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In this paper, a new identification method is developed for Wiener–Hammerstein systems that contain memory nonlinearity of backlash type. The latter is flanked by two linear transfer functions that may be parametric or not. The proposed identification method is carried out in two stages. Firstly, the parameters (the lateral borders) of memory operator are identified using a set of constant inputs. In the second stage, an identification method based on Fourier analysis is developed to determine the frequency responses of both transfer functions. The performances of the proposed identification method are highlighted by simulation results. Finally, experimental application has been established to show the effectiveness of this method.

The aim of this paper is to design a longitudinal motion controller for electric vehicles (EVs) and hybrid electric vehicles (HEVs) fitted with BLDC (Brushless Direct Current) in-wheel motors (IWMs). The developed controller relies on a highly complex nonlinear model, serving as a foundation for the creation of longitudinal vehicle movement simulators. This model incorporates the different nonlinear factors that arise in normal driving situations, such as rolling resistance, aerodynamic effects, and numerous phenomena occurring at the tire/road contact, which are captured by the Pacejka tire model. The backstepping design approach constitutes the basis of the controller’s design. It has been formally demonstrated that the controller developed achieves its objectives, such as close control of vehicle and driving wheel speeds in longitudinal motion.

The main objective of this research is to introduce an innovative and advanced methodology for the design of unknown-input observers adapted to continuous-time Takagi–Sugeno (T–S) systems. We focus on the development of functional observers capable of handling the unknown inputs present in the state and output equations. The design and analysis of these observers are strongly based on the principles of Lyapunov–Krasovskiĭ stability theory, providing a robust and theoretically powerful background. The convergence criteria for these observers are structured according to the formulation of linear matrix inequalities, providing a strict basis for stability analysis. In order to underline the effectiveness of the proposed approach, we offer full validation through simulation results derived from two numerical examples. These examples serve as specific demonstrations of the performance of the designed observers, highlighting their effectiveness in both reduced-order and full-order scenarios. Through this detailed exploration, we aim to highlight the applicability in actual applications and the reliability of our methodology introduced in the field of unknown-input observers for T–S systems.

In proton exchange membrane fuel cells (PEMFC), the management of the oxygen excess ratio is crucial. Indeed, an insufficient amount of oxygen can lead to the problem of degradation of the performance and lifetime of the cell. However, this ratio is not measurable, hence the need to use an observer to estimate it. Unlike a standard high-gain observer, where measurement noise affects the observer performances, in this paper, we propose a filtered high-gain observer based on measurable variables such as compressor speed and supply manifold pressure. Thus, it is equipped with low-pass filter dynamics to reduce sensitivity to measurement noise. The stability of the observer is analyzed using Lyapunov stability tools. The simulation results highlight the superiority of the new proposed observer compared with a standard high-gain observer.

This paper introduces an enhanced approach for monitoring the speed of an electric car propelled by a brushless DC motor (BLDC) through the utilization of particle swarm optimization (PSO). The aim is to optimize the proportional (Kp), integral (Ki), and derivative (Kd) gains of the PID controller in conjunction with the fuzzy controller. The system architecture is meticulously modeled, and optimization is conducted within the MATLAB/Simulink environment. Through rigorous experimentation, the proposed methodology showcases superior efficacy in minimizing error compared to conventional techniques. By synergizing PSO with PID and fuzzy control, the study demonstrates a promising avenue for enhancing the speed control system of BLDC-powered electric cars, thereby advancing the field of electric vehicle technology toward greater efficiency and performance.

This paper presents a complete model for the longitudinal motion of an electric vehicle (EV), incorporating the dynamics of the vehicle chassis, the aerodynamic effects and the road resistance. The propulsion system uses brushless direct current (BLDC) motors as in-wheel motors to drive the rear drive wheels. The developed model aims to improve the understanding of the performance of the electric vehicle fitted with BLDC in-wheel motors under real driving conditions, taking into account aerodynamic forces and road resistance.

This paper proposes a radial basis function neural network (RBFNN) observer for surface-mount permanent magnet synchronous motor (SPMSM). The corresponding observer is used to estimate the rotor speed, and the rotor position. The convergence of the observer estimation error is analyzed using Lyapunov theory, and uniformly ultimate boundedness stability is guaranteed. Simulation results are shown to confirm the effectiveness of the proposed observer.

Switched reluctance generators (SRG) become increasingly competitive with other electrical machines due to their design, which includes only a stator winding. The SRG are known for their reliability and capacity to operate over a wide speed range, these generators find significant use in variable wind power plants. The main limitations of these machines are torque fluctuation and relatively low efficiency. In this respect, the aim of this work is to mitigate these drawbacks. Presently, an extremum seeking online method has been developed, and a backstepping controller is designed for SRG-based wind energy conversion system (WESC) connected to the grid via a VSC converter. One of the main objectives of the proposed controller is to achieve the optimum rotation speed of the turbine and to regulate the SRG for tracking the maximum power point (MPPT) while minimizing torque ripple via Direct Instantaneous Torque Control (DITC). To show the efficiency and performance of the proposed controllers, simulation examples of the proposed controllers are established using MATLAB/SIMULINK tools. The simulation results show that the efficiency and performance of the system using the proposed controller are significantly improved.

This paper addresses the challenges in hydraulic pressure control for an electrohydraulic brake (EHB), considering nonlinearities and uncertainties such as friction and parameter variations. The proposed solution is a robust integral backstepping control (IBC) strategy, which incorporates motor current dynamics and the LuGre friction model. A nonlinear observer is introduced to estimate the unmeasurable internal state of the LuGre model. The IBC is applied to pressure control for the EHB, resulting in improved steady-state behavior and robustness. The paper concludes with a global stability analysis using Lyapunov theory and performance validation through simulation using MATLAB/Simulink.

In this paper, a Backstepping controller is proposed for the air supply system of polymer electrolyte membrane fuel cell (PEMFC). The control objective is to regulate the oxygen excess ratio to its optimal value despite the disturbances introduced by rapid transitions in the cell output current. This improves the PEMFC efficiency, reduces its losses, and prevents oxygen deficiency. To describe the airflow dynamic behaviors, a fourth-order PEMFC model is established, and then a Backstepping controller is proposed to track the optimal excess oxygen ratio. The closed-loop performances are established using Lyapunov stability tools. The efficiency of the suggested control strategy is validated, under Matlab-Simulink environment, by conducting a comparison with conventional controllers such as PID, and Super-Twisting Controller (STC). These simulations illustrate the supremacy of the proposed controller when the PMFC is subjected to various operating conditions.

The global energy consumption of commercial and residential buildings, driven by economic and demographic growth, has become a critical concern. This surge in demand has resulted in elevated electricity prices, strain on the primary grid, and increased carbon emissions due to inefficient energy utilization. This paper introduces an innovative energy management system (EMS) integrating both supply-side (SSM) and demand-side management (DSM) strategies. The demand side management involves optimizing load scheduling based on predicted electricity pricing. In contrast, the supply side management determines the next 24 hours optimal power setpoints, incorporating a Photovoltaic System (PV), an Energy Storage System (ESS), and the Electrical Power Grid (EPG). The primary objectives of this proposed EMS are to concurrently decrease electricity costs and the peak-to-average ratio (PAR), all while ensuring user comfort in terms of appliance operating waiting times. The proposed multi-objective optimization problem is solved using Particle Swarm Optimization algorithm (PSO). Based on residential building energy demand and time-of-use pricing (TOU), simulations were conducted using MATLAB. The results demonstrate the effectiveness of the proposed approach, achieving a 28% reduction in both electricity costs and the PAR, while maintaining user comfort.

This paper introduces an innovative Active and Reactive Residential Building Energy Management (AR-RBEM) system, seamlessly integrated with a smart microgrid and the Electrical Power Grid (EPG) supplier. The proposed AR-RBEM computes optimal active and reactive power setpoints for each energy source over the next 24 hours based on predicted PV power generation and load consumption. The proposed strategy aims to achieve two primary objectives: minimize overall energy expenses, considering both active and reactive power consumption, and account for Energy Storage System (ESS) degradation costs. Additionally, it seeks to reduce the Peak-to-Average Ratio (PAR) and carbon emissions from the supplier’s perspective. To tackle this multi-objective problem, a metaheuristic algorithm which is Particle Swarm Optimization (PSO), is employed. To assess the effectiveness of the proposed approach, a comparative analyse is conducted, emphasizing the significance of concurrently managing active and reactive power compared to a system exclusively managing active power (A-RBEM). Both strategies are evaluated using Time of Use Tarriff (TOU) and real world data. The comparative analysis underscores the advantages of the proposed AR-RBEM, showcasing benefits in terms of cost reduction for both active and reactive power of 59,6%, a substantial 25.3% reduction in carbon emissions compared to the alternative strategy.

This paper deals with the problem of output containment control of multi-agent systems. A backstepping-based approach is used to design a novel distributed controller that allows fast output containment of high order multi-agent systems. The proposed method ensures exponential convergence of the output containment error to zero despite external disturbances. A numerical example is provided to highlight the efficiency of the proposed approach. Applications of the proposed control approach in power systems are also presented.

In terms of energy transmission, several phenomena can occur, which can affect the reliability of the network, cause instabilities, and deteriorate the performance of the system, including delay. This work considers a simple energy source presented by a Buck-type DC-DC converter remote by a transmission line, assimilated to a distributed delay that interposes between the source and the considered resistive load. We are interested in the modeling of this system and the design of an adaptive observer that provides accurate estimates of the state and unknown parameters during the transmission of energy along the line.

In this paper, the problem of controlling anti-lock braking system (ABS) was considered. Based on LuGre friction model, an output-feedback adaptive controller that enjoys good braking performances whatever the road conditions, was developed. Specifically, the controller tracks the optimal slip coefficient and, unlike previous work, the reference slip ratio is online estimated according to road conditions as well as longitudinal vehicle speed. The adaptive feature of the controller proves to be crucial for compensating the uncertainty on the changing road characteristics. The controller consists of: (i) a generator of the reference signal for the slip coefficient designed in the form of a neural network; (ii) a nonlinear observer for estimating the internal friction state; (iii) a backstepping adaptive control law, designed on the basis of a quarter-car model, that ensures tracking of the optimal slip value and providing estimates of the model unknown parameters. The controller stability properties are analyzed using Lyapunov stability tools. The theoretical stability results are confirmed by processor-in-the-loop (PIL) experiments in different road conditions. A comparative study, a robustness test, and a co-simulation involving CarSim and MATLAB/Simulink were made to highlight the much better performances, in terms of stability, tracking, robustness, and practicability of the new controller.

In this paper, a dc–dc boost converter with continuous input is investigated. In addition to its input current, the converter offers the advantage of using a reduced number of electronic components. The objective is to design an appropriate controller for the nonlinear model taking into account the system nonlinearities, the uncertainty of the resistance load, the constrained fuel cell current, and the saturation of the control input signal (duty ratio).The point is that the converter presents a no minimum phase feature, and the duty ratio must constrain between 0 and 1. Then, a saturated nonlinear controller is elaborated. Detailed analysis and simulation show that the proposed controller meets all control objectives, namely: tight dc-bus voltage regulation and asymptotic stability of the closed-loop system. Experimental results are also given, which show the effectiveness of the controller.

This paper deals with the problem of output containment control of heterogeneous linear multi-agent systems. The objective is to design a distributed control law that drives the output of heterogeneous linear multi-agent systems to the dynamic convex hull spanned by the leaders. To do this, a sliding-mode distributed output containment controller is proposed in this work. We consider a general network topology which only contains a united spanning tree with no assumptions made regarding the topology as long as its stationary or quasi-stationary. The dynamics of the leaders are considered unknown, and only their outputs, i.e., the references, are available. The designed method has several advantages compared to the existing literature: (1) exponential convergence of the output containment error to zero within a response time fixed in advance; (2) control law with simple mathematical expression and thus easy numerical implementation; (3) easy and straightforward design procedure; (4) easy controller tuning, as the controller has one tuning parameter only; and (5) robustness to external disturbances without requiring additional signals in the control law for this purpose. A numerical example is provided to highlight the efficiency of the proposed approach under external disturbances.

This paper develops an adaptive neural network (NN) observer for proton-exchange membrane fuel cells (PEMFCs). Indeed, information on the oxygen excess ratio (OER) value is crucial to ensure optimal management of the durability and reliability of the PEMFC. The OER indicator is computed from the mass of oxygen and nitrogen inside the PEMFC cathode. Unfortunately, the measurement process of both these masses is difficult and costly. To solve this problem, the design of a PEMFC state observer is attractive. However, the behaviour of the fuel cell system is highly non-linear and its modelling is complex. Due to this constraint, a multilayer perceptron neural network (MLPNN)-based observer is proposed in this paper to estimate the oxygen and nitrogen masses. One notable advantage of the suggested MLPNN observer is that it does not require a database to train the NN. Indeed, the weights of the NN are updated in real time using the output error. In addition, the observer parameters, namely the learning rate and the damping factor, are online adapted using the optimization tools of extremum seeking. Moreover, the proposed observer stability analysis is performed using the Lyapunov theory. The observer performances are validated by simulation under MATLAB®/Simulink®. The supremacy of the proposed adaptive MLPNN observer is highlighted by comparison with a fixed-parameter MLPNN observer and a classical high-gain observer (HGO). The mean relative error value of the excess oxygen rate is considered the performance index, which is equal to 1.01% for an adaptive MLPNN and 3.95% and 9.95% for a fixed MLPNN and HGO, respectively. Finally, a robustness test of the proposed observer with respect to measurement noise is performed.Graphical AbstractAn adaptive neural network observer for proton-exchange membrane fuel cells using a multilayer perceptron neural network-based observer is proposed to estimate the oxygen and nitrogen inputs to the fuel cell. Neural network weights are updated in real time using the output error. Stability analysis is performed using Lyapunov theory.

To ensure braking stability and maximize energy recovery, electric vehicles combine two braking modes, friction and regenerative braking. This paper proposes a novel braking torque distribution (BTD) algorithm between frictional/regenerative brake systems. Security and regeneration energy are the two main issues in braking torque allocation design. Based on the extremum search (ES) technique, the objectives and constraints of the hybrid ABS brake torque distribution are well handled. The offered BTD can maximize the regeneration energy and improve the battery's state of charge (SOC) by optimizing the electric braking system efficiency, while respecting the braking actuators constraints. The validity and effectiveness of the proposed BTD has been demonstrated by simulation in the MATLAB/Simulink environment.

This paper deals with the problem of cooperative control of time-delayed multi-agent systems. The objective is to achieve fixed-time regulation and reference tracking of multi-agent systems with a random time-delay in presence of external disturbances. In this work, an enhanced event-triggered distributed cooperative control technique is proposed for fixed-time consensus of multi-agent systems with random time-delay and external disturbances. The controller expression contains two terms: a term that ensures fixed-time consensus in absence of time-delay and a second term triggered by the delay detection, and that compensates in fixed time the time-delay effect. The main features of the controller designed in this work compared to the literature are: 1) It ensures reference tracking and regulation of time-delayed multi-agent systems before the desired pre-fixed maximum settling time, despite the random constant or variable time-delays and the external disturbances. 2) The pre-fixed maximum settling time is independent of the value of the time-delay. 3) Unlike conventional event-triggered control systems, in absence of the time-delay, the control signal is not maintained constant but only the time-delay compensating term is deactivated, and fixed-time consensus is still ensured by the remaining term of the controller expression. Comparative numerical case studies with fixed time and conventional asymptotic control are provided to highlight the efficiency of the proposed approach.

Vehicles using internal combustion engine (ICE) adversely affect the environment. Therefore, all vehicle makers have started their mutation toward electric vehicles. In this paper, a quarter vehicle model is developed considering brushless DC (BLDC) motor actuation. The longitudinal behavior of the drive wheel is modeled using the Pacejka model or magic formula. This model describes the interaction between the tire and the road surface, which reflects road condition effect. The purpose of the control is to provide smooth speed control in both acceleration and deceleration modes, regardless of changes in road conditions. Due to the high complexity of the control model, a hybrid fuzzy-PID controller is designed to serve as a speed controller for the system motor-tire-road-surface. It is checked by numerous simulations that the proposed controller performs well, ensuring comfortable steering operation despite road characteristic changes.

For joint estimation of state variables and unknown parameters, adaptive observers usually assume some persistent excitation (PE) condition. In practice, the PE condition may not be satisfied, because the underlying recursive estimation problem is ill-posed. To remedy the lack of PE condition, inspired by the ridge regression, this paper proposes a regularized adaptive observer with enhanced parameter adaptation gain. Like in typical ill-posed inverse problems, regularization implies an estimation bias, which can be reduced by using prior knowledge about the unknown parameters.

Grid-connected photovoltaic (PV) systems have become a significant area of interest for research scientists. Given this, this article presents a nonlinear control of grid-connected PV systems using active power filter (APF) with three-phase three-level neutral point clamped (NPC) inverter. The control objectives involve: (1) Forcing the PV output voltage to track a reference signal provided by the MPPT block in order to produce their maximum power, (2) Regulating the DC link voltage to satisfy a correct operation mode of the three-phase active power filter, and (3) Satisfying power factor correction (PFC) objective at the point of common coupling, PCC. Therefore, the suggested control strategy includes a cascade structure of two loops. The inner-loop regulator is designed using the sliding mode control technique and Lyapunov approaches to ensure the PFC objective by forcing the grid current to follow a given reference signal. The Outer-loop consists mainly of a proportional-integral (PI) regulator so that to regulate the output voltage. Further, the theory of instantaneous real power is used to calculate reference harmonics. The effectiveness of the proposed controller is demonstrated through simulation results using MATLAB/Simulink.

Shunt Active power filters (SAPFs) are a prevalent device for improving power quality (PQ) in power systems by reducing harmonics caused by nonlinear loads. This paper developed a viable approach for improving power quality by utilizing a very promising power conversion device, namely the flying capacitor multicell inverter (FCMI). Advanced control of single-phase four-level FCMI topology operating as SAPF based on a two-loop structure is proposed. In the inner loop, the backstepping controller is adopted to compensate for the harmonic and reactive currents absorbed by the nonlinear load as well as to regulate the flying capacitors (FCs) voltages. Furthermore, since the regulation of the DC bus voltage is an important process in the control of SAPF, an adaptive neuro-fuzzy inference system (ANFIS) is proposed as an appropriate control technique to regulate the DC voltage to its optimal value in the outer loop. Lastly, the simulation results proved the effectiveness of the proposed controller in terms of tracking, regulation, and robustness.

The problem of modeling the longitudinal motion of an Electric Vehicle (EV) or Hybrid Electric Vehicle (HEV) propelled by In-Wheel motor is considered. A complete non-linear model of the vehicle propelled by brushless DC (BLDC) in-wheel motor is presented. The model describes the association of the inverter, BLDC in-wheel motor and the chassis of the vehicle. It describes the behavior of the vehicle in the different driving phases, i.e. acceleration mode and deceleration mode. The relevant fundamental laws are used to model the chassis dynamics taking into account the different non-linear aspects such as aerodynamic effects, rolling resistance and road load. The Pacejka tire model is retained to describe the various phenomena generated at the wheel-road contact. It is shown that the proposed model describes correctly the longitudinal behavior of the vehicle in the acceleration and deceleration modes in different driving conditions.

Supplying off-grid areas with stand-alone wind energy conversion systems required a battery charge controller for energy storage. Furthermore, to cope with the stochastic wind nature, the frequent variations of the load demand, together with ensuring a proper battery charging process, based on the available wind power, the battery status, and the load power demand, a multi-mode control strategy is proposed, which can instantly balance the wind power flow delivered to the DC load and the battery. The control system entails three operating modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, constant voltage (CV) charging mode. Thereafter, a new battery energy management algorithm is also developed, to ensure a smooth switching from one operating mode to another. The controller performances are theoretically analyzed, then are tested through MATLAB/SIMULINK/SIMPOWER environment. The obtained simulation results prove that the proposed controller meets its objectives under various weather conditions and load demand.

This paper focuses on the control development of a grid-tied photovoltaic system coupled with a shunt active power filter. The considered power system includes a PV panel, a single-capacitor three half-bridge interleaved buck converter connected to a three-phase power grid and a nonlinear load. This study aims to accomplish simultaneously three objectives: i) to ensure a high compensation level of current harmonics and reactive power consumed by the nonlinear load in order to perform power factor correction at the grid side; ii) to extract the maximum power from the PV panel regardless the climatic conditions; iii) to regulate the dc bus capacitor voltage to its reference provided by the MPPT controller. To this end, a controller using a two-loop cascade strategy is designed. First, the inner loop is developed with the hybrid automaton approach to formulate the switching control signals with the aim of guaranteeing a unitary power factor. Second, the outer loop is designed using fuzzy logic control to ensure the regulation of the photovoltaic voltage, while the perturb and observe algorithm is employed to perform maximum power point tracking. Finally, the system with the developed control scheme is simulated using MATLAB/SimPowerSystems environment. The obtained results confirm that the proposed controller is able to achieve the previous objectives under varying environmental conditions.

We consider the problem of controlling wind energy conversion systems that are based on doubly fed induction generators. The aim is to design a nonlinear controller to fulfill the control goals considering the two sides, grid and rotor side. On the rotor side, the control objectives are twofold: (i) ensuring the maximum power point tracking requirement and (ii) maintaining the stator reactive power at a desired reference. On the grid side, we also seek the achievement of two control objectives: (i) regulating the DC-link voltage at a given reference value and (ii) ensuring a zero reactive power. Besides the multi-objective nature of this study, the difficulty of the control problem lies in the nonlinearity and uncertainty of the system dynamics and external disturbances. The control problem under consideration is dealt with by designing a model-based controller using the adaptive sliding mode technique. The stability and performances of the closed-loop control system are analyzed both theoretically and by numerical simulations. The latter are performed using 2MW grid-connected wind turbine system in MATLAB/SIMULINK/SimPowerSystems environment. The simulation results confirm that the suggested controller meets its objectives and features robustness properties.

We are considering the problem of sampled-data observer design for nonlinear time-varying delay systems that are state- and parameter-affine. The novelty is that the system is subject to both distributed delay and parameter uncertainty. A Kalman-like observer is developed to deal with both state and parameter estimation. Its main components are: (i) a time-varying-gain state-estimator involving both output and parameter rate injections; (ii) a distributed-nature adaptive output-predictor that compensates for delay and output sampling delay; (iii) an optimized parameter-estimator that copes with parameter uncertainty, optimization is intended in the sense that we make use of all available information, unlike similar existing estimators. The resulting observer is shown to be exponentially convergent, for small delays and sampling intervals, provided the input signal is sufficiently exciting. The analysis is performed using a Lyapunov-Krasovskii functional, Halanay’s lemma, Wirtinger’s inequality and other tools.

This paper investigates the problem of controlling half-vehicle semi-active suspension system involving a magnetorheological (MR) damper. This features a hysteretic behavior that is presently captured through the nonlinear Bouc-Wen model. The control objective is to regulate well the heave and the pitch motions of the chassis despite the road irregularities. The difficulty of the control problem lies in the nonlinearity of the system model, the uncertainty of some of its parameters, and the inaccessibility to measurements of the hysteresis internal state variables. Using Lyapunov control design tools, we design two observers to get online estimates of the hysteresis internal states and a stabilizing adaptive state-feedback regulator. The whole adaptive controller is formally shown to meet the desired control objectives. This theoretical result is confirmed by several simulations demonstrating the supremacy of the latter compared to the skyhook control and passive suspension.

In this paper, a unidirectional wireless power transfer (WPT) system for battery electric vehicle (BEV) charger is addressed. Firstly, a detailed analysis is given focusing on the following points: (i) phase shift control technique, (ii) reactive power compensation topologies and (iii) total harmonic distortion analysis. Secondly, a mathematical model is established for the studied system and its stability is discussed. It was found that the system presents an unstable behaviour when the voltage across the load is chosen as the system output. This non-minimum phase feature is dealt with using an indirect control strategy. Thirdly, based on the derived mathematical model, a non-linear controller using sliding mode control technique is developed. The control objectives are twofold: (i) tight regulation of the output load voltage and (ii) asymptotic stability of the closed loop system. It is shown using theoretical analysis and simulation that the proposed controller meets all objectives.

For joint estimation of states and parameters in time varying time-delay systems (TDS) involving both distributed and lumped time-delays, a general approach is proposed in this paper to transforming existing (non adaptive) observers to adaptive observers. In addition to the convergence conditions of the considered existing observers, a persistent excitation condition is introduced in order to ensure the convergence of parameter estimation. In contrast to implicitly formulated convergence conditions, which are usually assumed jointly for both state and parameter estimations in most TDS adaptive observers, the persistent excitation condition in the proposed approach is explicitly formulated and decoupled from the conditions initially assumed for state estimation.

This paper studies the problem of designing sampled-data observers and observer-based, sampled-data, output feedback stabilizers for systems with both discrete and distributed, state and output time-delays. The obtained results can be applied to time delay systems of strict-feedback structure. The proposed design approach consists in exploiting an existing observer, which features robust exponential convergence of the error when continuous-time output measurements are available. The observer is then modified, mainly by adding an inter-sample output predictor, to compensate for the effect of data-sampling. Using Lyapunov stability tools and small-gain analysis, we show that robust exponential stability of the error is preserved, provided the sampling period is not too large.

This paper proposes a novel approach to control and manage energy production based on grid requirements. The main challenge is the distance between the load and production area, making it difficult to quantify energy requirements in real time at the production area. To overcome this challenge, the proposed approach relies on an adaptive observer design that provides accurate and reliable estimates of multiple signals without expensive and unreliable sensors. In this study, a renewable energy source that consists of a wind turbine coupled to a permanent magnet synchronous generator is actuated with a Vienna power converter. However, it is crucial to emphasize that this approach can be implemented at any production site, regardless of its nature. The paper’s main contribution is the design of a novel adaptive high-gain observer that estimates the grid energy requirement based on the voltage value at the endpoint of the high-voltage direct current line. Moreover, the system load parameters are unknown and come non-linear in the system model. Simulations and analysis demonstrate the effectiveness of the proposed observer, showing convergence to the origin under the well-established condition of persistent excitation (PE).

This paper proposes a novel control strategy for a single-stage photovoltaic (PV) system consisting of two PV panels connected to the grid via a three-level neutral point clamped converter topology with an LCL filter. This control method is based on a developed 5th order model containing sum and difference terms of the voltages of the two input capacitors. This allows decoupling the issues of maximum power point tracking and power factor correction from the issue of balancing the power exchange generated by the panels, which facilitates control design and improves system performances. The control problem under consideration is dealt with using a non-linear controller composed of three loops: (i) an inner loop is developed, based on backstepping and Lyapunov approaches, to correct the power factor by forcing the grid current to be sinusoidal and in phase with the grid voltage; (ii) an outer loop is designed, using a filtered proportional-integral controller, to regulate the DC bus voltage to a climate-dependent reference; (iii) a balancing loop is designed, using a proportional-integral controller, to cope with the neutral point voltage balancing problem. The proposed controller also includes a state observer that provides on-line estimation of the network state variables that are not accessible to measurements. Another important aspect of this work is the development of a formal, complete and rigorous analysis in order to describe the performance and analyse the stability of the closed-loop system using various analytical tools, including averaging theory, Routh criteria and indirect Lyapunov stability. A simulation in MATLAB/SIMULINK environment shows, on the one hand, the efficiency and robustness of the proposed nonlinear controller against changing climatic conditions and, on the other hand, the superiority of this control strategy compared to the one based on a PI linear inner loop controller for the studied system with an l- filter and an LCL filter.

•WECS power converter topologies.•Adaptive and maximum power extraction.•Renewable energy conversion system.•Fixed and variable wind turbine structure.

This paper deals with the problem of stability analysis and controlling a power system in which a synchronous generator and renewable energy sources supply the power to an infinite bus. Firstly the investigation of the existence of the equilibrium points of the system and their stability is presented. For this problem, we derive a sufficient condition on the renewable energy current for the existence of the equilibrium points. In addition, we analyse the stability of the equilibrium points, and show that there is only one equilibrium points which is stable. These results clarify the impact of the penetration of renewable energy sources on the existence of the stable equilibrium points of the system. Secondly, the focus is made on elaborating an output feedback controller, combining a state observer and a nonlinear control law that stabilises the closed loop system whatever the current of renewable energy sources. Numerical simulations are given in order to show the effectiveness of all theoretical results.

In this paper, the control of a bidirectional three-phase BEV charger with vehicle-to-grid functionality is addressed. The charger structure is composed of two bidirectional power converters: a three-phase ac-dc converter and a dc-dc converter associated to an electric vehicle battery. The control objectives are the following: i) estimation of the battery SOC; ii) unity power factor during grid-to-vehicle (G2V); iii) adjusting the reactive power injected into the power grid during vehicle-to-grid (V2G); iv) dc-bus voltage regulation; and v) ensuring and safeguarding the battery charging and discharging process. To this end and based on the system modelling into dq coordinates, a nonlinear controller is designed. The fact is that the battery SOC is not accessible to measurement. Hence, a nonlinear observer is designed to estimate all state variables on the battery-side. The performances of the proposed controller are highlighted using theoretical analysis and numerical simulations, which clearly illustrate that all control objectives are achieved.

Small-gain stability analysis is developed for a class of sampled-data systems consisting of a parabolic-type PDE and a linear ODE connected in series with a sampler-ZOH block. This stability analysis is shown to be useful in designing sampled-data exponentially-convergent observers and exponentially stabilizing output-feedback controllers for some classes of ODE-PDE cascades. This work generalizes existing results in the literature in various directions.

The problem of state observation is addressed for a class of systems subject to sensor delay and parameter uncertainty. The unknown parameter vector enters a finite-dimensional state equation through a possibly output-dependent regressor. The sensor delay effect is captured by a first-order hyperbolic PDE. Doing so, the system turns out to be an ODE-PDE association with a connection point not accessible to measurements. An adaptive observer is constructed by combining ideas from PDE-based and ODE-based design approaches. The observer provides estimates of the ODE subsystem states and parameters, on the one hand, and of the sensor states, on the other. Observer exponential convergence is established under an ad-hoc persistent excitation condition involving the regressor.

The problem of maximum power point tracking (MPPT) control of uncertain photovoltaic (PV) systems is addressed in this paper, on the basis of an adaptive Kalman-like observer. The system is composed of a photovoltaic generator (PVG) supplying power to a DC centrifugal pump driven by a DC–DC boost converter. The PVG is connected to the converter by a long PV cable. Indeed, many PV power plant situations require that the PV panels be installed at a great distance from the converter for several reasons including the security and the property of the site and its exposure to good daily solar irradiance. This obviously leads to the difficulty of PVG output current and voltage measurement using ordinary sensors. Such quantities measurement being in fact necessary for MPPT algorithms and controllers design. Furthermore, the specific parameters of the long PV cable used, namely its resistance and inductance, could have a significant effect on the MPPT control efficiency if instead of PVG delivered voltage and current measurements, only those accessible at the converter side of the cable are used in MPPT controllers design. Therefore, this work aims at overcoming the two aforementioned issues by proposing an adaptive output-feedback control-based MPPT for PV systems. An adaptive Kalman-like observer providing online estimates of inaccessible state variables as well as of PV cable unknown parameters is firstly designed. Then, a backstepping control law is synthesized to meet the MPPT objective. The convergence of both adaptive observer and closed-loop system control has been established using Lyapunov approach and the effectiveness of the developed adaptive MPPT controller in achieving an accurate and robust MPPT control towards uncertainties of the PV cable parameters, has been validated through numerical simulations.

This work addresses the problem of controlling a stand-alone wind energy conversion system with battery energy storage. The study target consists of a series association of a permanent magnet synchronous aero-generator, an uncontrolled rectifier, a Zeta converter, a Li-ion battery, and a DC load. It is well-known that the intermittent nature of wind power, as well as the frequent variations in load demand, decreases battery lifetime and reduces its charging performance. To overcome these drawbacks, a battery charge controller is required to instantaneously balance the wind turbine power flow delivered to the DC load and the battery, so that the wind turbine power is properly utilized and the battery is suitably charged. Precisely, depending on the available wind power, the battery state of charge (SOC), and DC load demand, three charging modes are adapted, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Afterward, to ensure the safety of the battery a new battery energy management algorithm is designed to operate the system in one of the three aforementioned modes, taking into account the weather conditions and load demand variations. Interestingly, no wind speed or battery soc. sensors are required for the implementation of the control system. Therefore, a nonlinear adaptive is also developed to provide the online estimation of the battery SOC required for the management algorithm. The performances of the studied system are tested through a semi-experimental platform involving a Processor-in-the-Loop (PIL) under the embedded board eZdsp TMS320F28335. The obtained simulation results prove that the proposed controller meets its objectives with high performances.

The problem Wiener systems identification is addressed in presence of hysteresis nonlinearities, presently described by the Bouc-Wen model. The latter is nonlinear differential equation involving unknown parameters, some of which coming in nonlinearly. Except for stability, the linear subsystem is arbitrary and, in particular, it is not given a particular structure. By using sine excitations, the identification problem is reformulated as a set of nonlinear prediction-error optimization problems. The latter will be coped with using nonlinear least squares estimators which will formally be shown to be consistent.

The problem of state observation, based on sampled output measurements, is addressed for a class of nonlinear state-affine systems. The difficulty lies in the fact that the state equation involve the undisturbed output with unknown parameters. A hybrid adaptive observer is designed and formally analysed. Sufficient conditions are established for the observer to be exponentially convergent in the absence of output disturbance. The sufficient conditions include a usual persistent excitation condition as well as an explicit upper bound on the admissible sampling time. In presence of nonzero disturbance, the L∞-gain between the disturbance input and the (state and parameter) estimation errors is explicitly established in function of the design parameters.

A new model is developed for (uniform air-gap) induction motor that accounts for the saturation feature of the machine magnetic circuit. It is built-up starting from the basic electrical/mechanical laws and turns out to be quite different from the standard model that is widely used in control oriented literature. Specifically, the new model involves state-dependent parameters. An experimental validation using a real 7.5 kW machine proved the good accuracy of the proposed model.