Keywords: Renewable Energy
Abstract: This research aims to evaluate the resilience of control techniques for Doubly Fed Induction Generator (DFIG). The proposed control methods for regulating the power output of the Doubly Fed Induction Generator (DFIG) involve feedback linearization and sliding mode techniques. These approaches aim to match the rotation speed of the turbine with the optimal point of the power coefficient. Afterwards, we examine the ability of various techniques to withstand changes in parameters. The simulation results exhibit favorable performance in terms of maintaining stability in reference tracking for both strategies. It is worth mentioning that sliding mode control demonstrates more resilience in comparison to feedback linearization.

Keywords: Control algorithms implementation, Robotics, Fractional order systems
Abstract: This paper proposes a control strategy for a robot manipulator with four Degrees Of Freedom (DOF) that takes advantage of sliding mode control theory, exponential reaching law, and fractional calculus. Tracking the joint space trajectories with precision is the goal of the proposed Fractional Exponential reaching law Sliding Mode Control (FrExpSMC). Utilizing fractional calculus in the sliding surface enhances the robustness of the exponential reaching mode against external disturbances. The fractional calculus expression of the sliding surface ensures a finite time convergence of the controller and provides the controller with a fast response in the transient phase. The proposed control approach s efficiency and performances are demonstrated by simulation on a model of a 4-DOF robot manipulator and compared to the conventional sliding mode control. The stability of the closed-loop system is proved using Lyapunov theory. The results indicate that the recommended control outperforms the conventional sliding mode control in both tracking performance and robustness against step disturbances.

Keywords: Fractional order systems, Multivariable control, Optimization
Abstract: This paper presents the application of fractional order operator to improve the control quality of multivariable systems. The basic ideas of this tuning method are based, in the first place, on the existed tuning methods for setting the parameters of the fractional order PI (FO-PI) controller for λ=1, which means setting the parameters of the integer order PI (IO-PI) controller, the minimum integral criterion by using Particle Swarm Optimization (PSO) algorithm for setting the fractional integration action order λ and the gains of the fractional order PI controller. The integral criterion is formulated to improve the dynamic response of the system, while causing minimum control effort. The Distillation Column, which is a multivariable system with two inputs and two outputs (TITO), in closed loop TITO with decoupling control structure, is analyzed. Simulation results are presented to show the control quality improvement of this proposed FO-PI controller tuning method compared to the IO-PI controller tuned using any existed tuning method.

Keywords: Image processing, Signal processing, Modeling and simulation
Abstract: This paper presents an application of a Region of Interest(ROI)-based compression technique designed to enhance the energy efficiency of visual sensor networks used in wildlife monitoring. By focusing on compressing only the most critical regions within each video frame, the proposed method significantly reduces data volume, leading to substantial energy savings during both compression and transmission stages. The integration of LoRaWAN technology further optimizes energy consumption by providing low-power, long-range communication capabilities. Experimental results demonstrate a compression ratio of 4:1, achieving overall energy savings of approximately 38% for short-range and 40% for long-range transmission compared to traditional non-ROI methods. Despite a slight reduction in image quality, the visual integrity remains acceptable for effective wildlife monitoring, and the method improves transmission success rates over varying distances. These findings highlight the potential of ROI-based compression to extend the operational lifespan of sensor nodes, offering a viable and sustainable solution for long-term environmental monitoring.

Keywords: Renewable Energy, Modeling and simulation, Optimization
Abstract: This paper presents an integrated 3D computational model for analysing high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs), focusing on the interactions within the system's components and processes. Developed using COMSOL software, the model incorporates key physical phenomena such as mass transport, electrokinetic, and heat transfer. By simulating various operating conditions and material properties, this study provides insights into the influence of parameters like operating pressures, gas diffusion layer porosity, anode stoichiometry, cathode stoichiometry, and membrane conductivity on fuel cell performance. The aim is to enhance understanding of HT-PEMFC operation and guide the development of more efficient and durable fuel cells.

Keywords: Modeling and simulation
Abstract: This paper aims to determine the airgap magnetic flux density of a surface-mounted Permanent Magnet Vernier Machine (PMVM) by considering both rotor and armature Magneto-Motive Forces (MMFs). The calculation method relies on permeance and MMF concepts to predict the mid-airgap flux density. Two Magnetomotive-Permeance Function (MMF-PF) methods are applied and compared: the first one is based a predefine flux lines permeance fucntion model, while the second one employs a conformal transformation approach to evaluate the airgap permeance fucntion. A square waveform is chosen for the MMF in both models. The effectiveness of these methods is assessed using Finite Element Analysis (FEA), focusing on an outer rotor PMVM as a case study. Results indicate that the first method is more accurate in calculating the armature magnetic field, whereas the second method proves more efficient for predicting the rotor magnetic field. The combined approach leveraging both methods offers a reliable and expedient alternative to FEA, particularly useful during the initial design phases of PMVMs.

Keywords: Transportation systems, Intelligent and AI based control, Optimization
Abstract: Road travelers worldwide encounter a pervasive issue of traffic congestion. Because of its ability to manage intricate large-scale urban settings, deep reinforcement learning (DRL) has found extensive use in Adaptive Traffic Signal Control (ATSC). However, the majority of current algorithms are tailored to particular road networks or traffic scenarios, posing difficulties in their adaptation to large complex road environments. To tackle the mentioned issues, this paper suggests novel cooperative approach-based Multi-Agent Reinforcement Learning (MARL) for controlling large-scale networks. Traffic congestions are forecasted using the intensity of pheromone considering its neighboring agents to create fluid green waves. Double Deep Q-Network (DDQN) is designed to enhance execution efficiency and traffic signal stability in complex environments for each agent, where each agent controls a region. Based on Batna traffic data, results showcase the efficacy of the proposed approach outperforms

Keywords: Real time systems, Signal processing, Fault detection and Diagnostics
Abstract: This paper presents a new architecture for implementing the Fast Fourier Transforms (FFT) on a field-programmable gate array (FPGA). The proposed architecture comprises one computational butterfly unit comprising one rotation operator and two complex adders. We have provided a detailed explanation of the structure. Our design demonstrates remarkable flexibility when handling increased FFT points. It maintains a fixed utilization of digital signal processing (DSP) resources while experiencing only minor increments in Look-Up Tables (LUTs) and flip-flops. This empowers us to confidently handle a more significant number of FFT points without resource concerns. The FFT architecture is also implemented in real-time on an FPGA, and its application is demonstrated in the context of broken rotor bar detection in induction motors, with the results displayed on a graphical user interface (GUI). The GUI further enhances usability by facilitating signal visualization. The results showcase the efficiency and effectiveness of the implemented architecture for real-time FFT computation in the specified application.

Keywords: Networks optimization, Bond graphs, Fuzzy and neural systems
Abstract: The integration of intrusion detection systems is essential for robust network security. Enhancing the effectiveness of IDS requires advanced methods to manage intrusion detection data, with machine learning playing a pivotal role. However, machine learning approaches often struggle to recognize complex attack patterns and tend to generate numerous false positives, especially with unknown attacks. To address these challenges, enhancing intrusion detection systems capabilities through the use of knowledge graphs is crucial. Knowledge graphs have proven to be effective tools for modeling and analyzing intricate interactions within security data. This article aims to explore an approach that combines Knowledge Graphs with Machine Learning and Deep Learning. By leveraging the semantic modeling, querying, and reasoning capabilities of Knowledge Graphs, this integration seeks to tackle the challenges of intelligent detection and decision-making in cybersecurity.

Keywords: Fuzzy and neural systems, Control applications, Intelligent and AI based control
Abstract: The objective of this work is to develop and innovate within the physicochemical analysis laboratory unit of a drinking water production station, with a particular focus on the key step in drinking water production: coagulation. This research combines aspects of automation, artificial intelligence, and biotechnology to develop an intelligent methodology for predicting the coagulant dose based on measured raw water characteristics, such as turbidity, pH, and temperature, using the Adaptive Neuro-Fuzzy Inference System (ANFIS) of the Takagi-Sugeno (TS) type. This research was conducted in collaboration with the SEATA drinking water production station in Annaba, Algeria.

Keywords: Intelligent and AI based control, Linear and nonlinear systems, Robotics
Abstract: This paper considers iterative learning control (ILC) design problem for a discrete system operating in a repetitive mode with input backlash using the measurable output. Two structure of ILC law are considered and compared. The influence of the backlash dead zone on the accuracy of the reference trajectory tracking is investigated.

Keywords: Motion control, Control algorithms implementation, Robotics
Abstract: This paper presents a hybrid path-planning algorithm that combines both global and local planning techniques. An offline global path planning approach based on the A-Star graph search algorithm is used to find the optimal path from the start to the goal. However, A-Star has some limitations, such as a lack of smoothness, lower safety margins, and an inability to adapt to dynamic environments. To address these issues, we propose integrating an online local path planning method based on the Artificial Potential Field (APF) for real-time obstacle avoidance. Simulation results demonstrate the effectiveness of the hybrid approach, guiding the mobile robot along the optimal global path while ensuring the locally generated trajectory is smooth enough for execution by the controller. The results also show improved navigation performance, with enhanced efficiency and safety due to the hybrid algorithm.

Keywords: Fuzzy and neural systems, Renewable Energy, Modeling and simulation
Abstract: This study compares the traditional MPPT algorithm to an intelligent fuzzy logic-based controller for a photovoltaic system. Through the control of DC-DC boost, these methods are used to track down and extract a PV system's maximum power point. Thanks to these algorithms, the PV generator may produce its maximum output regardless of alterations in the exterior environment (such as temperature changes and radiation). This article aims to provide a comparative analysis of several conventional and modern MPP tracking techniques for solar panels. Simulation results from the Matlab Simulink environment demonstrate the dependability of the proposed MPPT.

Keywords: Fault detection and Diagnostics, Optimization
Abstract: In the present work, we provide a short literature review on prognostics hybrid approaches as recently emerged in a wide range of industrial applications. In this context, we cover the main concepts of the prognostics field, different aspects of prognostic hybrid approaches in addition to basic benchmarks employed during the evaluation of hybrid approaches. We finally conclude with some observations and guidelines. We would like to mention that all these aspects were justified with appropriate up to date sources.

Keywords: Fault detection and Diagnostics, Control of telecommunications systems, Time-delay systems
Abstract: This paper focuses on the design of fault-tolerant control for networked control systems subject to actuator faults. A novel state feedback control scheme is introduced to ensure the stability of faulty networked control systems (NCS) where actuator faults are represented as multiplicative terms. Utilizing a Lyapunov-Krasovskii Functional (LKF) and accounting for uncertainty in multiplicative actuator faults, the study establishes relaxed Linear Matrix Inequality (LMI) conditions. These conditions ensure closed-loop NCS stability in the face of actuator faults and offer flexibility in accommodating network-induced delays. Two illustrative examples with different scenarios are provided to showcase the effectiveness and improvements of the proposed networked controller design.

Keywords: Intelligent and AI based control, Fuzzy and neural systems, Control applications
Abstract: This research focuses on developing an adaptive finite-time tracking controller for a class of non-strict-feedback nonlinear systems. The controller design addresses multiple challenges, including input saturation, both matched and unmatched external disturbances, and model uncertainties. Adaptive fuzzy systems are employed to approximate a set of uncertain nonlinear functions. An adaptive fuzzy disturbance observer is constructed to enhance control robustness. Most importantly, by designing virtual controls as smooth functions and using finite-time filters, the proposed control is free from singularity. The proposed control strategy's efficacy has been substantiated through numerical simulations.

Keywords: Signal processing, Modeling and simulation, Modeling of complex systems
Abstract: This paper presents investigates how temperature change and changes in the channel dimensions of NMOS transistors affect CMOS (Complementary Metal Oxide Semiconductor) circuit technology. In order to streamline this investigation, we initially identified important variables that define the device's performance and may be vulnerable to these influences. The analysis included important metrics such as the transfer characteristic, saturation point, and drain current. Notably, our simulation results revealed significant effects from temperature and geometric changes in NMOS. These parameters enabled us to validate the results obtained from the CADENCE simulator.

Keywords: Linear and nonlinear systems, Renewable Energy, Process control and instrumentation
Abstract: this paper proposes a modified and efficient control strategy for controlling a doubly fed induction generator (DFIG) based wind farm (WF) connected to an unbalanced electrical grid voltage. This strategy has two loops, the first is main and the second is auxiliary. The main loops control the positive sequence currents of the rotor and the grid side converter (GSC), while the auxiliary loops control the negative sequence currents. In this work, the positive and negative rotor current loops of each DFIG were controlled using a nonlinear integral backstepping control (IBSC) algorithm, while the positive and negative sequence current loops of each GSC were controlled using a proportional-integral (PI) controller. This study was validated by comparing the proposed strategy with the single-loop strategy through simulation in the Matlab/Simulink environment. The obtained results show that the proposed strategy allows to reduction of the oscillations in the active and reactive power generated by the WF, which reduces the oscillations in the DC bus voltage and reduces the total harmonic distortion.

Keywords: Renewable Energy, Power systems
Abstract: In light of the global energy crisis and the need to meet the huge demands for electricity, it is crucial to make short-term predictions about energy production. Solar energy is a clean and sustainable source of electricity generation, and solar systems are connected to smart grids that feed all categories of users. Therefore, it is important to consider the potential for solar energy production in the short term. This paper presents an approach based on machine learning algorithms to predict solar energy generation in a solar plant in India. Performance of the proposed models, linear regression and random forest regression, is verified using metrics such as R , mean square error (MSE), MAE, and others.

Keywords: Renewable Energy, Intelligent and AI based control, Signal processing
Abstract: Non-intrusive load monitoring can be considered as a knapsack or blind source separation problem, complicated by numerous possible combinations of devices that recon- struct total power. Simultaneous appliance usage and the presence of noise further complicate the task of accurately disaggregating the total power consumption into individual appliance contributions. In this paper we aim to solve the inverse problem of disaggregating the active power of each individual appliance from the overall power consumption and current harmonics. Additionally, we compare the per- formance of random forest, convolutional neural networks, support vector machine and linear regression models on the same set of features.

Keywords: Control algorithms implementation, Power systems, Renewable Energy
Abstract: This paper examines a hybrid renewable energy system that integrates photovoltaic (PV), fuel cell (FC), and battery technologies to meet isolated DC load requirements. The primary energy source is PV, with batteries functioning as a storage system, supplying energy when PV is unavailable. The FC serves as an emergency source when both PV and battery are unavailable or underperforming. An algorithm control strategy was developed to enhance system reliability and extend the lifespan of the sources (battery, FC). Energy management was evaluated using MATLAB/SIMULINK by integrating the STAT FLOW section block.

Keywords: Renewable Energy, Power systems
Abstract: In this paper, we present an enhanced Maximum Power Point Tracking (MPPT) algorithm for photovoltaic (PV) systems, combining the well-established Perturb and Observe (P&O) method with an Ultra-Local Model (ULM) control approach. The proposed technique aims to improve the efficiency and performance of PV systems by addressing the limitations of the traditional P&O algorithm, such as slow response times and oscillations around the maximum power point (MPP) under varying environmental conditions. The ULM-based MPPT algorithm dynamically adjusts the reference voltage used in the P&O method, leveraging online estimations to optimize the control action. This integration enables faster convergence to the MPP and reduces steady-state oscillations, leading to more efficient energy harvesting from the PV system. Simulations results demonstrate the superiority of the proposed method over the conventional P&O algorithm. The enhanced algorithm exhibits improved tracking accuracy, stability, and adaptability to changes in irradiance, ultimately boosting the overall efficiency of the PV system. This study highlights the potential of combining classical MPPT techniques with advanced control strategies to achieve higher performance in renewable energy systems, paving the way for more reliable and efficient solar power generation.

Keywords: Renewable Energy, Control algorithms implementation, Fuzzy and neural systems
Abstract: To perform the stability and the robustness of the dc-link voltage in the photovoltaic (PV) grid-connected system, Fuzzy super twisting sliding mode strategy is presented in this paper. The combinaison between the Nonlinear super twisting sliding mode and fuzzy logic enhance the robustness and optimize the system response. The tracking superiority of the proposed control is illustrated in dc-link bus voltage regulation with fast transient response and low oscillations and total Harmonic Distortion (THD) reducing in the current injected to the grid under any weather conditions. To show the superiority of the between the proposed controller(FSTC) comprehensive comparisons are done with traditional control techniques, including th conventional Proportional Integral (PI), and Super-Twisting Sliding Mode Control (STC) using MATLAB/SIMULINK .