Keywords: Linear and nonlinear systems, Estimations and identification, Time-delay systems
Abstract: In this paper a novel synthesis of an observer for a class of non-uniformly observable disturbed systems is proposed. The outputs of the systems are delayed with a known (time-varying) delay and their measurements are continuously available over some time horizons and sampled over other ones. The observer design is firstly detailed in the case of a small time-varying delay before being extended to the case of long delays. The performance of the proposed observer is applied to a chaotic uncertain system.

Keywords: Autonomous Ssystems, Control applications, Estimations and identification
Abstract: Consider the observation problem of bidimensional systems which requires the construction of an embedding in higher dimension. A multi-observers approach has been recently introduced by the authors to deal with a class of such singularly observable systems. This approach does not require any coordinates transformation. In this work we illustrate through an academic example the applicability of this approach.

Keywords: Fault detection and Diagnostics, Power systems, Optimization
Abstract: In this paper, the concept of exact recovery under sparse fault assumption is applied to the diagnosis of three-phase DC-AC power electronic inverter. In order to apply the proposed algorithm, it is first necessary to have a dynamic modeling without and with each occurred fault. After that, roughly speaking, some assumptions (sparsity, Restrictive Isometry Property) are necessary with respect to the influence of fault on the measured signal, in order to apply an exact sparse recovery (SR). The method used in this paper is a finite time technique which takes into account the quality of the measured noisy signal. The paper ends by some simulation results on a case study which highlight the well founded of the proposed algorithm with respect to previous algorithms that did not consider the measurement is noisy.

Keywords: Linear and nonlinear systems, Estimations and identification
Abstract: This paper proposes a prescribed-time varying high gain observer for a class of nonlinear systems. The fixed time convergence of the observer within a predefined time is shown through a Lyapunov differential inequality and a state transformation that involves a scaling function. The effectiveness of the proposed observer is illustrated through two numerical examples.

Keywords: Fault detection and Diagnostics, Linear and nonlinear systems, Power systems
Abstract: This paper is dedicated to the control of a mode changeable DC/DC converter allowing to interface several energy sources and different loads in an integrated topology. The main objective is to propose a control strategy with sensor fault tolerance. The switched system methodology is adopted, and an observer design is proposed to simultaneously estimate the converter state and sensor faults. To show the relevance of the approach, simulation results are given

Keywords: Linear and nonlinear systems, Estimations and identification
Abstract: In this paper, we define observability when the measurements are not time-stamped. In general, observer theory always assumes that the outputs correspond to correctly time-stamped states (although varying delays are sometimes considered). In practice, this is not always the case. The extreme situation is the complete absence of a time stamp. We will therefore propose a definition that we will justify by a few examples. We will try to characterize the rarity of systems with unknown dating but which are nevertheless observable.

Keywords: Stochastic control, Optimization, Estimations and identification
Abstract: In this paper, a problem of adjacency matrix tuning in sensor networks is considered. The object of estimating is linear discrete time-varying system with several measured outputs. Each measurement may be dropout with given probability. The external disturbance belongs to class of zero mean disturbances with positive anisotropy level of extended vector. The goal is to modify initial adjacency matrix with respect to fixed estimation model to ensure minimum value of anisotropic norm for input-to-error system. Contrary to standard Hc_2 and Hc_infty methods, anisotropy-based approach for estimation problem demonstrates more reliable results when stochastic parameters of disturbance are not well defined. The numerical example demonstrates efficiency of supported algorithm.

Keywords: Autonomous Ssystems, Estimations and identification, Embedded Systems
Abstract: This paper compares two nonlinear Kalman filters to estimate a projectile trajectory: an Extended Kalman Filter (EKF) and an Imperfect Right-Invariant Extended Kalman Filter (Imperfect R-IEKF). For this purpose, only an Inertial Measurement Unit (IMU), composed by an accelerometer and a gyrometer embedded in the projectile is considered. In addition, a misalignment between the IMU and the projectile is used as an observation. Both filters share the same evolution and measurement models. A nonlinear observability analysis is performed and suggests that the EKF creates a false observability contrary to the Imperfect R-IEKF. Furthermore, evaluated on 100 mortar fire simulations, the Imperfect R-IEKF is considerably more accurate than the EKF to estimate a projectile trajectory. These observations on accuracy and observability are explained by the Imperfect R-IEKF design, i.e. an EKF based on a nonlinear error and an update step based, in part, on an exponential application.

Keywords: Estimations and identification
Abstract: Two recursive identification algorithms for Finite Impulse Response (FIR) systems are presented in this paper. These algorithms are dedicated to systems with binary value measurements on the output. They are based on a weighted Least Squares criterion. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms in different framework such as the input distribution, the presence of noise or the length of the impulse response.

Keywords: Estimations and identification
Abstract: This paper presents a new identification algorithm of a system which operates in closed-loop using binary measurement in the output. The paper proposes an algorithm for the estimation of the parameters of the system, this algorithm is based on the output error in closed loop. Simulation results are given to show the effectiveness of the proposed approach.

Keywords: Fractional order systems, Estimations and identification, Signal processing
Abstract: The fractional derivative estimation of the output for fractional order systems is investigated in this paper. Firstly, an annihilator is designed in the frequency domain by using the Laplace transform, which ensures the unknown initial conditions can be eliminated. Then, the fractional order differentiator is implemented in an algebraic formula using a recursive way. The proposed fractional order differentiator can estimate the fractional derivative of the output with arbitrary order. Moreover, the estimation error is analyzed in the discrete noisy case. Finally, to illustrate the performance of the proposed method, numerical simulation is provided.

Keywords: Fractional order systems, Mathematical systems theory
Abstract: In this paper, we investigate the boundary stabilization of an unstable time-space fractional diffusion-wave equation with Caputo time fractional derivative and spacial fractional Laplacian operator. Due to the spacial nonlocal operator leading to the difficulty in calculating the spacial fractional derivative of Volterra integral transformation, the classic backstepping method fails to design the boundary controller. To overcome this difficulty, we are base on the state space decomposition so that the system is decomposed into infinite dimensional stable part and finite-dimensional unstable part, then we design a boundary feedback control for the finite-dimensional unstable part, which is shown to actually stabilize the entire system.

Keywords: Fractional order systems, Linear and nonlinear systems, Modeling and simulation
Abstract: In this manuscript, a fractional order model for rumor spreading in mobile social networks (MSNs) is derived from an underlying physical stochastic process, which can be regarded as a heterogeneous-network-based fractional order epidemic model. In the model, the fractional derivative appears because of the time for denying the rumor is power law distributed. Then, the basic reproduction number R_0 depended on the fractional derivative order is calculated. If R_0 < 1, the rumor-free equilibrium is global asymptotically stable, the rumors will disappear. If R_0 > 1, the rumor-endemic equilibrium is local stable, then the rumors will become endemic. Further, the numerical simulation based on the Digg2009 data set demonstrate the effectiveness of the proposed model.

Keywords: Fractional order systems, Estimations and identification
Abstract: This paper proposes an adaptive observer for a class of space fractional partial differential equations (FPDEs) that describes gas pressures in fractured media. The observer is based on a backstepping design and jointly estimate both the system’s state and the unknown disturbance.

Keywords: Fractional order systems, Modeling of complex systems
Abstract: This contribution investigates the extension of the certainty equivalence based model reference adaptive control (MRAC) towards fractional-order parameter update laws. We use the infinite state representation of the fractional-order integral for constructing an integer-order Lyapunov function to derive the parameter update law. This renders the model reference error asymptotically stable. In comparison to the integer-order MRAC the fractional- order approach increases the robustness with respect to un- structured uncertainties and allows for higher adaptation rates due to the algebraic type of convergence. All the results are illustrated with a simulation example stabilizing the benchmark of the wing-rock dynamics.

Keywords: Estimations and identification, Renewable Energy, Power systems
Abstract: The state of health of electrical double layer capacitors (EDLCs) has an important role on the determination of the performance and the safety of an electrical vehicle. The degradation of these components is usually correlated with the evolution of their internal resistance, and their capacitance. Therefore, the estimation of these two parameters is necessary to predict the state of health (SoH) of supercapacitors and to ensure their operational safety in vehicular application. This paper presents a comparative study of supercapacitor parameters estimation between High Gain Observer (HGO) and Extended Kalman Filter (EKF). The performance of each algorithm is evaluated based on the accuracy of solution and convergence speed through simulation and experimental results. -Index terms: Supercpacitors, Observer, State of health, High gain observer, Extended Kalman filter, electrical vehicle.

Keywords: Optimization, Optimal control, Fault detection and Diagnostics
Abstract: Cyber-physical systems are prone to various security threats. Systems must be equipped with robust detection strategies to ensure timely detection of attacks. One such detection strategy proposes adding a random authentication signal to the optimal control signal and uses a chi-squared failure detector on the residues of measurements to detect the attacks. In this paper, we design the authentication signal's covariance matrix to optimize the performance of the chi-squared detector. The covariance matrix is a unit rank matrix and is an outer product of a well-defined vector. This structure maximizes the probability of correctly detecting an attack while maintaining the probability of false alarm in the chi-squared detector. We corroborate our analytical results by employing simulations. The optimal chi-squared setting demonstrates significant performance gain against arbitrary settings.

Keywords: Optimization, Time-delay systems, Modeling of complex systems
Abstract: The dynamics of a large variety of systems such as micro-grids and parallel hybrid cars can be described using finite state machines. The optimal control of these systems leads to mixed-integer non-linear programming (MINLP) problems. This class of problems typically belongs to the non-deterministic polynomial-time hard problems since they combine both the computational intensity of solving problems with discrete variables and the complexity of solving non-linear functions. In the last decade, different techniques are developed to reach a global optimal solution for convex MINLP problems, such as branch & bound, outer approximation or the generalized benders decomposition algorithm. These techniques often have difficulties in efficiently handling the constraints of state machines. These constraints include the minimum time to stay within a state or the minimum cycle time before the same state can be reached again. In order to tackle these issues, the paper proposes a new technique that exploits the properties of a state machine within the branch & bound tree to reduce execution time. This reduction is achieved by separately solving the state machine variables and relaxing the switches between states using sigmoid functions. As a result, the technique reduces the number of explored nodes in the branch & bound tree significantly. The algorithm can be used in an online optimization strategy when employing a model predictive control framework that preserves added constraints from a previous iteration in the next iteration. A numerical simulation demonstrates the computational efficiency of this algorithm.

Keywords: Optimal control, Robotics, Motion control
Abstract: In the framework of Pontryagin's Maximum Principle, the cost function choice is fundamental because it has a direct impact on the system Hamiltonian. This choice naturally affects the resulting controls and the cost function should therefore be chosen appropriately, in accordance with the desired system behavior. We deal with robotic systems for which the governing equations and optimality conditions are commonly affected by numerical stiffness. As such, computing the optimal controls can be difficult for such nonlinear systems. We apply Pontryagin's Maximum Principle to the optimal control of a robotic system and compare the impact of four different cost functions on a commercial ODE solver that determines the optimal solution. The focus is on numerical stability and overall computing time. Two of these selected cost functions are typically found in the literature. We propose alternative forms of these cost functions where the robot mass tensor is embedded. Numerical trials show that the mass tensor acts as a stabilizing factor that leads to: lower link motion amplitudes, improved numerical stability as well as decreased CPU computing times when solving the optimal control of a robotic manipulator. These cost functions can be beneficial for motion planning and optimal control of robotic manipulators.

Keywords: Optimization, Fuzzy and neural systems, Power systems
Abstract: This paper proposes an optimal energy management approach for a hybrid system of a 5G telecom station. In such systems, it is required to satisfy the energy demand by the load and optimizing the utilization cost and the lifetime of energy storage systems. Furthermore, the battery state of charge should be maintained in some predefined domain to achieve its best performance. A fuzzy logic-based energy management approach is developed to deal with these objectives. The proposed approach satisfies the load demand and optimizes the utilization of the energy storage systems while respecting the limits of the battery state of charge. The performance of the proposed approach is illustrated through simulation results

Keywords: Control algorithms implementation, Embedded Systems, Renewable Energy
Abstract: The use of hydrogen fuel and hybridization technology on board aircraft can be seen as a promising option. This allow to evolve aircraft configurations that will be more environmentally friendly. In this paper, in the case of a hybrid energy sources, a strategy for energy management is described specifically in vertical takeoff and landing electric aircraft eVTOLs. Firstly, the H2 quantity needed to perform a planned mission is estimated. This estimated quantity is necessary to optimize the eVTOL mass and also the energy expected to perform a mission. Then, based on the equivalent consumption minimization strategy ECM, an optimal distribution of the power demanded between the energy sources is formulated as a constraint optimization problem. The main idea of this study is to optimize the management of the use of energy sources such that keeping them within their operating ranges and respecting their limits.

Keywords: Fault detection and Diagnostics, Estimations and identification, Robotics
Abstract: This paper addresses the problem of quadrotor sensors fault diagnosis where the vehicle model is expressed in an LPV framework and accompanied by a robust self-scheduled LPV controller which minimizes the quadratic performance H_infty norm. Afterward, a new observer scheme including a virtual residual signal beside an auxiliary output is introduced to achieve quadrotor sensor fault estimation and isolation. Through the synthesis of such an observer gains assignment, some structural conditions are discussed to ensure exact or asymptotic residual to fault convergence followed by an enhanced H_{-}/H_{infty} approach when the decoupling conditions are not satisfied. Finally, the efficiency of the developed approach in fault estimation is demonstrated by applying it in simulation on a quadrotor UAV subjected to sensor faults.

Keywords: Estimations and identification, Modeling and simulation
Abstract: The prediction and observation of the growth, and the peaks reproduction for the Coronavirus are of main importance. In this study, we revisit the oldest deterministic SIR model, show its ability to describe the disease spread and then use it to design some observers to avoid perturbations noise and measurements imperfections. After finite time converging observations an output re-injection is used for parameters estimation. Note that the data are not provided by sensors which are regular in their measurements, synchronized in time and robust versus noise

Keywords: Linear and nonlinear systems, Time-delay systems, Estimations and identification
Abstract: This paper considers the problem of designing functional observers for multi-delayed linear systems. It produces a general presentation and practical method for the design of the observers examined by Darouach in cite{c14}. Based on the Lyapunov-Krasovskii functional theory, the stability conditions are obtained in terms of linear matrix inequalities (LMIs). Sufficient conditions for the stability of the observers with delays are proposed. Constant and variable delays are taken into account. Numerical examples are awarded to show how the results presented in cite{c14} cannot be applied to the system proposed in this paper.

Keywords: Time-delay systems, Multivariable control, Linear and nonlinear systems
Abstract: The paper presents one method from the field of Metzler linear time-delay systems, which is able to handle constraint complexity of the design condition, when synthesising the state control law parameter for this class of systems. LMI-based algorithm variants are proposed in order to derive the controller matrix parameters that guarantee closed-loop system positive performance and stability under the proposed conditions of solvability. In this line of solution the approach reflect particular dynamics properties of the closed-loop system, when guaranteeing desired effects by adjustment of diagonal linear matrix variables. The interest of the contribution is to increase the efficiency and performance of the state control design for strictly Metzler linear time-delay systems. The feasibility of the proposed method is illustrated by the numerical example.

Keywords: Linear and nonlinear systems, Optimal control, Robust control and Hinfty control
Abstract: The paper deals with structured state-feedback controllers design for linear time-invariant (LTI) singular systems. New necessary and sufficient conditions for the existence of a structured state-feedback guaranteeing the closed-loop admissibility is proposed by means of descriptor systems' linear-quadratic (LQ) regulator theory. These conditions are presented in the form a system of matrix equations involving a non-smooth unconstrained equation. Thereby, a non-smooth Newton iterative algorithm, based on the Clarke generalized Jacobian of symmetric real matrices projection on the cone of positive-definite matrices, is presented. This method has a guaranteed local convergence. Finally, numerical examples illustrate the efficiency of the proposed approach.

Keywords: Motion control, Real time systems, Intelligent and AI based control
Abstract: The presence of wind disturbance and model uncertainties are significant problems for quadrotor stable flight and its control performance. In this paper, the intelligent disturbance compensator (IDC) designed with the disturbance accommodating control (DAC) observer and the radial basis function (RBF) neural network compensates for uncertainties such as wind disturbance and modeling error without information on nonlinear terms and input matrix of the mathematical model. The IDC can design adaptive law based on the Lyapunov function and tune the RBF neural network online. The validity of the proposed compensator is shown through numerical simulation.

Keywords: Power systems, Control applications, Embedded Systems
Abstract: This work presents a voltage sensorless predictive direct power control strategy for PWM-rectifier. Firstly, the line-voltages are estimated using Second-Order Generalized Integrators (SOGI) coupled with Frequency Locked Loop (FLL). SOGI are inherent noise rejectors and FLL provides instantaneous tracking of the grid frequency. Their combination offers high robustness against grid disturbances, distinctively: harmonics distortion and frequency fluctuations. Secondly, Model Predictive Control (MPC) with Finite Control Set (FCS) attributes conceptual simplicity and higher accuracy than conventional controllers but suffers from variable switching frequency. In this paper, we propose MPC approach that solves the switching frequency problem while maintaining the accuracy of FCS. Last, the decoupled control between active and reactive powers of Direct Power Control (DPC) offers flexibility of operation over variable power factors. The effectiveness and reliability of the proposed approach are investigated through simulation using MATLAB/SIMULINK software.

Keywords: Power systems, Control applications, Fuzzy and neural systems
Abstract: This paper proposes a cascade control strategy of a floating interleaved boost converter consisting of a signed distance fuzzy logic controller as an outer voltage loop and two sliding mode controllers as an inner current loop. Typically, the fuzzy logic controllers are designed using two-input variables, hence, a high number of fuzzy rules is needed, increasing the complexity and the rules tuning. The adopted signed distance fuzzy logic concept in this paper allows reducing the fuzzy rules number and consequently the tuning complexity. Besides the simplicity of design, many other advantages are offered by the proposed control strategy such as robustness against external disturbance and parameters variation. Computer simulations using Matlab/Simulink software were carried out to validate the effectiveness of the proposed strategy.