.

Keywords: Optimal control, Control algorithms implementation, Linear and nonlinear systems
Abstract: This paper presents an optimized H∞ control for a two degree of freedom (2-DoF) helicopter based on Linear Matrix Inequalities (LMI) and Adaptive Particle Swarm Optimization (APSO). The 2-DoF helicopter is used as a laboratory helicopter model. The nonlinear dynamic equations of the system are obtained using the Euler-Lagrange equations. The integral action is introduced to minimize the static errors. A feedforward gain is used to eliminate the effect of the gravity disturbance. The H∞ criterion is employed to reject the effect of external disturbances. Based on a quadratic candidate Lyapunov function, the H∞ sta- bility conditions of the closed loop system are formulated into an LMI. The optimal weighting matrix of H∞ -LMI is derived using APSO. A comparative assessment on the system performance is investigated between regular integral-LQR, integral-LQR-APSO and the proposed controller.

Keywords: Manufacturing systems, Industrial control, Real time systems
Abstract: Gas turbines are considered one of the most important energy-generating systems in the world, covering the energy shortage resulting from the increasing electricity demand. Although the energy they produce is clean, they face a major environmental challenge, and this is due to the pollution they cause through the gases emitted during their operation.Carbon Oxides (CO) and Nitrogen Oxides (NOx) gas are some of the most important of these gases, and measuring their proportions in emissions has become an urgent matter. This measure contributes to many positive improvements. First, preserving the environment and air pollution by reducing the emission rates of greenhouse gases, which affect warming. Second, knowledge of the proportions of these gases makes us fully aware of the system’s status, which allows us to develop methods for monitoring it using modern technologies. A prognosis of the proportions of emitted gases is one of the good solutions that contribute to the sustainability of gas turbines. In this work, we have proposed an intelligent model to predict the proportion of (CO) and (NOx), based on the Long Short-Term Memory (LSTM) this algorithm gave good results compared to the convolution neural network (CNN 1D).

Keywords: Autonomous Systems, Mechatronics, Motion control
Abstract: This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample- data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws.

Keywords: Renewable Energy, Transportation systems, Modeling and simulation
Abstract: This article investigates the impact of varying compression ratios on the performance, combustion, and emissions characteristics of a hydrogen-fueled internal combustion engine (ICE). The study employs numerical simulations using OpenModelica, an open-source modeling and simulation platform based on the Modelica language. The analysis focuses on three different compression ratios (8:1, 9:1, and 10:1) and examines parameters such as brake power, NOx concentration, and brake thermal efficiency. The results demonstrate that increasing the compression ratio leads to higher peak brake power and NOx emissions, while also improving brake thermal efficiency over a certain power range. The findings highlight the trade-offs between engine performance, efficiency, and emissions, which can inform the design and optimization of hydrogen-fueled ICEs for various applications.

Keywords: Renewable Energy, Power systems
Abstract: Integration of a storage system into hybrid renewable energy systems is investigated in this paper. The storage system features a bidirectional Buck-Boost converter connected with a Lead-Acid battery pack. The converter operates in Buck mode for charging and Boost mode for discharging the batteries. The proposed control strategy aims to regulate the current delivered or consumed by the batteries, ensuring a constant DC bus voltage regulation regardless the system operation mode. This is achieved through the implementation of two cascaded regulation loops. Key innovations include precise design of the converter and effective regulator synthesis through transfer function modeling, addressing inherent challenges posed by nonlinear and variable converter structures. Simulation tests validate the system robustness, demonstrating its capability to meet energy demands and maintain voltage stability under various operational conditions.

Keywords: Renewable Energy, Modeling and simulation, Control education
Abstract: This work details the methodology for the design and implementation of the Double Pulse Test (DPT) method, extensively employed for extracting the dynamic characteristics of Silicon Carbide (SiC) MOSFETs. We focus on the design techniques for the printed circuit board (PCB) and the dimensioning of key components alongside precise measurement techniques. Experimental results from our custom-designed DPT test bench are presented, demonstrating the accuracy and reliability of the test setup. This study provides practical insights into optimizing DPT setups for SiC MOSFETs, contributing to enhanced performance in power electronic applications.

Keywords: Renewable Energy, Power systems, Real time systems
Abstract: The increase of the energy demand presents a main problem for modern buildings. The energy saving and reduction of energy consumption are the two challenges of the world. This paper consists in determining the consumption by uses in a domestic environment from the measurements taken at the level of the energy meter just downstream, using a current and voltage sensor, without a learning phase inside the residence. This so-called non-intrusive method has several advantages. It is used to process load curves and extract useful information for identifying uses, especially the most energy intensive. Also, this method helps us to review the energy management, control, metering and billing systems for our consumption, to achieve our goal of increasing energy efficiency and minimizing consumption.

Keywords: Renewable Energy, Power systems, Modeling and simulation
Abstract: This paper presents a general study of the instruments and control strategies involved in the design of a hybrid microgrid (HMG), where multi-source power production unites are spread within its local confines. The primary goal of this endeavor is to observe, analyze and document the feasibility and performance of hybrid topologies, where a region of stability is adopted for the analysis of system’s response to a step load. The characteristics of the employed devices, namely; current source converters (CSC) on the DC side and paralleled voltage source inverters (VSI) are viewed from a power generation standpoint. For the DC part of the HMG, coordinated power sharing is achieved by super imposing the currents of the sources to possible values that correspond to a desired point on each source’s power production chart. On the AC side, a droop controller is implemented to coordinate the energy production of the different source supplying the AC bus.

Keywords: Control applications, Optimization, Robotics
Abstract: This paper investigates the use of Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms to optimize the Super-twisting Sliding Mode Control (STSMC) technique for robot manipulators. STSMC is a powerful control technique known by its accuracy but it requires a careful tuning of its parameters. By employing PSO and GWO, this study aims to enhance the control performance by automating the tuning process. Simulation results show that both algorithms significantly improve STSMC's robustness, stability, and tracking accuracy besides the enhanced convergence performance. This study highlights the potential of using meta-heuristic optimization techniques in advancing robotic control systems.

Keywords: Control applications, Power systems, Renewable Energy
Abstract: To solve the problem of balanced power grid voltage faults in Wind Turbine Conversion Systems (WTCS) based on Permanent Magnetic Synchronous Generator (PMSG), an advanced method solution based on Machine Side Converter Control (MSC) using the Integrated Back-Stepping Control (IBSC) is proposed in this paper. This nonlinear IBSC technique effectively addresses the harmonics of the stator current in the classical Proportional Integral control (PI) and enhances the dynamic performance of the MSC such as the DC link voltage controls during grid faults. The suggested IBSC method and its efficacy in comparison to the traditional PI method are demonstrated by time-domain simulation tests conducted on a WTCS-driven PMSG utilizing MATLAB/Simulink.

Keywords: Control applications, Robotics, Fractional order systems
Abstract: In this study a novel PD-FOMRAC adaptive control design is proposed to deal with the Parallel robot Veloce trajectory tracking control. The proposed regulator implies an PD control action with parameters adjustment using the DE optimization technique and a fractional-order MRAC based controller. The Veloce model is detailed and numerical simulation results are performed to illustrate the efficiency of the proposed solution that guarantees an improved robust tracking precision and rapidity performance.

Keywords: Control of telecommunications systems, Transportation systems
Abstract: In this paper, we introduce a new design for a high-gain, wide-band Substrate Integrated Waveguide (SIW) cavity-backed antenna, specifically optimized for millimeter-wave applications at 60 GHz. The proposed antenna configuration incorporates a SIW structure with a backed cavity, accommodating an array of four segmented circular patch elements. The antenna is designed and simulated using the High-Frequency Structure Simulator (HFSS). This design achieves a bandwidth of 27%, covering the frequency range from 54.7 GHz to 71.1 GHz, and delivers a peak gain of 14.19 dBi. These simulation results underscore the antenna’s suitability for practical deployment in future wireless communication systems, showcasing significant improvements in performance metrics such as high gain, wide bandwidth, and low side lobe levels. These attributes are essential for advanced millimeter-wave applications, ensuring robust and efficient operation.

Keywords: Modeling and simulation, Modeling of complex systems
Abstract: In this paper, we present a two-dimensional analytical model for an axisymmetric problem that enables the calculation of the magnetic field and eddy currents in a conducting rod surrounded by a cylindrical coil, for both finite and infinite length cases. The analytical model for the finite length case is based on a solution that combines the approach of separation of variables and the Truncated Region Eigenfunction Expansion method (TREEM). This approach allows solving the Laplace, Helmholtz, and Poisson equations formulated within the framework of the magnetic vector potential (MVP). This analytical model takes into account the effects of longitudinal ends in the case of a conductive rod of finite length, but does not include them for the case of infinite length. Furthermore, it computes the complex eigenvalues generated by a nonlinear trigonometric equation. The results of the analytical model's computation are compared with those obtained using the finite element method (FEM).

Keywords: Robotics, Motion control, Modeling and simulation
Abstract: This paper presents a novel approach for mobile robot trajectory tracking using adaptive fuzzy system and terminal sliding mode control in presence of uncertainties and disturbances. The proposed controller aims to tend the posture error to zero in finite time and guarantees the stability of the system using Lyapunov theory. Its configuration solicits two sub-control systems, a kinematic and a dynamic controller. The kinematic controller uses a fast terminal function (FTF) and classical sliding mode theory in order to design the linear and angular control velocities. These control parameters permit to the robot to converge towards the reference and stabilize the posture error to zero. The dynamic controller is constructed based on FTF and adaptive fuzzy system which permits to stabilize the velocity error between the dynamic velocities and the references. Moreover, this adaptive fuzzy controller approximates the nonlinear function of the dynamic model and decreases the disturbances. The simulation works under MatlabSimulink show the robustness of the proposed approach.