This paper presents an implementation of an implicit Generalized Predictive Control (GPC) strategy applied to a wastewater treatment plant (WWTP) modeled via a biofiltration process simulator called SimBio. Without identifying an explicit dynamic model, the control law is derived directly from system input-output behavior. The main goal is to regulate the effluent ammonium ($\mathrm{NH}_{4}^{+}$) concentration by manipulating the aeration rate, while coping with perturbations such as influent flow and ammonium concentration. Several configurations of the control parameters were tested and evaluated using process performance indicators. The obtained results show that the proposed controller allows a good tracking of the desired setpoint while presenting a low energy consumption during the considered period of the experiment.

The purpose of this paper is to model the green hydrogen production chain using the proton exchange membrane electrolyzer. By adding a battery, the proposed scheme optimizes the energy transfer between the photovoltaic generator (PV) and the electrolyzer. The MPPT controller and the energy management supervisor stabilize the overall system. The system was simulated under MATLAB/Simulink to study the dynamic performance of each subsystem including photovoltaic generator, converters, storge system (battery, hydrogen tank,) and electrolyser throughout variation in solar radiation. The results show that the photovoltaic generator successfully maintains its maximum power and enables efficient use of the produced energy. The battery is used to have the DC bus voltage fixed and counteract the power disturbance coming from solar input (irradiation). Also, the electrolyser has shown a long-term stable performance with actual energy consumption of 63–65 kWh/kg of produced hydrogen. Overall, findings show that the suggested hybrid PV–electrolyser system is effective as it maximizes green hydrogen generation and possesses an enhanced overall performance.

Biological wastewater treatment processes are essential in the sustainable management of water resources, offering an efficient method for removing contaminants and pollutants, such as ammonium, from wastewater to protect both public health and the environment. Among various treatment methods, submerged aerated biofilters stand out for their efficiency in converting high ammonium concentrations into nitrate. This process stimulates the growth of specific microorganisms on filtering materials, aiding in efficient pollutant conversion.However, the complexity of biological wastewater treatment processes presents significant modeling challenges, especially under varying operational conditions. Linear Parameter-Varying (LPV) models have emerged as a promising solution to accurately represent these nonlinear systems. Despite their potential, constructing LPV models remains complex, especially for intricate biological treatment processes like wastewater treatment.This paper presents a novel methodology within the global approach framework for estimating continuous-time LPV models. The proposed approach addresses the challenge of initializing iterative procedures due to the lack of prior knowledge about LPV model parameters. By extending the reinitialized partial moment approach to LPV models, the methodology provides an effective pre-estimate for initializing parameter estimation algorithms. Validation of the proposed methodology through simulation examples establishes a robust foundation for extending the approach to real-world applications, such as estimating LPV models for the nitrification process in wastewater treatment plants.

This paper introduces a novel strategy for the diagnosis and fault-tolerant control (FTC) of inter-turn short-circuit (ITSC) faults in the stator windings of Doubly Fed Induction Generator (DFIG)-based wind turbines. ITSC faults are among the most common electrical issues in rotating machines: early detection is therefore essential to reduce maintenance costs and prevent severe damage to the wind turbine system. To address this, a Fault Detection and Diagnosis (FDD) approach is proposed to identify and assess the severity of ITSC faults in the stator windings. A state-space model of the DFIG under ITSC fault conditions is first developed in the (d,q) reference frame. Based on this model, an Unknown Input Observer (UIO) structured using Takagi–Sugeno (T-S) fuzzy models is designed to estimate the fault level. To mitigate the impact of the fault and ensure continued operation under degraded conditions, a T-S fuzzy fault-tolerant controller is synthesized. This controller enables natural decoupling and optimal power extraction across a wide range of rotor speed variations. Since the effectiveness of the FTC relies on accurate fault information, a Proportional-Integral Observer (PIO) is employed to estimate the ITSC fault level. The proposed diagnosis and compensation strategy is validated through simulations performed on a 3 kW wind turbine system, demonstrating its efficiency and robustness.

This paper presents a comparative study of Maximum Power Point Tracking (MPPT) strategies applied to a photovoltaic (PV) system interfaced with a Quadratic Boost Converter (QBC). Two control strategies are analyzed: the conventional Perturb and Observe ($\mathrm{P} \mathrm{\& O}$) algorithm and an advanced model-free controller based on Deep Reinforcement Learning (DRL) using the Deep Deterministic Policy Gradient (DDPG) algorithm. A complete system-level simulation is carried out in MATLAB/Simulink, incorporating a dynamic PV model, a detailed QBC circuit, and real-time control loops. The simulation results show that DRL-based strategy significantly outperforms $\mathrm{P} \& \mathrm{O}$ in terms of tracking efficiency ($94.3 \% \mathrm{vs}$. 91.8%), settling time (6 ms vs. 150 ms), and power ripple (${\lt}0.5 \%$ vs. 3.2%) under varying irradiance and temperature conditions.

This paper investigates the finite-region stability (FRS) and stabilization problems for 2D continuous-time systems described by a Roesser model. We first establish a novel set of FRS and finite-region boundedness (FRB) conditions, extending the L2-based concept on finite-time stability from 1D systems to the 2D continuous domain with a new condition based on the generalized state vector of the 2D continuous-time system in contrast with the norm-based condition found in the literature. Sufficient conditions are then derived to guarantee that the system state remains within a predefined quadratic region over a finite-time horizon. Furthermore, the framework is extended to analyze FRB under two distinct classes of external disturbances. Finally, a complete procedure for state feedback stabilization is provided, with all sufficient conditions for FRS and stabilization expressed entirely in terms of numerically tractable Linear Matrix Inequalities (LMIs) enabling controller design that ensures closed-loop finite-region performance under both disturbance classes. The effectiveness and feasibility of the proposed approach are demonstrated through numerical examples.

The reliability of wind energy conversion systems is critically influenced by electrical faults in the generator, particularly Stator Inter-Turn Short-Circuits (ITSC). If undetected in their early stages, these faults can cause severe equipment damage and significant downtime. The aim of this paper is to use an Unknown Input Observer (UIO) to detect faults in a wind turbine system equipped with a Doubly-Fed Induction Generator. The system’s non-linear dynamics are accurately described under different operating conditions using a multi-model representation. The observer that is being suggested is assessed on its sensitivity to ITSC faults and its ability to handle external disturbances. Simulation results demonstrate that the UIO can efficiently detect and isolate stator ITSC faults while maintaining high robustness to wind speed variations. Based to the findings, confirm that the suggested UIO based fault recognition design is effective and reliable to improve the dependability of wind turbine systems.

The feasibility of next-generation urban power networks hinges on the ability of DC converters to replace their conventional counterpart. Reliability and efficiency are heavily contested points when discussing the challenges facing power electronics proliferation in power systems. Motivated to find a working solution, this paper presents the paradigm of the two-time scale converter. By employing a division in dynamics, interpreted from singular perturbation theory, a cost-effective redundancy can be attained in the form of complementary fast and slow subsystems. This enables the proposed converter to handle considerable perturbations with a quick response time and achieve cost-effective operation at steady state, thanks to the reduction in switching frequency. The overall controller scheme comprises an inner current control loop and an outer voltage control loop; the control laws are derived from a generic Lyapunov approach, which can be adapted to the characteristics of the voltage source. The simulation results demonstrated a seamless response to various types of perturbations and the proper handling of different omission fault scenarios.

This paper presents a new controller based on the Lyapunov theory used in a stand-alone photovoltaic (PV) system. The proposed system does not include a pulse width modulation (PWM) controller unlike a traditional one. The quadratic converter including one controllable switch is modeled as a switching system which allows us to propose a switching rule that can be used to control the converter. This part assures the stability of the operating point at any desired voltage value. To determine the operating point that allows to extract a maximum of power from the system, a double-mode variable step size maximum power point tracking algorithm is included in the controller. The proposed method is a hybridization between the well-known perturb and observe (P O) algorithm and an operation at a fixed voltage value during fast solar irradiance changes. The performances of the global controller are tested through simulation using Simulink/MATLAB ^TM . It is compared to a classical system with PWM and P O controllers. The result shows that the proposed controller can harvest up to 15 % more energy from the PV panel in case of fast solar irradiance changes without significantly increasing the implementation cost.

This paper addresses the problem of H-infinity performance analysis of 2D discrete singular stochastic system described by Roesser model which is challenging since it involves 2D random variables and the disturbance simultaneously. Sufficient conditions are established for the regularity, causality and stability of the system. The proposed results are expressed in terms of strict linear matrix inequalities. Furthermore, a mean square asymptotic stability with an H-infinity disturbance level is developed. Simulation example is provided in order to illustrate the relevance of our approach.

The overexploitation of non-renewable fossil resources has led to dangerous warming of our planet due to greenhouse gas emissions. The main reason for this problem is the increase in global energy demand. The rising prices of oil and gas have pushed governments around the world to turn to renewable energy, especially solar and wind power. For this reason, the present paper aimed to focus on photovoltaic and wind energy systems. However, exploitation of these two sources individually is not always easy because of their intermittent and irregular characters. Therefore, the obvious solution is the hybridisation of these two sources, which, when used alongside other systems such as batteries, increases the reliability, availability, and efficiency of these renewable sources. The main objective of this paper is to give an overview of different configurations of hybrid solar and wind energy conversion systems. First, the behaviour of each system, as well as their mathematical models, characteristics, and existing topologies, is presented. Then, the control strategies, optimal configurations, and sizing techniques, as well as different energy management strategies, of these hybrid PV–wind systems are presented.

This paper deals with the state feedback control of singular continuous-time linear systems with discrete-time measurement of the state. Intuitively, descriptor or singular description of linear systems is more general than conventional state space description. In particular, a descriptor form includes information about algebraic as well as dynamic constraints. The use of an event-triggering mechanism results in a varying sampling-time period. Moreover, it is worth noting that the system is really in open loop during the inter-event time interval. The consequence is that there is no means to make the system admissible and by the way all the nice theory of admissibilization becomes useless. Hence, a continuous-time singular system in a sampled data closed-loop framework is badly posed unless the open loop is regular. In the case the open-loop system is regular, it is shown that the fast component of the state vector exhibits a discontinuous behavior. The dynamic event-triggering mechanism is shown to insure that the inter-event time interval is lower bounded, and as a consequence, it avoids the Zeno phenomenon. Simulations are presented to illustrate the obtained results.

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.

The analysis of positive nonlinear delayed systems is of great importance for many real‐world applications. Such systems' stability and stabilization assessment is still an open topic, and there is limited literature on this field. Moreover, further convergence conditions should be considered for many experimental processes, such as exponential stability analysis, which is highly important. Considering the above, we deal in this study with the problem of exponential stability and stabilization of nonlinear fuzzy positive systems with delay. We establish exponential stability criteria using Lyapunov–Krasovskii functional (LKF) and a delay bi‐decomposition approach for bounded and time‐varying delayed systems. The obtained results are then extended to the exponential stabilization case. The control law is designed using Parallel distributed compensation (PDC). The proposed approach, formulated in terms of linear matrix inequalities (LMIs), allows reducing the conservativeness of the delay‐dependent conditions. A comparative study is presented to illustrate the superiority of our method. Moreover, simulation results for the two tanks process show the advantages of the proposed control design.

This article provides new conditions for testing the structural stability of 3D Roesser models. The models can be discrete, continuous, or mixed discrete/continuous. The conditions consist in a few tests on the eigenvalues of matrices and one test on an auxiliary 2D model. The latter test is based upon a hierarchy of linear matrix inequalities relaxations. The global test for structural stability is necessary and sufficient for a large enough value of the hierarchy level.

This paper deals with the problem of fault detection and state estimation in an observer-based controller framework for two dimensional (2D) continuous time switched systems described by Roesser model. The aim of this paper is to estimate the state and sensor fault and to stabilize the 2D system by an observer-based controller where the estimation of the state is used by the controller. The Lyapunov theory is then applied to establish two conditions in the form of matrix inequalities allowing the design of the observer-based controller. The two conditions have to be solved sequentially since the observer condition depends on the solution of the controller condition. A numeric example is provided in order to illustrate the effectiveness of our results.

This article aims at broadening the panel of the existing conditions for structural stability of 2-D Roesser models. The models can be discrete, continuous, or mixed discrete/continuous. The conditions are necessary and sufficient. They are either expressed in terms of linear matrix inequalities or based on direct tests on eigenvalues of constant matrices. The effectiveness of these tests is highlighted.

The work on this paper aims to improve the efficiency of a photovoltaic energy system. Reducing the losses in the adaptation stage allows to improve this efficiency. For that, in this paper we will work on the adaptation stage between the solar panels and the loads to reduce the losses. As a first step, we will present a comparison between a conventional DC-DC boost converter usually used in solar system and a quadratic boost with a single switch. This comparison aims to show the advantage of using quadratic converters with high gain in solar systems. The results show that the quadratic converter has a higher efficiency compared to the conventional one, it allows also to reach higher values of gain for a practically acceptable values of duty cycle. This comparison justifies the choice of the quadratic converter. Then, a new dual-mode variable step-size maximum power point tracking (MPPT) algorithm is proposed. The proposed algorithm is used to control the quadratic converter. The proposed algorithm is based on the perturb and observe (P&O) algorithm. The P&O is one of the most known and used MPPT methods, its drawbacks appear during fast solar irradiance variation. The proposed control method is a double mode algorithm. The first mode is active when the operating point is near of the MPP. This mode allows to stabilize the operating point to reduce the steady-state oscillations that occur using the P&O. The second mode is activated when the operating point moves away from the MPP. This mode is a P&O with a large step size that allows tracking the MPP quickly which also reduces the losses. The proposed algorithm is tested through simulations using Simulink/MATLAB. Simulations results prove the efficiency of the proposed algorithm and its advantage compared to the P&O.