Keywords: Transportation systems, Real time systems
Abstract: In the context of the Intelligent Transportation System (ITS), the exchange of information among vehicles and other entities is made possible through Vehicle-to-Everything (V2X) communications. This includes interactions with roadside infrastructure units, cycles, and pedestrians, in addition to other vehicles, thus enabling a broad array of advanced applications, including safety and non-safety infotainment applications. To support V2X, two primary Radio Access Technologies (RATs) are utilized: Dedicated Short Range Communications (DSRC) and Cellular Networks (C-V2X). However, with the anticipated increase in the number of vehicles on the roads and the emergence of more advanced applications, it is unlikely that a single technology will satisfy the Quality-of-Service (QoS) requirements for the increased number of vehicles in the near future. Hence, exploring multi-RAT diversity for effective V2X communications is proposed. This paper encapsulates a detailed overview of V2X RATs based on their state-of-the-art, evolutions, and limitations to determine the design and implementation issues in multi-RAT scenarios. Generally, this review study intends to provide a solid platform that will reduce time and effort for the research community working on V2X multi-RAT and heterogeneous contexts. Additionally, it addresses new open issues for potential future research directions and identifies trends and future technologies that will lead to more efficient V2X multi-RAT deployment.

Keywords: Transportation systems, Renewable Energy, Fault detection and Diagnostics
Abstract: This paper introduces an innovative approach for determining the dynamic stability margin of a power system. The method employs a Genetic Algorithm (GA) to optimize the parameters of a Conventional Power System Stabilizer (CPSS) linked with the Automatic Voltage Regulator (AVR) in a multi-machine power system. The objective is to tune the stabilizers in a way that effectively shifts the lightly damped and undamped electromechanical modes of all plants to a specified region in the s-plane. To achieve this, a multiobjective problem is formulated, optimizing a composite set of objective functions that include the damping factor of the lightly damped electromechanical modes. The dynamic behavior of the system is assessed using small signal stability analysis, while the stability of the system is evaluated by monitoring the variations of eigenvalues under line-to-ground (LG) fault conditions. The proposed method is then demonstrated on a 3-machine 9-bus system, and the effectiveness and robustness of the suggested Power System Stabilizers (PSSs) are showcased by incorporating them into a conventional AVR-PSS model. This allows the proposed approach to efficiently damp low-frequency oscillations in the power system.

Keywords: Transportation systems, Renewable Energy, Fault detection and Diagnostics
Abstract: With the increasing need for sustainable energy sources, wind power has gained prominence as a clean and renewable option. Nevertheless, incorporating wind power into the electrical grid at a significant scale can have a considerable impact on the dynamic behavior of the power system, leading to heightened operational uncertainties. The expanding requirement for power network connection to wind farms has led to the occurrence of rotor angle fluctuations and frequency instability. This paper utilizes a Static Synchronous Compensator (STATCOM) along with a multi-band power system stabilizer (MB-PSS) to assess the transient stability of a power grid. To demonstrate the system's efficiency under fault conditions, such as triple line-to-ground faults (LLLG), a nine-bus, three-machine test network incorporating a Doubly Fed Induction Generator (DFIG) has been employed. The system's performance has been evaluated through MATLAB/Simulink simulations, which reveal that the combination of STATCOM with MB-PSS produces superior results in enhancing transient stability when compared to the scenarios of using STATCOM alone or not using it at all.

Keywords: Renewable Energy, Control applications, Modeling and simulation
Abstract: To increase the efficiency of power generation of Grid-Tied Photovoltaic (GTPV) system, a Maximum Power Point Tracking (MPPT) technique is necessary. The MPPT technique enables the PV array to produce the maximum amount of power regardless of operating conditions. This study proposes a Marine Predators Algorithm based Model Predictive Controller (MPA-MPC) to achieve the Maximum Power Point (MPP). The MPC technique enhances the tracking speed and dynamic performance of the PV array connected to a smart grid. Furthermore, an Energy Storage (ES) system based MPC is introduced to smooth the fluctuations in the output power of the PV array. The MPCs match the measured current signals in both PV and ES systems to their reference values. Moreover, integrating PV with ES systems can achieve power smoothing and enhance the voltage stability of the utility grid. The GTPV system was modeled in the Matlab/Simulink environment using real irradiance and temperature data of a typical day in all four seasons. The simulation results showed that the highest efficiency of 97% was achieved for delivering maximum power to the utility grid based on the provided solar irradiance and temperature levels of the typical days.

Keywords: Renewable Energy, Control applications
Abstract: The work presented in this paper focuses on the control and ‎supervision of a hybrid renewable energy production unit ‎comprising: a wind farm (WF), a fuel cell (FC) and a super-‎capacitor (SC). In order to supply DC loads, the various ‎elements of this unit are integrated with the help of static ‎converters on a common DC bus using an appropriate control ‎system. The control strategy of the hybrid generation unit is ‎decomposed into two parts: a local control based on the ‎control of several electrical quantities through PI control ‎loops and a global control based on the supervision of the ‎power exchange in order to guarantee the power ‎requirements of the DC load. According to the wind speed ‎variation and the power of the load, the global control ‎provides the reference powers of the FC and the SC. In ‎addition, the proposed strategy adjust the wind farm output ‎and the load power demand. The obtained results prove the ‎effectiveness of the control strategy adopted in the proposed ‎hybrid generation unit.‎

Keywords: Renewable Energy, Modeling and simulation, Power systems
Abstract: The operation of renewable energy conversion systems in rural areas is a promising alternative to the exploitation of fossil fuels, thanks to the advantages they present, mainly through their availability and their cleanliness. But the main problem with renewable energies is their intermittent and random production insufficient to ensure continuity of service. Hybrid energy systems find their importance in overcoming intermittency, uncertainty, and low availability of each renewable energy source, which makes these systems more reliable. Indeed, the photovoltaic and wind generators are practically complementary, because sunny days often have a light wind, but cloudy days and nights probably have strong winds. The hybrid way among other wind / photovoltaic with conventional sources (diesel generators, etc.) with storage offers several advantages such as the reliability and continuity of service in remote and rural areas With this in mind, we present in this article, the strategies for optimal management of energy flows provided by an energy system of multi-source types composed of renewable sources (solar and wind) and storage elements (batteries) which is associated with a Diesel Generator (DG). Simulation results that have been tested and validated using MATLAB/SIMULINK are provided to show the continuity of service and the efficiency of the proposed systems regardless of the metrological variation.

Keywords: Observer design, Fault detection and Diagnostics, Renewable Energy
Abstract: This paper highlights the importance of using a Doubly-Fed Induction Generators (DFIG) in the wind industry due to their ability to adapting for all variations in wind speed, thus providing increased efficiency and reliability. However, like any machine, DFIG are not immune to dysfunctional problems and faults (sensor faults, actuator faults and system faults) which affect energy production. To remedy this problem, we develop a Fault Detection and Insolation (FDI) system for sensors fault diagnosis in wind turbine. This work specifically addresses the use of observer’s bench to detect and locate faults, such as intermittent sensor faults, inter-coil short circuits, emphasizing a multi-model approach. We use the Dedicated Observer Structure (DOS) and the Generalized Observer Structure (GOS) to solve the complex challenge of multiple and simultaneous sensor fault. Simulation results are presented to assess the effectiveness of the proposed diagnostic methods.

Keywords: Observer design, Linear and nonlinear systems, Robust control and Hinfty control
Abstract: This article concerns the design of an observer- based robust control system for a class of nonlinear (NL) systems with uncertainties and disturbances including the input quantization. It addresses nonlinearities by employing simultaneously the quadratically inner-bounded conditions and the one-sided Lipschitz (OSL) technique. Additionally, auxiliary variables that facilitate the derivation of less conservative design conditions in the form of Linear Matrix Inequality (LMI) constraints are added. These LMIs enable the simultaneous computation of observer and controller gains. To demonstrate the effectiveness of this control approach, a numerical case study featuring a nonlinear DC motor system is presented.

Keywords: Fractional order systems, Observer design, Linear and nonlinear systems
Abstract: Numerous research studies have focused on addressing the observer synthesis query in systems with integer-order dynamics. However, there has been comparatively less emphasis on investigating the wider class of Fractional systems, characterized by a derivative order which belongs to the interval [0;1]. Furthermore, only very few research works have focused on addressing the observer design issue using the concept of conformable fractional derivatives. This paper extends a specific type of observers, originally designed for systems with integer-order nonlinear dynamics, including Lipschitz and One-Sided Lipschitz (OSL) nonlinearities, to the realm of FO calculus, employing the conformable derivative definition. The efficacy of the proposed findings is demonstrated by means of simulations in a numerical example.

Keywords: Embedded Systems, Industrial control
Abstract: This study introduces an innovative approach to automatically identify and tally faulty components within an Industry 4.0 manufacturing environment. The method involves integrating an affordable image sensor with a Raspberry Pi kit that operates AI-driven software. By examining images or video streams from the production line, this proposed solution swiftly identifies and categorizes defective items, regardless of their orientation to the camera. This automated process not only reduces human error but also seamlessly integrates with existing systems, enhancing workflow efficiency. This solution transforms the defect counting and recognition process in Industry 4.0 production lines, leading to elevated quality control standards and enhanced operational productivity. The achieved accuracy of our work is 95%, and our training dataset encompasses over 500 images.

Keywords: Embedded Systems, Intelligent and AI based control, Real time systems
Abstract: The Internet of Things (IoT) has changed the way we gather medical data in real time. But, it also brings worries about keeping this data safe and private. Ensuring a secure system for IoT is crucial. At the same time, a new technology is emerging that can help the IoT industry a lot. It’s called Blockchain technology. It keeps data secure, transparent, and unchangeable. It’s like a ledger for tracking lots of connected devices and making them work together. To make IoT even safer, we can use facial recognition with Convolutional Neural Networks (CNN). This paper introduces a healthcare system that combines Blockchain and artificial intelligence in IoT. An implementation of Raspberry Pi E-Health system is presented and evaluated in terms of function’s cost.Our system present low cost functions.

Keywords: Embedded Systems
Abstract: This paper presents a novel idea to create a secure platform for sharing data collected by drones. The main goal is to use blockchain technology to record and store various types of data, such as geospatial, meteorological, and environmental information. The platform's purpose is to offer a secure and transparent way for different stakeholders, including researchers, urban planners, farmers, and government agencies, to access and utilize this data. The process starts with drones collecting data during their missions, which is then encrypted and stored on the blockchain. By leveraging the decentralized nature of blockchain, the data is distributed across multiple network nodes to ensure its integrity and availability. Another important aspect of this proposal involves managing the real-time 3D locations of drones to optimize 5G coverage, especially in densely populated areas.

Keywords: Robotics, Control algorithms implementation, Modeling and simulation
Abstract: This paper presents the evolved state feedback control applied to manipulator robots with parameter uncertainties and external disturbances.A high gain control, based on the state feedback, is used in order to guarantee a high performances such as precision and stability of the system despite of the presence of the parameter uncertainties and disturbances. Furthermore, this control provide a smooth signal control that is favorable for the robot actuators. The stability of the control is proved using the Lyapunov approach.Some simulations results present the application of this control to a two degree of freedom manipulator robot and prove its effectiveness.

Keywords: Robotics, Control applications, Discrete-continuous time design
Abstract: In this study, the design of a Lyapunov-based trajectory tracking controller is presented for an underactuated quadrotor unmanned aerial vehicle (QUAV). First, the mathematical dynamics of QUAV are introduced, and then an approximate model is derived using the Euler approximation. Afterward, positive semi-definite Lyapunov functions are introduced for both attitude and position subsystems considering the underactuated structure of the QUAV system. Based on positive semi-definite functions, discrete-time nonlinear controllers are devised. These designs offer an approach that addresses the complex dynamics of the QUAV by ensuring the asymptotic stability of each subsystem while aiming to achieve the desired performance for the closed-loop system. Finally, simulation works are carried out to show the effectiveness of the proposed discrete-time controller.

Keywords: Optimal control, Robotics
Abstract: In this paper, we consider a two-wheel Dubins' car with only one motor. This motor drive both wheels in forward direction but only one wheel, the freewheel, in backward direction. We calculate the time optimal synthesis.

Keywords: Modeling of complex systems, Robotics
Abstract: This paper discusses, a 3RRR plane parallel robot modeling developed with a kinematic model and Jacobian matrix. An illustration is proposed which determines the workspace and the different working modes of the 3RRR parallel robot through the Jacobian matrix, to find the different singular points of the 3RRR robot. The simulation results are described to illustrate the efficacy of the proposed technique.

Keywords: Modeling of complex systems, Robotics
Abstract: This article presents a solution based on a soft-continuum arm for depositing material in predictive maintenance of complex structures susceptible to deformation-induced micro-cracks. The soft-continuum robots are chosen for their agility in navigating confined spaces and accessing hard-to-reach areas, making them ideal for precise material injection into cracks. The soft-continuum arm is attached to an Unmanned Aerial Vehicle (UAV) equipped with a camera, enabling the localisation of cracks on wall surfaces and material deposition for on-site repairs, offering a swift and cost-effective intervention without the need for scaffolding. To ensure precise material deposition, maintaining the soft-continuum arm's bending speed and trajectory tracking is essential. This article primarily focuses on modelling the soft arm, assuming a stationary drone and minimal force during material deposition. The modelling approach draws inspiration from concepts used in parallel rigid manipulators and incorporates effort-based deformation into a kinematic model. To evaluate the proposed approach's efficiency, a series of experiments using a flexible section of the CBHA is conducted, complemented by a study of deformation as a function of pressure using Finite Element Method (FEM) analysis through Ansys software. A series of experiments using a section flexible of the CBHA is conducted to evaluate the efficiency of the proposed approach.

Keywords: Multivariable control, Robotics
Abstract: This paper deals with the development of control law to achieve the consensus tracking on a multilateral teleoperation system (MTS) that is proposed is to achieve a complex goal which is hard to achieve by a single manipulator with communication time delay. In MTS, each robot shares its information to the other robots to complete the task. In order to ensure fast convergence rate (FCR), singularity avoidance, and robustness against uncertainties and external disturbances (ED), a novel adaptive non-singular fast terminal sliding mode control (ANFTSMC) on multilateral teleoperation is applied to master-slave-followers manipulators with an unidentified upper limit on the system uncertainty (SU) and varying-time delay (VTD) external disturbances. The controller exhibits a finite-time convergence and is robust in the presence of the external force and (VTD). The Lyapunov stability theory is employed for stability analysis, and simulation studies are carried out to confirm the efficacy of the control schemes that have been developed.

Keywords: Control algorithms implementation, Control applications, Optimal control
Abstract: Designing an effective cancer therapy has long been of practical interest in bio-science and engineering. Thanks to today's technology, a handful of techniques are available and effective for treating cancer diseases, including chemotherapy. The main limitation of such treatments comes from the tumor heterogeneity resulting in the inefficiency of the treatment after a year or two. Hence, it is common to provide overdosed treatment in practice, which enhances the side effects and even builds up drug resistance. To tackle this issue, a representative mathematical model is developed from clinical research observations to simulate the dynamic behavior of the cancerous cell population under tumor heterogeneity. This model, in which tumor heterogeneity is characterized by the cells' drug-resistance levels, has been widely used in the areas of systems science and engineering. Our previous research has studied the resulting optimal control problem for multiple significant traits, however, it remains a challenge to discuss the drug's impact on the evolution of the cancerous cell population through the standard optimal control formulation. In other words, an optimal cell-distribution control problem is in great demand to minimize the combination of tumor volumes and drugs' side effects, which guarantees the effectiveness of the designed treatment. In this paper, we extend our previous work and combine it with the Liouville approach for distribution controls to describe the evolution of the cancerous cell population quantitatively. Several numerical examples are included to demonstrate the performance of this technique and discuss the optimal cancer chemotherapeutic treatment scenarios when a single treatment is available.

Keywords: Control algorithms implementation, Control applications, Optimal control
Abstract: In this article, model predictive control was parameterized in terms of orthogonal Legendre and Chebyshev polynomials to minimize the set of free variables of constrained optimization problem that has to be solved at every time step. In this study, these orthogonal polynomials were used to approximate the control signal to represent them by a lower dimensional set of variables; these variables are the unknown coefficients of the truncated series of orthogonal polynomials. It was shown that constrained model predictive control and the associated constrained optimization problem can be recast in terms of these unknown coefficients of the truncated series of orthogonal polynomials. This new constrained optimization problem can be solved in a fraction of the time required for the original constrained model predictive control. This new constrained model predictive control was tested on a four-tank bench-marking problem to demonstrate its performance.

Keywords: Control applications
Abstract: The approach used in this paper is particularly useful for designing controllers for linear systems with uncertainties. Many practical systems can be approximated as LTI systems, making this approach widely applicable. The use of a linear parametric uncertainty model allows for uncertainties in the system's dynamics should be subject to consideration while the controller design process. The recursive least squares algorithm is a common method for identifying such models, and it works by iteratively refining the estimates of the model parameters based on measured input and output data. The controller design process gives priority to both desired performance and uncertainties in the model parameters. The 𝐇∞ controller synthesis approach is used to design the controller, which is a popular approach in robust control due to its ability to handle uncertainty and disturbances. By using this approach, the designed controller is able to achieve desired step response characteristics while maintaining closed-loop stability and robust performance even in the presence of uncertain parameters, disturbances, or measurement noise.

Keywords: Control applications, Control algorithms implementation, Industrial control
Abstract: Model predictive control is performed by solving a constrained optimization problem at every time step. Since this optimization problem is a function of the control signal in the prediction horizon, long prediction horizon for large-scale plant model is not practical for model predictive control applications. However, it can be shown that when the control signal sampled by a coarse time step is represented with linear or cubic splines, these splines can be used to interpolate control signal series to calculate the missing data values. Hence, it can be shown that control signal is a function of the coefficients of splines. Using this fact, it is shown that the quadratic optimization defined in terms of control signal can be replaced by a new optimization problem defined in terms of the coefficients of splines. Thereby, it is shown that the size optimization problem can be reduced and model predictive control can be accelerated in order of magnitudes.

Keywords: Control applications, Control algorithms implementation, Optimal control
Abstract: In this paper, classical model predictive algorithm was revisited and it was reformulated in terms of lse-splines to accelerate the algorithm. To this end, model predictive control law and the underlying constrained optimization problem were recast in terms of the coefficients of a compact set of lse-splines. Since lse-splines are not required to pass all discrete-time values of control signal, it was shown that the compact set of lse-splines is minimal. Thereby, time-complexity of model predictive control and size of the underlying constrained optimization problem were minimized. It was shown that solution of the constrained optimization problem can be accelerated and real-time application of model predictive control is possible. Four-tank bench-marking problem was used to investigate performance of the model predictive control.

Keywords: Autonomous Ssystems, Linear and nonlinear systems, Robust control and Hinfty control
Abstract: This work is concerned with the leader-following control design for linear multi-agent systems with norm bounded uncertainties as well as constrained inputs. The purpose is to design a distributed control algorithm for the followers to track their leader’s states without having access to the latter’s inputs, in addition to respecting the predefined bounds of the input saturation constraints. For the control design, the H∞ criterion is used to minimize the tracking error of the agents. Knowing the initial state of the followers, sufficient LMI-based conditions are proposed to solve the leader tracking problem and restrict the amplitudes of the followers inputs in predefined areas. The efficiency of this method is illustrated through the simulation of a group of two mass-spring systems.

Keywords: Autonomous Systems
Abstract: This paper explores the entity relationship diagram, a popular conceptual model used to depict entities, attributes, and relationships graphically. To help with this, we use ChatGPT, a sophisticated language model based on the GPT architecture, which can translate natural language text into an entity relationship diagram. The paper details the process of evaluating how well ChatGPT can perform compared to other state-of-the-art approaches for entity and relationship extraction. Our experimental findings demonstrate the strong ability of ChatGPT to translate natural language text into entity relationship diagrams, which has potential applications for knowledge graph building, data integration, and database schema design. Moreover, it can aid in automating the extraction and organization of information from unstructured text data, thereby simplifying the study of complex systems.

Keywords: Autonomous Ssystems, Mechatronics, Robotics
Abstract: The control performance of the model-based control depends on the accuracy of model identification. However, accurate model identification is difficult in quad-rotor drone control due to nonlinearities and complex dynamic characteristics of aerodynamics, actuators, and sensors. In contrast, in the data-driven approach, since controllers are designed directly from input-output data without using model identification, even nonlinear multivariable control systems are designed easily. Because of this usefulness, the application of data-driven design to quad-rotor drones has attracted much attention in recent years. The attitude of a quad-rotor drone consists of three angles: roll, pitch, and yaw, which interfere with each other. Therefore, if three single-input, single-output systems are designed to ignore the interference, adequate performance cannot be obtained. Although a data drive scheme for a two-input, two-output system that includes roll and pitch angle interference has been proposed, it does not include the yaw axis. Therefore, this study proposes a data-driven design that includes interference at all attitude angles and also verifies its effectiveness through simulations.

Keywords: Autonomous Ssystems, Motion control, Transportation systems
Abstract: This paper presents an autonomous car parking system. First, a path planning algorithm is designed to generate the required trajectory to park the vehicle in narrow spaces while avoiding the collision with other parked vehicles. Nonholonomic constraints of the vehicle are taken into account while designing smooth parking trajectories. Various parking situations like parallel, forward and backward parking are considered. A Linear Quadratic Regulator (LQR) controller is then designed to track the obtained path precisely. The designed algorithms are implemented in MATLAB/Simulink and effectiveness of the algorithm is shown through simulation results.

Keywords: Autonomous Ssystems, Signal processing, Estimations and identification
Abstract: The emergence and widespread use of unmanned aerial vehicles (UAVs), commonly known as drones, have brought about numerous benefits and opportunities across various industries. However, this technological advancement has also introduced new security challenges, with the threat of rogue drones posing significant risks to public safety. This paper presents a comprehensive view of the classifications and detection methods associated with rogue drones. We explore various categories of rogue drones, considering factors such as their capabilities, sizes, and purposes, which influence the potential risks they pose. Next, we review state-of-the-art detection techniques, ranging from radar-based systems to advanced sensor technologies and computer vision solutions. It is evident that drone detection is a complex task due to high resemblance between drones and the background objects such as birds. The performance of detection solutions usually depends on the class of attacking drones and the detection technology being implemented. This paper also highlights the methods of performance evaluation. Finally, we conclude by exploring the potential future developments and research directions for enhanced rogue drone detection.

Keywords: Autonomous Systems, Control applications, Optimization
Abstract: Hexapod robots are highly stable due to their six legs and are commonly used in challenging terrain. Precise control of the joint angles of the legs is crucial for smooth locomotion and stability. In this study, we propose a Proportional-Integral-Derivative (PID) controller to regulate the positions of the joint angles. The PID controller adjusts the control output based on the error between the desired setpoint and the actual system state. To optimize the controller's performance, we employed a Genetic Algorithm, which improves the parameters of the controller through natural selection. We simulated the proposed method using SIMMECHANICS in MATLAB and demonstrated highly accurate tracking of the desired trajectory of the leg angles and significant stability of the robot during movement. The results indicate the proposed approach's effectiveness in regulating the hexapod robot's leg angles. The utilization of an optimized PID controller through the Genetic Algorithm in this study enhances the advancement of hexapod robots. By employing this method, we achieve automatic optimization, which significantly reduces the time and effort required to determine the best PID parameters. Consequently, this approach enhances the stability, control, and overall performance of the robot across diverse locomotion tasks.

Keywords: Renewable Energy
Abstract: Generating the optimum power from the PV array is one of the most challenging issues with photovoltaic (PV) systems, mainly when operating in partial shade conditions (PSCs). The present research illustrates a particle swarm optimization (PSO)--based max power point tracking (MPPT) approach to overcome this problem. In addition to avoiding the common drawbacks of conventional MPPT techniques (such as perturbing and observing, or P&O) and incremental conductance, the developed MPPT technique also provides a robust and straightforward MPPT scheme to handle partial shading in PV systems since it only requires two control parameters and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The photovoltaic system under consideration and the MPPT approach employed are modeled and simulated in MATLAB Simulink. The simulation results proved the effectiveness of the proposed heuristic (PSO)strategy.

Keywords: Renewable Energy, Modeling and simulation, Control algorithms implementation
Abstract: Maximum power point tracking (MPPT) presents an important method for ensuring the optimal operation of photovoltaic systems (PVS). In this paper, we present an MPPT method based on combining the MPPT method: Perturb and Observe (P&O) with the sliding mode controller which is an external voltage controller. To achieve high-performance control, particularly in terms of accuracy and rapid response, we propose the integration of derivative and integral terms into the sliding surface. In general, the Sliding Mode Control (SMC) consists of two parts: the equivalent and discontinuous control laws. However, the discontinuity related to the discontinuous control law often leads to a chattering phenomenon. To solve this problem, we replace the sign function in the conventional SMC method with its sigmoidal approximation. The proposed MPPT method is evaluated under conditions of rapid irradiation changes using the MATLAB/Simulink environment. Furthermore, the proposed method is compared to the P&O MPPT algorithm with a proportional-integral (PI) controller and to the P&O MPPT algorithm with conventional SMC. The simulation results of the suggested MPPT approach demonstrate high dynamic performance, particularly in the case of fast variations in irradiance

Keywords: Renewable Energy, Optimization, Modeling and simulation
Abstract: This paper presents a novel approach to optimize energy exchanges in local energy communities using a private blockchain environment. The proposed system leverages the benefits of blockchain technology, such as decentralization, immutability, and transparency, to address the challenges faced by traditional energy management systems. It specifically focuses on optimizing energy exchanges while respecting individual preferences and objectives. Comprehensive guidelines are provided for the development of blockchain-based applications that facilitate effective energy optimization. Simulations conducted on real data demonstrate that consensus is achieved after a few iterations. A metric introduced in this context is "Gas spent" quantifying the computational efforts essential for blockchain operations and evaluating the efficiency and resource used.

Keywords: Renewable Energy, Power systems, Modeling and simulation
Abstract: The use of wind to power pumping for irrigation of agricultural crops in the region of Tozeur, Tunisia is studied. This study is motivated by the availability of wind energy in this area and the interest in sustainable agricultural products. The work presented here focuses on the design and evaluation of an underground pumping solution by wind energy for a small farm growing tomatoes and strawberries. Using data from the location the system is also evaluated to confirm the adequate performance.

Keywords: Renewable Energy, Power systems, Fault detection and Diagnostics
Abstract: Online monitoring and prediction play a crucial role in ensuring the optimal performance of grid-connected photovoltaic (PV) stations. On the other hand, using machine learning techniques, specifically tree-based methods, has demonstrated its effectiveness in predicting and detecting faults in nonlinear processes. In this study, we employed a tree regression method for the online monitoring and prediction of a 7 kWp PV station located in the desert region of Adrar. Our approach exhibited clear superiority when compared to classical regression methods such as the Linear Least Squares Method (LLS). We achieved a determination coefficient (R²) of 0.9671 and a Mean Absolute Error (MAE) of 187.2, surpassing the results obtained with LLS, which yielded an R² of 0.9646 and an MAE of 252.06, highlighting the efficacy of our proposed methodology

Keywords: Renewable Energy, Power systems
Abstract: In this paper a detailed evaluation analysis of a photovoltaic plant with 2.5 Kwp capacity was performed, this grid tied photovoltaic plant is located and installed on the roof top of the Research Unit on Renewable Energy (URER-MS) in Adrar region in southern Algeria (Latitude 27.88°N, Longitude -0.27 °E, Altitude 262 m) in year 2018/2019. The Adrar region is characterized by a very high average temperature, low humidity rate and high potential for solar radiation. This analysis was carried out by carrying out a precise evaluation of the various impacts of the environmental parameters on the operating performance of the grid-connected photovoltaic installation. During 12 months of the year 2019 with the annual average temperature was 32 °C, the grid connected photovoltaic plant was supplied a total power of 4456 kWh. The experimental results indicated that for different months an important variation have been observed in performance parameters. The maximum/minimum of monthly average daily values of final, reference, and array yields are; 5.55-4.08 kW h/kWp/day, 5.68-5.2 kW h/kWp/day, and 5.07/3.75 kW h/kWp/day, respectively. The overall system, PV module and inverter efficiency reached; 12.80-11.87%, 15.08-11.78%, and 97.5-/98.01%, respectively. The annual performance ratio (PR) is from 65.4% to 82.74%.

Keywords: Estimations and identification, Fractional order systems, Linear and nonlinear systems
Abstract: The presented work treats the identification problem for a fractional nonlinear system, especially the fractional-order Hammerstein system starting from measurement data. Using the hierarchical principle, a Kalman-Filter-Based-Least-Squares algorithm is developed to determine the system states and the system parameters. The simulation example prove the efficacy of the algorithm.

Keywords: Estimations and identification, Modeling and simulation, Optimization
Abstract: This paper is dedicated to simulation in the smartphone environment. More precisely, this work is on the design of an improved identification algorithm based on the Feasible Generalized Least-Squares method. The goal is optimizing the identification algorithm to render it suitable for constrained environment in term of necessary computations. Several combinations of the proposed algorithm are considered and analyzed. Afterwards, the proposed approach is compared with other standard literature methods to highlight the performance of the established algorithm. Based on the comparison results and further studies, a generalized form of the proposed algorithm is introduced.

Keywords: Estimations and identification, Observer design, Optimization
Abstract: This paper proposes a interval filter, for linear timeinvariant systems. The goal is to provide upper and lower bounds of a linear combination of the system states, in a guaranteed manner, so that the influence of unknown inputs on the envelop size, is minimised in the H1 and/or H∞-norm sense. A key feature of the proposed interval filter is that it consists of a general statespace realization, so the filter can be of higher order than the system’s model. This is also a major difference compared to current existing interval approaches that are based on observer structures. Using the Lyapunov theory, it is shown that the design of the filter state space matrices can be formulated as solving a nonlinear semi-definite programming (NL-SDP) problem.The feasibility of the proposed approach is demonstrated on an academic example.

Keywords: Estimations and identification, Optimal control
Abstract: In this paper, an adaptive system identification scheme is proposed that retrieves the unknown nonlinear open-loop plant dynamics even though it might be unstable. More specifically, an iterative process is adopted, where a neural network is fitted over a series of trajectories that cover the desired subset of the state space. The main novelty lies in the way the reference trajectories are generated so that they sufficiently excite the underlying system dynamics. Subsequently, the prescribed performance control (PPC) technique is employed to ensure the accurate tracking of the aforementioned trajectories. The simulation results demonstrate the success of the identification process and the effectiveness of our approach.

Keywords: Estimations and identification
Abstract: This paper focuses on identifying linear systems parameterized by an Output Error (OE) model from input/output data while considering the presence of unknown but bounded noise. The proposed algorithm is an Ellipsoidal Outer Bounding (EOB) algorithm integrated in an iterative scheme. It aims to constraint the magnitude of the output error under a bound depending on the noise level and to compute the minimal volume ellipsoid that encloses the real parameter vector. The algorithm is analyzed and some numerical simulations are conducted. Results confirm the analysis and the interest of the proposed algorithm.

Keywords: Estimations and identification
Abstract: The purpose of the study is to investigate the predictors associated with breast cancer recurrence. From 2004 to 2006, the Benghazi Medical Center's Department of Oncology (DoO) provided the data set. Eight factors—social and clinical—were taken into account in this investigation. The Cox proportional hazard model was fitted, survival curves were generated using the Kaplan-Meier method, and hazard ratios were obtained with a 95% confidence interval. A total of 107 patients (49.1%) and 111 patients (50.9%) had censored observations (no recurrence occurred). The study concluded that patients who have received hormonal therapy have improved their survival time. In contrast, chemotherapy increases the risk of tumor return. These results have significant clinical implications and can assist medical professionals in making informed decisions about treatment options for breast cancer patients, ultimately improving patient outcomes and survival rates.

Keywords: Control of telecommunications systems, Intelligent and AI based control, Real time systems
Abstract: This paper presents the design and the realization of a wireless controlled smart home that offers its user more comfort, energy efficiency and better security. This project consists of an electrical part that contains the different sensors, actuators and the programming card that ensures the connection of the house to the internet and an Android application that must be installed on the user’s mobile phone. With this intelligent system, the user can control the parameters of their home remotely and in real time, to configure the instructions (max temperature, max humidity, etc...) according to his needs, to control the state of the various installed actuators (fan, door (servo motor), alarm, lamps etc.), to receive a notification during a gas leak or attempted intrusion. The user can still manually command the actuators remotely through the mobile application via the internet.

Keywords: Control of telecommunications systems, Manufacturing systems, Embedded Systems
Abstract: A compact and low profile printed monopole antenna for applications in wireless communication systems (WCS) is presented in this paper. The antenna design is based on a rectangular patch along with a V-shaped staircase slot on the top side of the substrate and a partial ground plane on the bottom side. This design technique allows obtaining a wideband operation with bandwidth extended from 2 GHz to more than 8 GHz. The investigated antenna shows a monopole radiation pattern in the H-plane and a bisectional radiation pattern in the E-plane. The measurement and simulation results are in a quite good agreement, which indicates that the antenna is appropriate for WCS.

Keywords: Control of telecommunications systems, Networks optimization, Renewable Energy
Abstract: The importance of energy efficiency in wireless sensor networks (WSNs) is well understood. It is necessary to increase energy efficiency while also improving WSN performance, particularly latency. A well-liked protocol design idea is the sacrifice of different performance metrics for energy, such as throughput. In this article, we propose an Energy-efficient Asynchronous MAC protocol for WNS with energy harvesters (Harvested Energy Efficient MAC) based in the harvested energy. By transmitting Beacon messages, HEE-MAC takes use of receiver-initiated schemes where each sensor node in the WSN to determinate the length of its subsequent sleep to optimize its duty-cycle by making use of its remaining energy, which may rise over time, in addition using the energy threshold. Furthermore, HEE-MAC enables WSN to perform well in low energy harvesting scenarios. We performed simulations using OMNeT++/MiXiM and compared this to the current energy harvesting management technique, such as EH2M, in order to demonstrate that our solution, HEE-MAC, will truly increase the WSN performances. The outcomes of the simulation show that HEE-MAC provides low latency and saves more energy than EH2M.

Keywords: Control of telecommunications systems, Modeling and simulation
Abstract: In this communication, an improved MIMO antenna array with defected ground structure (DGS) is designed for 5G wireless applications. Its schematic comprises eight radiator elements, situated at the center of an FR-4 printed circuit board. For optimizing the mutual coupling features, the metamaterial rectangular slots are employed between adjacent elements. Extensive simulation results prove that our proposed design 4x4 MIMO array operated at 5.8 GHz offers significantly better performance in terms of antenna gain and envelope correlation coefficient (ECC) with 6 dB and less than 0.0009 respectively. Rendering to the reached outcomes, the proposed MIMO antenna is an efficient application-oriented design for 5G communication.

Keywords: Control of telecommunications systems
Abstract: In recent years, there has been growing interest in the study of Trellis Coded Modulation (TCM) for wireless communication systems. TCM is a powerful error-correction technique that can improve a communication system's Bit Error Rate (BER) performance. In particular, combining TCM with Multiple Input Multiple Output (MIMO) systems has shown great promise in improving BER performance in wireless communications. This paper presents a comparison study on the Bit Error Rate (BER) performance of 8-Phase Shift Keying (8-PSK) and 8-Quadrature Amplitude Modulation (8-QAM) TCM coded in a 2x2 Orthogonal Space-Time Block Code (OSTBC)-Multiple Input Multiple Output (MIMO) system over Additive White Gaussian Noise (AWGN) and fading channels. The study uses extensive simulations to compare the BER performance of the two modulation schemes under different channel conditions. The results of the study show that TCM improves the overall system performance regardless of the channel type. Additionally, 8-PSK TCM performed slightly better in AWGN channels, while 8-QAM TCM performed better in fading channels. The study also finds that the 2x2 OSTBC-MIMO system is viable for improving BER performance in wireless communications.

Keywords: Control of telecommunications systems
Abstract: M-ary quadrature amplitude modulation (M-QAM) is one of the High-level modulation, which is receiving a lot of attention in latest conceptual investigations of communication systems, and the majority of these researches are simulation-based. QAM has emerged as a prominent modulation scheme due to its ability to achieve high data rate and spectral efficiency. Among the various QAM schemes, 32-QAM stands out as a particularly attractive option, offering a balance between complexity and performance. In this study, a comprehensive simulation-based investigation of 32-QAM modulation schemes is presented by applying Matlab/Simulink, and BERTool is also utilized in conjunction with the model. For 32-QAMs, the phase noise's impact on the constellation diagram is investigated. The study is also done on the effects of modifying the input signal's strength, phase noise, and frequency offset on the way 32-QAM performs in terms of Bit Error Rate (BER). The 32-QAM modulation process is simulated over Additive White Gaussian Noise (AWGN) channel, this model offers straightforward techniques in order to acquire useful insights into a system's fundamental behavior before the aforementioned aspects are taken into consideration. The simulation results are shown and discussed in terms of the constellation diagram and BER curve under various scenarios.

Keywords: Modeling and simulation, Optimization, Renewable Energy
Abstract: In this article, optical and electrical properties of ultra-thin Ag/SnO2 structure were experimentally studied for indium-free transparent conductive oxide (In-free TCO) applications. The DC sputtering technique was used for the development of different thin-films. The impact of the silver (Ag) ultrathin top film on the TCO multilayer film, transmittance, optical properties and resistivity of the deposited tin-dioxide thin-film is analyzed by performed optoelectronic characterizations using the appropriate experimental techniques such as UV–Visible spectroscopy, XRD, and Hall effect measurements. The proposed bilayer structure shows a good transparency of 62% over the visible spectral range. Through experimental analysis, it is found that the recorded optoelectronic haacke's Figure-of-Merit (FoM=Transmittance at (550nm) / sheet resistance) of the proposed Ag/SnO2 TCO layer is 6.7×10-4 Ω-1, while the FoM of the conventional ultrathin SnO2 is 2×10-6 Ω-1, with an increasing of more than 95% at almost the same layer thickness (50 nm). Consequently, this contribution can provide new insights to boost the optoelectronic properties of transparent electrodes for high-performance and eco-friendly solar cells for photovoltaic applications.

Keywords: Modeling and simulation, Estimations and identification
Abstract: Batteries raise issues in electric vehicles because of their limited range and lifespan. They can however be extended by implementing smart, accurate and computationally inexpensive control algorithms. Herein, a lithium-ion battery cell is modelled with an equivalent circuit model which is complex enough to track the output voltage of the cell while remaining simple enough to be used in a control algorithm. This work presents the identification procedure of equivalent circuit model parameters for a lithium-ion battery cell by applying high currents to the cell. Consequently, high accuracy was achieved for the whole range of possible currents. The parameters were identified on a dataset with high currents, while the model was evaluated on a different dataset acquired from a real electric vehicle usage data. Metrics from both the identification and the validation datasets are similar meaning that the model generalises well.

Keywords: Modeling and simulation, Modeling of complex systems
Abstract: The heat exchanger/reactor(HEX Reactor) focused in this paper is a device with well-characterized performance on both heat exchanging and chemical reaction parts. A detailed nominal model for this HEX Reactor was established before for the use of further researches in the diagnostic section. However, the detailed model is too complex to bring with difficulties in the design of controllers and the amount of calculations. In this paper, a new simplified interconnected model is developed for the HEX Reactor. The new methodology tries to make full use of the sensors. The interconnected cells in the model are constructed in different sizes according to the locations of the thermocouples. In this way, the total amount of subsystems is evidentially decreased and the nominal model is therefore simplified. A series of experiments in the heat exchange part have been carried out. The experiment data is then compared with that of the simulations on the simplified model. Results show a very high precision of the new model to the physic HEX Reactor.

Keywords: Modeling and simulation, Modeling of complex systems, Optimization
Abstract: The aim of this paper is to propose a new mathematical model able to compute the electro-thermal performance of a Gate All Around (GAA)-FET transistors. The given electrical formulation is given by the Drift-Diffusion (D-D) model in conjunction with an effective electron mobility formulation. To better compute the nano heat conduction, the enhanced D-D model is coupled with the hydrodynamic model. To achieve high evidence to the proposed model, the simulation of the transfer and output characteristics are compared with experimental and numerical data, and we have succeeded to achieve a good concordance. Power dissipation with phonon radiation effect, device temperature has been studied in 50 nm and 10 nm of GAAFET gate lengths. We have shown that, the Joule effect increases with the decreases of the GAAFET gate length. On the other hand, we demonstrate that for the two studied structure, the saturated temperature from 360 K to 405 K when the device gate length increases from 10 nm to 50 nm, respectively.

Keywords: Modeling and simulation, Process control and instrumentation, Industrial control
Abstract: Tank modeling and simulation is an important aspect when designing a level control system in a tank. This paper correction the gravity drained tank model. Interestingly, the gravity drained tank model is modeled without considering the pipe resistance to drain water out of the tank. Furthermore, the simulation using Simulink for non-linear and linearized models of gravity drained tanks. The cross-sectional area of the tank is 9.7 x 10-3 m2, and the cross-sectional area of the pipe to drain water out of the tank is 1.86 x 10-4 m2. Simulates the flow rate of water flowing into the tank for 3 x 10-4 m3/s, 3.33 x 10-4 m3/s, and 3.5 x 10-4 m3/s. The water level in the tank from the simulation results for the non-linear model are 0.13 m, 0.16 m, and 0.18 m, respectively. Meanwhile, the simulation results for the linearized model are 0.26 m, 0.32 m, and 0.38 m, respectively. The water level in the tank based on the linearized model is two times higher than the non-linear model. Experiment to find out the actual process of the water level in the tank, due to the flow rate of the water flowing into the tank. Obtained that the gain of the linearized model must be corrected. The gain correction factor for the linearized model is 0.5.

Keywords: Renewable Energy, Control algorithms implementation
Abstract: The goal of this research is to produce both direct and alternating electricity using an inverter that can simultaneously produce 220V AC and 12V DC. Solar cells were used to power the inverter, which in turn charged the batteries. Proteus software was used to design and create the inverter. Following that, the actual inverter was created and put to the test, and the effectiveness and performance of the hardware and software were compared. While the performance of the charger control unit varied depending on whether it was running on software or hardware, the software efficiency was 80.7% and the hardware efficiency was 70.3%. The maximum load for this inverter is 60 W. This inverter's modeling and design were successful. It can be used in emergencies like a sudden power outage or harsh environments and off-grid locations because it can operate 220 V loads, direct current loads, and mobile phone chargers.

Keywords: Renewable Energy, Power systems, Process control and instrumentation
Abstract: The present paper discusses the dust effect on the photovoltaics solar panels and proposes the waterless automatic cleaning ranges, applicable to improve the panels output performances in the sahelian region. A comparative study is conducted between the short circuit current and maximal power of the both cleaned and not cleaned polycrystalline solar panels, installed at the Advanced School of Engineering , Dakar. Several automatic cleaning ranges are applied on one panel whereas the other one is never cleaned. The every ten second electrical parameters of the both panels ( short-circuit current, open circuit voltage, power) as well as the consumed current by the cleaning brush are collected with CR800 data logger installed on the experimental platform. The obtained results show respectively a drop in performance of 14.22% and 13.69% for the both short circuit current and power, after four weeks exposure without cleaning. The consumed energy by the cleaning brush is evaluated at 0.9 wh per cleaning in the standard test condition; This correspond to 0.018% of the photovoltaic energy production. These results show an automatic cleaning effectiveness of the photovoltaics solar modules against dust deposits in the Sahelian regions.

Keywords: Renewable Energy, Power systems
Abstract: Since photovoltaic (PV) solar energy is one of the most promising renewable energy sources, its production is witnessing an on-going performance improvement. Increasing the efficiency is usually accomplished using maximum power point tracking (MPPT) techniques. The latter require the determination of the derivative (of order one and two) of the power with respect to the voltage in order to perform accurately. This paper suggests incorporating with MPPT methods an algorithm for numerical derivative estimation to enhance the performance of the control strategy in photovoltaic power generation systems. The proposed technique is an online polynomial interpolation adjusted by a Recursive-Least-Square (RLS) algorithm. Without increasing the complexity of the control algorithm, the suggested approach shows promising results in the presence of noise on the measurements and guarantees the correct performance for MPPT technique in both simulation and practice. The global algorithm for maximum power point tracking is validated through Matlab/Simulink and implemented on the experimental test bench of the laboratory. The estimates are compared to the conventional Euler approximation algorithm usually integrated in this application. The efficiency of tracking is highly increased without any additional sensors or calculators.

Keywords: Renewable Energy, Power systems, Modeling and simulation
Abstract: This paper explores the potential of ejector refrigeration systems as sustainable cooling solutions driven by low-grade energy sources like waste heat or solar collectors. The focus is on adjustable ejectors, vital for enhancing efficiency in these systems. Simulations examine their performance within solar air-conditioning setups. In typical ejector refrigeration systems, a generator, condenser, evaporator, ejector, pump, and expansion valve form the core components. The generator initiates the refrigeration process using low-grade energy, with high-pressure vapor entering the ejector nozzle, inducing the release of low-pressure vapor from the evaporator. Mixing and pressure recovery within the ejector are key to optimizing system performance. Using the k–ε turbulence model in CFD simulation, the study conducts 2D steady flow analysis under varying conditions, including generator temperature, back pressure, and evaporator temperature, with R134a as the working fluid. Validation experiments confirm results accuracy, particularly regarding the entrainment ratio, the optimum Er value obtained from the simulation is 1.2, highlighting the significance of generator temperature and back pressure in enhancing coefficient of performance (COP), among other findings

Keywords: Renewable Energy
Abstract: Photovoltaic (PV) emulators (PVEs) become a critical tool to test the PV generation system such as the maximum power point tracking (MPPT) converter and the PV system control. The latter ensures a controllable light source environment that mimics the output characteristics of a real PV panel. In this context, this paper deals with the implementation of different MPPT techniques to a developed low-cost PV emulator based on two‐diode model. Mainly, The PVE structure is formed by three parts that are the PV model, the control strategy, and the power stage. In an initial phase, the PV emulator system is presented, and its three main components are described. In a second phase, three MPPT algorithms, namely, the perturb and observe (P&O) technique, the incremental conductance (IC) technique and the sliding mode control (SMC) technique are studied and applied to the developed PVE. The acquired simulation results show that the developed PVE can follow accurately the output characteristics of the PV module. Moreover, the studied MPPT algorithms allows the PVE to operate at its maximum power during irradiance and temperature variations. As compared to the P&O and the IC techniques, the SMC provides a good response in MPP tracking without considerable ripples.

Keywords: Control applications, Modeling and simulation, Renewable Energy
Abstract: Traditionally, high-voltage AC Transmission system (HVAC) was used for offshore wind farms (OWFs). The key factor in the extension of offshore wind farms is the improvement of reliable and efficient offshore electrical transmission infrastructure. Recently, thanks to the grid-forming capabilities of the Multi-terminal voltage source converter VSC-based HVDC transmission technologies, the HVDC system is exploited for the integration of large-scale offshore wind farms in the grid. This paper presents and studies the system operation of a multiterminal DC transmission system (MTDC) for onshore integration of offshore wind-farms. The main objective of this paper is to evaluate the operation and the control of an MTDC networks based offshore wind-farms with the purpose of having transmission balance and grid stability. Using the Matlab platform,

Keywords: Control applications, Optimization, Robotics
Abstract: Electric wheelchairs play a crucial role in improving the mobility and independence of individuals with limited physical capabilities. However, ensuring accurate control and smooth maneuverability in diverse environments remains a challenge. In this paper, we present effective control architecture for an electric wheelchair, which uses a PID controller optimized by the Particle Swarm Optimization (PSO) metaheuristic algorithm for trajectory tracking. We developed a kinematic model adapted to the electric wheelchair, and simulated various trajectories to evaluate the tracking control. The proposed architecture employs two PID controllers, where one of the PID controllers is dedicated to controlling the linear translation of the wheelchair, while the other is responsible for adjusting the rotation of the wheelchair to achieve the desired positions along the trajectory. The simulation results show that the proposed architecture achieves accurate and efficient trajectory tracking, highlighting the effectiveness of the PSO-optimized PID controller for this application.

Keywords: Control applications, Power systems, Observer design
Abstract: In this paper, we propose a real-time control approach for an induction motor drive used in an electric vehicle. This approach combines Direct Field Oriented Control (DFOC) with Backstepping Control (BSC) techniques and incorporates a Model Reference Adaptive System (MRAS) for precise speed control. Indeed, the BSC is proposed for overcoming the DFOC limitations in terms of robustness under load torque disturbances, parameter variations, as well as fast speed and torque convergence towards their references. Moreover, the MRAS is suggested for real time rotor speed estimation which consequently reduces the system cost and the maintenance rate by eliminating the speed sensors. The performance of the proposed control algorithm, which utilizes sensorless DFOC-BSC with an MRAS estimator, is assessed through numerical simulation conducted in the Matlab/Simulink environment. These results are then compared to those obtained from conventional DFOCbased PI controllers. The findings conclusively demonstrate that the proposed sensorless control method outperforms traditional approaches in terms of convergence accuracy, robustness in the face of sudden load changes, resistance variations in the rotor, and precision of speed estimation during both steady-state and low-speed operations.

Keywords: Control applications, Robust control and Hinfty control, Observer design
Abstract: In this paper, the problem of tracking control is investigated for turbocharged diesel engine. For this, an observer based tracking control is designed for the estimation of compressor power and then to drive pressures of the intake and exhaust to track desired trajectories which lead to polluted emissions reduction. To attenuate the tracking error and reduce fuel mass flow rate effects the H_infty performance is included in the control design. We focus on controlling both EGR and VGT valves which represent the two actuators of the turbocharged diesel engine. Considering the fact that these valves may be saturated and their value should be positive, the input saturation is studied. Both observer and controller gains can be computed in only one step procedure via satisfying conditions presented in terms of Linear Matrix Inequalities (LMIs) based on Lyapunov theory. Finally, simulation results are presented to show the efficiency of the proposed tracking control design.

Keywords: Process control and instrumentation, Control applications, Discrete-continuous time design
Abstract: Multi-linear model approach is employed to control a strongly nonlinear process where a global controller is constructed based on a weighted combination of local controller outputs. In this paper, a new weighting method is presented for multimodel control of nonlinear systems, in which the gap metric is used to design weighting functions for local controllers combination. Compared to conventional weighting approaches, the suggested new weighting approach requires just one tuning parameter. It is therefore simple and easy to adjust, and improves closed-loop performance.

Keywords: Linear and nonlinear systems, Control algorithms implementation, Modeling and simulation
Abstract: Quadcopters, a type of Unmanned Arial Vehicle, have gained a major area of interest due to their various applications, small size, low cost, and maneuverability. The flight principle of a quadcopter regulates its thrust and moments to control movements. This paper investigates linear controllers for non-linear systems based on linearized systems, comparing PID and LQR with integral control, examining input/output disturbance effects, and comparing cost and performance. The results of this investigation show that the LQR controller outperforms the PID controller in controlling quadcopters with satisfactory performance and cost results. It offers a balance between performance and cost, handling constant disturbances efficiently. In the non-linear model, there is a minimal difference in performance and controllers' costs due to the strong coupling between system states and constant disturbances.

Keywords: Linear and nonlinear systems, Estimations and identification, Fault detection and Diagnostics
Abstract: In this paper the fault detection and estimation of a nonlinear system described by a decoupled multimodel subjected to unknown but bounded disturbances and measurement noise is studied. For detection two residual are generated to determine the occurrence of a fault, whereas for fault estimation an augmented system is introduced and an interval multiobserver is proposed. Sufficient conditions are formed under Linear Matrix Inequalities (LMI) to ensure convergence. Simulation results are presented to show the effectiveness of the proposed method.

Keywords: Linear and nonlinear systems, Mathematical systems theory, Optimal control
Abstract: In this paper, we prove approximate solvability of the semilinear operator equation Lu+Nu=v using Tikhonov regularization and Browder's fixed point theorem under the following assumptions:

(i) The corresponding linear operator equation Lu=v is approximately solvable. (ii) The nonlinear operator N: U rightarrow V is compact. (iii) | Nu | leq a+b | u | for some positive constants a and b with b < frac{2sqrt{lambda}}{ 1+frac{1}{2 sqrt{lambda} } } where lambda >0 is a regularization parameter. Theory is substantiated with an application for computation of regularized control for an approximately controllable semilinear system.

Keywords: Linear and nonlinear systems, Optimization, Control of telecommunications systems
Abstract: For digital control systems which are implemented using digital devices, control performance can be affected by the quantization of input and output signals. Therefore, such control systems must be designed to encompass signal quantization to prevent the performance degradation associated with quantization. One design guideline for this is to make the response of a quantized system and an ideal system without quantization as close as possible. In a conventional study of design based on minimizing an upper bound of the error, design conditions expressed by matrix inequalities must be solved numerically. In contrast to the conventional method, this study proposes an analytical design method, and as a result, optimal controllers are designed with a small computational load. Finally, the effectiveness of the proposed method is demonstrated by numerical examples to illustrate the significance of this study.

Keywords: Linear and nonlinear systems, Robust control and Hinfty control, Optimization
Abstract: This paper addresses the robust dynamic output feedback (DOF) problem for linear parameter-varying (LPV) discrete-time systems with polynomial dependence of uncertainties. For this purpose, the robust control problem is formulated via linear matrix inequalities (LMI) using Lyapunov's theory for a stability proof. Two relaxation methods are required to solve the robust LMI problem. The first is necessary to expand the Lyapunov stability condition to the structured linear control (SLC) problem for DOF controllers. The second type of relaxation is based on the theorem of Ehlich and Zeller and is required to include the polynomial dependence of uncertainties in the LMI design. These two relaxation methods yield an iterative LMI optimization approach. In this paper, we consider the specific example of a robust I-PD controller with a predefined decay rate that satisfies H_{infty} conditions for a polynomial parameter-dependent uncertain system.

Keywords: Autonomous Systems, Modeling and simulation, Optimal control
Abstract: In this paper, we present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. The model is used in a Model Predictive Controller in which we included a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation. The stability analysis of the model is presented, as well as a brief description of the overall planning and control scheme which includes an offline trajectory generation pipeline, an online local speed profile planner, and a low-level longitudinal controller. An improvement of the lateral path tracking is demonstrated in preliminary experimental results that have been produced on a Dallara AV-21 during the first Indy Autonomous Challenge event on the Monza F1 racetrack. Final adjustments and tuning have been performed in a high-fidelity simulator demonstrating the effectiveness of the solution when performing close to the tire limits.

Keywords: Autonomous Systems, Motion control, Optimization
Abstract: The wide use of Unmanned Aerial Vehicle UAV is pushing researchers to develop and improve control techniques for these systems. An optimized LQR controller is proposed to control the 3DOF quadrotor "Quanser" in this paper. Using different metaheuristic algorithms PSO, FPA and ACO to calculate the controller’s gain. Comparing by other works, this article presents sufficient results obtained by the three techniques proposed to control the angular positions of the studied system "Roll, Pitch and Yaw" especially by keeping the saturation of the voltage applied to the quadrotor motors in the simulation, and makes it possible to find the best method taking en consediration three comparison factors: stability, speed, precision.

Keywords: Motion control, Autonomous Systems, Control algorithms implementation
Abstract: A basic prerequisite for the realization of automated driving is a lateral control, which converts the input of the trajectory planning into a control value for the steering actuator. In the past, many controller concepts have been presented that address various applications. These include automated driving on highways, in cities, and in the context of parking. In this paper, first a comprehensive overview of established controller concepts is given. Subsequently, these are evaluated and suitable metrics for assessing controller performance are presented. Based on the findings, an own concept based on model predictive control (MPC) using a dynamic single-track model is presented. The focus here is on the development of a universal controller that can be used in different applications without reparameterization. At the same time, the focus is on computational efficiency, since the concept is used in the context of a fully automated driving system, as shown in [1]. To evaluate the concept, a real-world validation is performed using a series passenger vehicle in representative scenarios. The model predictive controller is compared with a classical, rule-based concept. The driving experiments take place in urban areas and cover the use cases of inner-city driving up to 50 km/h (City Pilot) as well as driverless operation inside parking garages (Automated Valet Parking).

Keywords: Robust control and Hinfty control, Motion control, Robotics
Abstract: In this paper a novel method is presented for segmented polytopic LPV control. A typical drawback of polytopic LPV is the conservatism introduced by the larger than necessary polytope to abide the assumption of convexity. To reduce conservatism and maintain convexity at the same time, a new approach is presented in which multiple polytopes are defined according to the operating needs and the resulting controllers are blended based on a selection of criteria. First, the overlapping region is equipped with a Vinnicombe metric that measures distance between dynamic systems and an appropriate logic decides on which set of controllers to choose. As an alternative, the H∞ norm is utilized and either the controller resulting in higher or lower gain closed loop is chosen. The proposed method is tested in simulation on a 2-DOF robotic arm model.

Keywords: Fault detection and Diagnostics, Control applications, Observer design
Abstract: This paper proposes a fault tolerant second order sliding mode control (FT-2SMC) for linear discrete systems. Firstly, the sensor fault is estimated with a first order sliding mode observer. The observer gain is computed using Lyapunov formulation and Linear Matrix Inequalities in order to ensure the asymptotic stability. Secondly a discrete fault tolerant second order sliding mode control is developed based on states and fault estimation. A simulation example is given to illustrate the effectiveness of the proposed FT-2SMC.

Keywords: Fault detection and Diagnostics, Intelligent and AI based control, Industrial control
Abstract: The autoencoder is widely used in anomaly detection. The anomaly detection-based approach assumes a reconstruction error for the anomalous samples higher than the normal ones and can identify abnormal data from normal data. This paper compares two deep autoencoders to earlier anomaly detection in electric motors. Our approach is based on reconstructing the stator current temporal signal for rotor broken bars detection. To evaluate the performance of the Dense and the conv1D autoencoder, we have used an experimental database acquired under varied load conditions and with different types of rotor faults. The results obtained show a promising induction momotornomaly detection approach.

Keywords: Fault detection and Diagnostics, Observer design, Estimations and identification
Abstract: In controlled robot platoons, every subsystem needs information about the states of its neighbouring subsystems. On the other hand, actuator faults and communication errors have a considerable influence on the behaviour of the entire networked system. In this paper, an actuator fault diagnosis method is proposed that uses relative output information and it can be implemented without information interchange among the neighbouring subsystems. A subsystem model is proposed for the platoons, based on which the non-measurable states of the neighbouring subsystems can be determined by proper filtering techniques. In addition, the model is also used as the basis for designing a Proportional Integral (PI) observer for actuator fault estimation. Both the pole placement method and Linear Quadratic Estimator (LQE) are explored to determine the observer gain. The simulation results show that the model without network communication produces the same expected behaviour as the one that uses network communication. In addition, the proposed observer can correctly estimate the magnitude of the actuator fault.

Keywords: Fault detection and Diagnostics, Observer design, Robust control and Hinfty control
Abstract: This paper proposes an integrated design of fault tolerant control (FTC) for continuous-time switched systems in the presence of sensor faults and external disturbances. A switched descriptor observer is proposed to estimate the sensor faults and system states simultaneously, and using the estimates, a state feedback fault-tolerant controller is constructed to ensure the robust stability of the closed-loop system. The observer and controller gain are determined through {H_infty } optimization using a one-step Linear Matrix Inequality (LMI) formula. This is achieved by utilizing multiple Lyapunov functions and the concept of Average Dwell Time (ADT) switching signal. Sufficient conditions, characterized by reduced conservatism, are then derived and expressed in LMI form. Finally, a simulation analysis of a two-tank liquid level control system is presented to illustrate the effectiveness of the developed integrated FTC strategy.

Keywords: Fault detection and Diagnostics, Observer design, Optimization
Abstract: The present paper deals with robust sensor fault detection using a residual intervals generator for discrete time linear systems with bounded internal and external disturbances. To enhance the accuracy of fault detection, the L∞ performance technique is used. The stability conditions are deduced using the Lyapunov approach and are formulated in terms of Linear Matrix Inequalities (LMIs). A simulation example is then used to illustrate the significance of the proposed strategy.

Keywords: Fault detection and Diagnostics, Optimization, Discrete-continuous time design
Abstract: This paper proposes a new strategy to sensor fault tolerant control in hybrid systems, addressing the challenge of unknown switching signals. Our proposed method employs additive control techniques while estimating switching modes off-line using the Chiu clustering algorithm. By integrating data from multiple sensors and dynamically adapting control inputs, our approach effectively manages sensor faults. The Chiu clustering algorithm provides an efficient means of estimating switching modes, enhancing fault tolerance. This paper outlines the theoretical foundations of our approach, including additive control design and the off-line estimation process. Simulation results illustrate the effectiveness of our method, offering a promising solution to sensor fault tolerance in hybrid systems with unknown switching signals, further improved by the Chiu clustering algorithm.