Keywords: Estimations and identification, Fractional order systems, Observer design
Abstract: A fact is that the dynamics of a Lithium-ion battery possesses fractional-order characteristic and the accurate state of charge estimation can significantly enhance the efficiency and reliability of battery management systems. The aim of this paper is to apply the fractional-order modulating functions method to a fractional-order model of Lithium-ion batteries to online estimate the state rapidly using noisy output measurements. Indeed, the modulating functions method has been applied to fractional-order systems in the past years. Similarly, this work provides algebraic integral formulas for state variables as well as the constructions of the required modulating functions. Different from those studies, the considered non-commensurate fractional-order model is not transformed into the observable canonical form. Numerical simulation results are employed to verify the efficiency of the proposed method in noisy environment.

Keywords: Estimations and identification, Power systems, Signal processing
Abstract: This paper proposes a new Non-Intrusive Load Monitoring (NILM) approach to deal with industrial settings. The goal is to supervise the states of a set of appliances connected to the same electrical network. To do so, the proposed method makes use of characteristic frequencies, thus implying the need of an algorithm capable of estimating signal frequencies and magnitudes. Moreover, prior information about the appliances is to be obtained through a feature extraction step. Some classification algorithms will be tested and compared to each other.

Keywords: Estimations and identification
Abstract: This work introduces a methodology grounded in the standstill frequency response (SSFR) tests as outlined in IEEE Std 115-A. The process begins by determining the frequency response of direct and quadrature axis inductances, followed by the identification of transfer function models through curve fitting techniques. By utilizing time constants as input variables and synchronous machine circuit parameters as outputs, the parameters of different model configurations can be efficiently estimated using the numerical Newton-Raphson method. This technique proves to be both cost-efficient and highly accurate, ensuring dependable results.

Keywords: Estimations and identification
Abstract: This paper focuses on the identification of ARMA models for time-series analysis. The proposed approach is developed in the Unknown But Bounded Error framework. Two Ellipsoidal Outer Bounding (EOB) identification algorithms are proposed. The second algorithm is an extended version of the first one. The paper includes stability and convergence analysis.Some numerical simulations are conducted to validate the analysis and assess the effectiveness of the proposed algorithms.

Keywords: Signal processing, Fault detection and Diagnostics, Image processing
Abstract: In this work, the information obtained from plotting the Recurrence Plot (RP) diagram and Recurrence Quantification Analysis (RQA), referring to the glottal signal waveform, is explored for the diagnosis and assessment of human voice disorders. For that, the glottal signal was initially obtained from the vocal signal through the use of an inverse filtering algorithm : Iterative Adaptive Inverse Filtering (IAIF). RP tracing and RQA were applied to the glottal signal to evaluate the dynamic process and vibration of Vocal Folds (VFs). the normal or healthy voice is considered as a reference. Visual inspection of the RPs and parametric analysis reveals a valuable information about the state of vocal folds under pathologies.

Keywords: Fault detection and Diagnostics, Fractional order systems, Observer design
Abstract: This paper is the first of its kind to present a fractional-order learning observer for diagnosing individual battery faults in a battery string. There are no voltage measurements to each individual battery cell on this string; instead a sensor is mounted at the two ends of the string to measure the total of the voltage values for all cells on the same string. To accurately model a battery cell, the constant phase element is used in the electric circuit model. Because it has the fractional-order characteristic, the battery cell is modeled as a fractional-order system. Based on the fractional-order model of the battery string, a fractional-order learning observer is designed to diagnose the faults from battery controller signals and internal short circuit. Simulation studies are conducted to verify the effectiveness of the proposed fractional-order learning observer.

Keywords: Renewable Energy, Optimization
Abstract: Toward the realization of a decarbonized society, the introduction of renewable energy sources such as solar and wind power generation facilities is expanding. Furthermore, the utilization of green hydrogen, which utilizes renewable energy, is attracting attention. However, there are few papers on the method of determining the facility capacity to build and operate hydrogen facilities at the lowest cost. Our goal is to build a renewable energy and green hydrogen model at the lowest cost. In this study, we set the study period to one year and considered a simulation of the introduction of solar power generation facilities and hydrogen facilities. From the results, we derived the necessary conditions for companies, local governments, and the country to introduce hydrogen facilities that can achieve Japan's carbon dioxide emission reduction target at the lowest operational cost. As a trend, it was shown that an increase in carbon pricing promotes the introduction of hydrogen facilities.

Keywords: Renewable Energy, Optimization, Modeling and simulation
Abstract: Quantum dot solar cells have emerged as promising candidates for next-generation photovoltaics due to their tunable bandgap and potential for high efficiency. This study presents a numerical simulation of a novel quantum dot solar cell structure incorporating a 3D perovskite interfacial layer. The device architecture consists of ITO/TiO2/FASnI3/PbS-TBAI/PbS-EDT, where PbS-TBAI quantum dots serve as the primary absorber, FASnI3 acts as a perovskite interfacial layer, TiO2 as the electron transport layer (ETL), and PbS-EDT as the hole transport layer (HTL). Using SCAPS-1D simulation software, we investigated the device's performance through analysis of J-V characteristics, external quantum efficiency (EQE), energy band diagram, generation-recombination rates and the Electric field across the device. The inclusion of the FASnI3 perovskite interfacial layer aims to enhance charge separation and reduce interface recombination between the TiO2 ETL and the PbS-TBAI quantum dot layer. Simulation results reveal a power conversion efficiency of 12.24 %, with an open-circuit voltage of 0.67 V, short-circuit current density of 32.08 mA/cm2, and fill factor of 56.38%. The EQE spectrum demonstrates a broad response across the visible and near-infrared regions, with peak quantum efficiency exceeding 90% between approximately 400 nm and 700 nm, and maintaining significant efficiency up to about 1000 nm. Analysis of the energy band diagram and generation-recombination profiles provides insights into the charge transport mechanisms and loss processes within the device. This work contributes to the understanding of hybrid quantum dot-perovskite structures and their potential for high-efficiency solar cells. The findings suggest promising avenues for further optimization of QD SC.

Keywords: Control algorithms implementation, Intelligent and AI based control, Fuzzy and neural systems
Abstract: This study introduces a method to enhance the performance of electrode wire feeding mechanisms (EWFMs) in arc welding machines using an artificial intelligent (AI) method, called radial basis function neural network (RBFNN), to implement a self-tuning active disturbance rejection controller (ADRC). The method involves developing an ADRC approach to improve tracking and robustness against disturbances, followed by employing an RBFNN-based method for precise tuning of the ADRC controller's parameters. A real-time testing system on the dSPACE platform is used to validate the effectiveness of this method. The results show that the ADRC controller with RBFNN-based parameter tuning effectively rejects disturbances, minimizes overshoot, and ensures rapid response and high precision, thereby improving the stability and quality of the arc welding process.

Keywords: Renewable Energy, Modeling and simulation, Power systems
Abstract: The increasing need for sustainable energy sources around the globe requires innovative approaches for power distribution and generation. This paper presents an hybrid micro-grid that integrates photovoltaic (PV), battery storage system and hydrogen systems which contains a fuel cell, the electrolyser, and H2 storage tank with grid effectively. Its design significantly lowers carbon emissions and increases cost-effectiveness while maintaining a consistent energy supply, even in emergency situations. Its simulation under Matlab/Simulink analyzes energy consumption, production, total energy costs, and carbon emissions both locally in France and globally, emphasizing the advantages of integrating renewable energy sources. This emphasizes the importance of regional considerations and optimizing the integration of renewable energy for cost efficiency, reliability, and environmental benefits. The article further contributes by exploring the hybridization of battery and hydrogen storage, and studying multiple scenarios using both economic and ecological approaches. An overview of the results indicates significant improvements in energy efficiency and sustainability. The hybrid system shows a notable reduction in carbon emissions and overall energy costs.

Keywords: Renewable Energy, Optimization, Modeling and simulation
Abstract: Here, we use sophisticated numerical simulations to perform a detailed study of 2D Dion-Jacobson (DJ) phase halide perovskite solar cells (PSCs). Our objective is to investigate the ways in which various parameters impact these devices' performance and to determine the optimal design configurations. In particular we investigate how the efficiency of 2D DJ perovskite solar cells is affected by variations in defect density at the interfaces of CdZnS/PeDAMA5Pb6I19 and PeDAMA5Pb6I19/CdTe. With a VOC of 0.92 V, a FF of 65.63%, an overall efficiency of 15.27%, and a JSC of 25.29 mA/cm², the optimized design exhibits remarkable performance parameters. These findings demonstrate the great potential for raising DJ phase halide perovskite solar cells' efficiency.

Keywords: Modeling and simulation, Modeling of complex systems, Mathematical systems theory
Abstract: Within the context of Hybrid Dynamic System (HDS) modelling, Petri Nets (PN) have evolved as a powerful graphical modelling tool, extending their capabilities to effectively represent and model HDS. This paper introduces a novel extension to Petri nets by adapting timed Petri nets to discretely model a specific class of HDS. The proposed Extended Timed Continuous Petri Net (ETCPN) approach facilitates the capture of interactions between HDS components, encompassing both discrete events and continuous dynamics. This is achieved through the innovative integration of ordinary Petri nets and timed continuous Petri nets, resulting in a more comprehensive modelling framework. To validate the efficacy of this modelling paradigm, a simulation example has been implemented. This example serves to visualize both the schematic and analytical representations of HDS, providing insights into the system's behaviour and the ETCPN's modelling capabilities. The results of this study demonstrate the ETCPN's proficiency in addressing the challenges associated with modelling and describing HDS within a discrete time evolution framework

Keywords: Power systems, Fuzzy and neural systems, Networks optimization
Abstract: This paper presents PSS (Power system stabilizer) design based on fractional order fuzzy PID (FOFPID). The proposed PSS architecture design is considered for single-machine infinite bus power system. The primary goal of this design is to reduce or stabilize low-frequency oscillation in power systems. Initially, talking about fractional PID control. Next, combining it with a fuzzy logic controller. The African vulture optimization algorithm (AVOA) is then used to fine-tune the FOFPID parameters ( ) in order to achieve the best possible global stability. The findings of the simulation validate that the system is effective in improving the stability of the generator connected to the infinite power supply.

Keywords: Linear and nonlinear systems, Control applications, Observer design
Abstract: This paper deals with the problem of constrained bilinear control of a parabolic trough solar collector (PTSC) in the infinite-dimensional setting. The PTSC is modeled by a 1^{st}-order hyperbolic PDE, and the objective is to drive the tube's outlet temperature to track a desired profile. The fluid's velocity is the manipulated variable, and that leads to a bilinear control problem. In addition, due to physical and technical considerations, control constraints are imposed. First, based on Lyapunov's stability theory, we design a distributed state-feedback controller that guarantees point-wise exponential stabilization of the tracking error. Then, since only boundary measurements are available, we develop an observer-based controller that stabilizes the tracking error asymptotically. Simulation results attest to the performance of the proposed control method.

Keywords: Robotics, Control algorithms implementation, Modeling and simulation
Abstract: This research explores the application of quantum-inspired neural networks (QNNs) to address the inverse kinematics problem in robotic arms, explicitly focusing on the ABB IRB140, an articulated robot arm with six degrees of freedom. The primary objective is to develop a quantum-inspired activation function for multilayer perceptron (MLP) neural networks. The study evaluates their performance in avoiding singularities by comparing artificial neural networks (ANNs) with quantum neural networks (QNNs). The findings demonstrate that QNNs outperform ANNs in terms of mean absolute error (MAE), achieving a 15.60% lower MAE in the model without singularities and a 16.67% reduction in the Jacobian-based MAE in the model designed to avoid singularities. The study demonstrates that QNNs offer superior accuracy in predicting the robot arm's inverse kinematics, achieving a position error of 1.64 mm and an orientation error of 0.00179 radians while effectively avoiding singularities. These outcomes underscore the potential of quantum-inspired neural networks to enhance robotic arm manipulations' precision, efficiency, and performance.

Keywords: Robust control and Hinfty control, Renewable Energy, Modeling and simulation
Abstract: This paper focuses on enhancing the efficiency of MPPT based on an optimal Tip Speed Ratio (TSR) technique for wind turbines by employing a robust Super-twisting third order sliding mode control.A comparative analysis was conducted to showcase the improvements achieved by employing the new proposed control technique in comparison to conventional sliding mode control SMC, aiming to address its primary limitation, namely, the chattering phenomena.The results validate that the suggested control approach effectively maintains the stability of system and attaining the desired performance, despite variations in parameters.

Keywords: Control applications, Fault detection and Diagnostics, Robust control and Hinfty control
Abstract: Resilience refers how system ability to continue operating at an acceptable level of normalcy in the face of disturbances or threats. This progression aims to attain what has long been recognized as the ultimate advantage of resilience. This paper focuses on the terminology and fundamental issues related to nominal, robust, Fault Tolerant Control (FTC) and resilience. Building on the concept that engineers at any level aim to control (or at least manage) a system, whether it is functioning normally or experiencing faults, successive expansions of the Standard Control Problem (SCP) are proposed. A two tank system have been investigated in order to illustrate innovative control strategies and including simulation results.

Keywords: Optimization, Estimations and identification, Modeling and simulation
Abstract: This work treats parametric optimization of nonlinear systems using genetic algorithms. The authors focus, in this paper, in combining the ARX-Laguerre model and the multimodel approach with its coupled configuration to develop a model named the coupled ARX-Laguerre multimodel. The proposed model is characterized by its Laguerre poles, Fourier coefficients and weighting functions parameters. The complexity of the model is tightly related to the optimal identification of the Laguerre poles characterizing the model. The authors propose an optimization algorithm of the multimodel parameters (Laguerre poles and weighting functions parameters) based on the genetic to guarantee a significant parametric number reduction with regards to the classic ARX multimodel.

Keywords: Optimization, Modeling and simulation, Industrial control
Abstract: Eddy current separation using a rotary drum is a prominent technique for recovering non-ferrous conductive particles from industrial waste. This method is especially effective for sorting conductive materials such as aluminum, copper, and silver... etc. This paper focuses on the design and simulation of an eddy current separator to enhance its performance. A response surface modeling (RSM) approach is utilized to optimize the induced eddy currents in conductive particles, accounting for variations in key parameters: drum speed, bar thickness, and number of poles. The experimental procedure involves a series of steps: (1) defining the variation limits of input variables, (2) establishing the optimal settings, and (3) assessing the process's robustness by testing its performance under slight variations from the set parameters. The study includes one-factor-at-a-time experiments and factorial designs to refine the separation process.

Keywords: Optimization, Robotics, Control algorithms implementation
Abstract: In this paper, a PSO-based global path planning approach is proposed for mobile robots operating in environments highly cluttered with static obstacles and danger zones. Firstly, by suitably representing the workspace environment and the candidate robot path solutions, the path planning task is formulated as a bi-objective optimization problem that includes path length and collision degree objective functions. Then, the weighted sum method is used to reformulate the bi-objective optimization task as a single one. Moreover, to obtain better mobile robot paths in terms of path length and collision degree parameters, the meta-heuristic PSO algorithm is applied to solve the formulated path planning optimization task. Finally, various numerical tests of highly cluttered robot workspace environments have been conducted to assess the performance of the proposed global path planner algorithm.

Keywords: Optimization, Intelligent and AI based control, Modeling and simulation
Abstract: In this paper, an improvement on the nature-inspired hybrid algorithm HPSOGWO is proposed. This latter hybrid algorithm is a combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The newly modified HPSOGWO is designed to enhance the accuracy of convergence. First, the MHPSOGWO is implied to optimize various unimodal and multimodal test functions for different dimensions. Second, an optimization of the gains of a PI proportional and integral corrector in a permanent magnet synchronous motor (PMSM) vector control system is described. Consequently, numerical, statistical, and graphical results reveal that the enhanced variation is slightly better the HPSOGWO, PSO, and GWO variants in terms of solution quality and capacity to locate the global optimum.

Keywords: Image processing, Robotics, Optimization
Abstract: This paper investigates the accuracy of 2D Graph-SLAM maps for the TurtleBot3 (TB3) robot in the Robot Operating System (ROS). We conduct two experiments to assess the impact of Pose Graph Optimization (PGO) and loop closure detection on map quality. In the first experiment, the robot navigates a single-loop trajectory in two indoor environments, generating maps with both standard Graph-SLAM and PGO-based Graph-SLAM methods. The second experiment involves multiple-loop navigation. Teleoperated paths result in non-optimal trajectories and large ROSbag files due to extensive data collection. To address this, we use a map-based navigation method to optimize the robot's trajectory, reducing simulation time and file sizes. Results show that PGO-based Graph-SLAM significantly improves map accuracy and trajectory quality, especially when combined with loop closure detection.

Keywords: Optimization, Signal processing, Modeling and simulation
Abstract: This study explores techniques for modulating the threshold voltage of metal oxide semiconductor field-effect devices. The threshold voltage is a critical characteristic of these transistors. Our analysis examines the factors influencing this parameter and proposes efficient strategies for its adjustment. To validate these approaches, we designed and simulated an n-channel device using computer-aided tools, with tests conducted at a temperature of 0 °C. The findings demonstrate the efficacy of the proposed methods in fine-tuning transistor behavior