Keywords: Linear and nonlinear systems, Mathematical systems theory
Abstract: This paper deals with the Finite-time stability (FTS) analysis for a special class of hybrid system, namely PieceWise Linear system (PWL). Indeed, this issue is relevant since it is highly difficult to study the stability in the sense of Lyapunov for this class of systems. Moroever, this issue was hardly adressed in the literature. The analysis is achieved through a Lyapunov-like approach and provides a sufficient condition for the FTS of PWL system. The S-procedure was used in order to limit its conservativeness. These consditions are given in terms of Linear Matrix Inequalities (LMI) that are easily tractable. A numerical example is presented to illustrate the advantages of the proposed methodology.

Keywords: Linear and nonlinear systems, Multivariable control, Optimization
Abstract: This paper deals with controller design of discrete-time linear systems subject to magnitude and rate-saturated actuators. Thereby, oscillations are minimized by a pole placement within a convex approximation of the cardioid contour of constant damping. Stability is ensured by a quadratic Lyapunov function, whereby the nonlinear twofold actuator saturation is included in a convex hull. To design different controller types, a new iterative LMI method is presented. As an example, an observer-based structured state feedback is derived and the results are demonstrated and compared on two examples.

Keywords: Linear and nonlinear systems, Optimal control, Fuzzy and neural systems
Abstract: The stabilization problem for a class of singular Takagi-Sugeno fuzzy systems (SFSs) is addressed in this paper. Sufficient conditions on stability and stabilization via static state feedback (SSF) controller are presented in terms of strict linear matrix inequalities (LMIs) for T-S fuzzy models utilizing the Lyapunov method and specific slack matrices. To verify the effectiveness of the suggested method, we apply it to a given simulation example.

Keywords: Linear and nonlinear systems, Robust control and Hinfty control, Optimal control
Abstract: This work concerns the problem of robust performance by analyzing the H∞ norm of one−dimensional (1D) uncertain linear time−invariant systems with external disturbance (noise) while the uncertainty is described as a polytope. The contribution of this work consists of giving a more general methodology, by combining Lyapunov function with Finsler’s lemma technique. The objective is to develop a new sufficient conditions by checking an optimal H∞ performance of uncertain system using a number of Lyapunov functions with derivative of higher−order of the state vector in continuous−time (difference of higher−order of the state vector in the discrete−time), moreover some slack and free matrices to eliminate the product between state matrix and Lyapunov matrices, these conditions in this paper are providing in terms of a set of linear matrix inequalities defined at each vertex. Finally, numerical examples are used to demonstrate the benefits of this proposed methodology.

Keywords: Linear and nonlinear systems, Time-delay systems
Abstract: The paper focuses on a one-dimensional wave equation subject to second order dynamic boundary conditions with source terms on a Hilbert space. The purpose is the regulation of the wave velocity toward a given constant by the means of a Proportional-Integral action. The regulation problem is reformulated as a stability problem over an attractor which is not bounded in the state space. The asymptotic stability of which is established using La Salle invariance principle in Hilbert space. In order to proceed, this paper proves that the evolution problem associated with a linear maximal operator (or equivalently linear C_0-semigroup of contraction) gives precompact trajectories. This result is used on the considered wave on the state space-attractor quotient which is proven to be a Hilbert space.

Keywords: Mathematical systems theory
Abstract: We consider a bipartite open quantum system constituted by two interacting qubits A and B, assuming that the former is coupled to the environment and is directly affected by coherent control, while the latter does not interact directly with the environment and the control fields. We are interested in the controllability properties of the subsystem B

In this paper, we give a first analysis of the problem and provide some negative answers.

Keywords: Modeling and simulation
Abstract: An investigation has been computationally carried out to study the effects of intake hydrogen enrichment on combustion characteristics and engine performance in a light-duty diesel engine. The combustion of H2-diesel has been modeled using a reduced chemical mechanism consisting of 76 species and 349 reactions. In order to maintain the engine operating conditions at a constant speed and load, the addition of the H2 rate has been accompanied by the decrease of diesel fuel mass injected in each cycle. This has been accomplished by substituting 20% of the diesel fuel mass for every 10% H2 addition. Moreover, different Exhaust Gas Recirculation (EGR) strategies have been applied to control the formation of NOx emissions. In this study, the effects of hydrogen additions of 0%, 10%, 20%, 30%, and 40% at constant engine operating speed with EGR rates from 0-40% have been studied. The Computational Fluid Dynamics (CFD) results have revealed that, in a constant EGR rate, when the H2 addition was increased up to 30%, the in-cylinder pressure gradually increased, and then decreased when the hydrogen addition was increased to 40%. It has been found that the application of an EGR strategy accompanied by a proper H2 addition is an effective in-cylinder technique for controlling the NOx and soot emissions while maintaining the engine operating conditions.

Keywords: Fault detection and Diagnostics, Renewable Energy, Intelligent and AI based control
Abstract: Photovoltaic systems are susceptible to a range of component-related failures and environmental disturbances, which have an impact on their regular operation and cause significant energy loss. To solve such issues, fault-tolerant control (FTC) techniques have been applied to control and supervise the PV system state and notify the user when any unexpected change in the systems normal operation occurs. In addition, finding the optimal operating point of a PV system in the presence of challenging environmental circumstances has been made possible through a variety of maximum power point tracking (MPPT) techniques. The goal of this work is to propose an FTC method for setting up the MPPT controller to seek for the sub-optimal point of operation. The deep reinforcement learning approach is the basis of the MMPT controller. The proposed FTC approach has good performance in the presence of diverse fault types, according to simulation results on Matlab/Simulink.

Keywords: Control applications, Linear and nonlinear systems, Optimization
Abstract: This paper deals with the problem of finite fre-quency (FF) static output feedback (SOF) H∞ controller design for the urea-based selective catalytic reduction (SCR) system in Diesel engines based descriptor approach to reduce nitrogen oxides (NOx) emissions. The state space model of the urea-SCR system in Diesel engines is firstly, linearized around an average operating point of the urea-based SCR systems. Then, the objective is to regulate the concentration of NOx,out at outlet of a catalyst in the SCR model to desired reference concentration by controlling the concentration of NH3,in at inlet of a catalyst. By applying the generalized Kalman Yakubovich Popov (gKYP) lemma and Lyapunov functions, sufficient conditions are established to ensure both the good reference trajectory tracking and the prescribed H∞ performance of the concentration of NOx,in at inlet of a catalyst variation. Numerical simulations using nonlinear urea-SCR system of Diesel engines is provided to illustrate the performance of the developed approach.

Keywords: Fault detection and Diagnostics, Linear and nonlinear systems, Modeling of complex systems
Abstract: This paper deals with the problem of fault detection and state estimation in an observer-based controller framework for 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.

Keywords: Linear and nonlinear systems, Estimations and identification
Abstract: This paper studies an Event Triggered Control (ETC) based on an interval observer for linear discrete time system subject to unknown bounded disturbance and measurement noise vectors.First, the analysis of the interval observer is presented. Then an Event triggered Control is designed using the two bounds of the interval observer to maintain system stabilization. The observer and ETC gains are calculated based on Lyapunov theory and convex conditions are formulated in Linear Matrix Inequality (LMI) form. Finally, the approach is illustrated by a numerical example to prove its effectiveness.

Keywords: Autonomous Ssystems, Control applications, Optimization
Abstract: This paper presents a Distributed Model Predictive Control approach for UAVs trajectory optimization with the state-dependent collision avoidance criterion. While classical trajectory tracking optimization criterion ensures accuracy of arrival points for the multi-agent system, risk of collision is evaluated at every optimization step, and collision avoidance is included or neglected in the cost function accordingly. Proposed solution is finally evaluated in simulation tests with two scenarios, intersecting and parallel paths of two UAVs. It is, therefore, applicable for different planned path configurations.

Keywords: Autonomous Ssystems, Optimal control, Time-delay systems
Abstract: This paper discusses the problems of stability analysis for a class of discrete-time switched systems with time-varying delay in which both arbitrary switching signal and Average Dwell Time (ADT) switching are considered. New sufficient conditions guaranteeing the exponential stability of the studied system are proposed and developed by constructing an appropriate multiple Lyapunov-Krasovskii functional, via an arbitrary switching law firstly and with average dwell time switching in the other hand. Furthermore, the discrete Wirtinger-based inequality approach is employed with an improved Reciprocally Convex Lemma in order to develop our method and to improve less conservative results over than some recent works. The obtained conditions are established in a form of a set of strict linear matrix inequalities (LMIs). Finally, numerical examples are given to illustrate and to demonstrate the effectiveness of the proposed method and to compare the obtained results with some previous works in the literature.

Keywords: Modeling and simulation, Control algorithms implementation, Modeling of complex systems
Abstract: The recent mobility and motion mega-trends push the automotive industry towards the development of partially- and, in the nearby-future, fully-autonomous drive systems. In the context of the autonomous driving, the systems availability is mandatory and, thus, the redundancy becomes a must. In this situation, highly redundant and cost-efficient new designs are necessary to be developed. This paper starts to address these topics by providing an appropriate theoretical study, supplemented by the mathematical modeling and simulation of a high-performance strategy for controlling an electric actuation system during both Normal- and especially different Fault-Mode Operations (NMOs & FMOs). A highly redundant Motor Control Algorithm (MCA) was specifically developed for various faults compensations. This MCA is based on the Field-Oriented Control (FOC) method, which, combined with a specifically adapted Space Vector Modulation (SVM) technique, provides optimum control signals which diminish the impact of the failure(s) and permit the function of the system in a limp-mode operation, without disabling it or interrupting its operation. The proposed system uses as the main actuator a specifically designed Brushless Direct Current Machine / Motor (BLDCM) provided with five Stator’s Phase Windings (SPWs). The proposed fault tolerant concept may be easily implemented into the servo-steering, servo-brake and (if scaled) traction systems, of both automated and autonomous vehicles.

Keywords: Control applications, Estimations and identification, Modeling of complex systems
Abstract: Microalgae are widely studied as a promising solution for the challenges raised by the sustainable development. Monitoring these kind of cultures is a hard task due to the cost of the physical sensors and the lack of reliable ones for some biochemical variables, especially for the online measurement of biomass concentration. This paper proposes an observer for the biomass concentration based on the online measurement of the dissolved oxygen in the culture.

An Hybrid Observer is developed, by the combination of a Kalman filter and an asymptotic observer. The performance and tuning of the Hybrid observer is discussed and highlighted in simulation in the case of a continuous culture of the microalgae C. reinhardtii, performed in a closed photobioreactor.

Keywords: Control applications, Guidance and control theory, Multivariable control
Abstract: Basic L1 adaptive controller [1] is considered to be an improved controller as compared to the traditional Direct and Indirect MRAC (Model Reference Adaptive control). It is proposed to take advantage of the new B control method [2,3] which exhibits improved performances in the time and the frequency domain and integrate these methods. Preliminary comparisons of the transmissions of output to input, as well as to input and output plant disturbances show a definite improvement when integrating L1 control and B control which we name BL1 control.

Keywords: Control algorithms implementation, Linear and nonlinear systems, Control applications
Abstract: In this work, we provide new time-varying feedbacks which are stabilizing in small-time Brockett’s integrator. Construction of these feedbacks is based on the uniform persistence excitation (used in [33], [34] ) and the homogeneity conditions. To show high performances of the proposed feedback laws, simulations results are provided in the last section of this work.

[33] A. Loria, E. Panteley, and K. Melhem. UGAS of skew-symmetric time-varying systems: Applications to stabilization of chained systems. Eur. J. Contr., 8:33–43, 2002. [34] A. Loria, E. Panteley, and A. Teel. A new persistency-ofexcitation condition for UGAS of NLTV systems: Application to stabilization of nonholonomic systems. In Proc. 5th. European Contr. Conf., (Karlsrhe, Germany), volume 5, pages 1363–1368, 1999.

Keywords: Fault detection and Diagnostics, Control applications, Real time systems
Abstract: A novel fault detection method is presented in this paper. The proposed method can be considered as an extension of the model-free control which is based on an ultra-local model. The model-free controller has demonstrated a great ability to perform the control tasks despite the presence of a fault. This undeniable advantage can mask the effect of a fault resulting in an abnormal degradation impacting one of the components ensuring the control loop or the system itself. The proposed method allows coping with this type of event rapid coping. The basic idea of the diagnostic method is to reconstruct the measured output of the system from the ultralocal model used for system control. The estimated output is compared with the measured one to generate a residual which is employed as the fault indicator. An experimental validation of the polymer electrolyte membrane fuel cell (PEMFC) system is carried out in real time to detect a a system fault that results in membrane flooding. The main advantage of the method is to be able to simultaneously control the system and detect faults that may affect it without needing an accurate knowledge of the mathematical/physical system model. The results obtained are very promising for real-time diagnosis of the polymer electrolyte membrane fuel cell systems.

Keywords: Fault detection and Diagnostics, Renewable Energy
Abstract: A novel diagnosis approach for proton exchange membrane fuel cell (PEMFC) systems is proposed in this paper. Different fault conditions can be classified based on the patterns of stack voltage fluctuation, which can be extracted by the autoregressive model (AR model). The proposed method focuses on the stack voltage fluctuation over time, thus it is more practical and less complex as only the stack voltage needs to be collected. The AR model is employed to extract voltage fluctuation features, and then several widely applied classifiers are applied to classify fault conditions. Experiments are carried out to demonstrate the effectiveness, including 9 single-fault conditions and 8 multi-fault conditions. Those faults are introduced by the adjustment of anode stoichiometry, cathode stoichiometry, relative humidity level, and the cooling circuit temperature. The diagnostic accuracy for single-fault conditions is 99%, while it is 93% for multi-fault conditions. Also, compared to the singularity analysis method in our former research, the proposed method is more time-saving. Moreover, the voltage sampling frequency and sample window length are adjusted to research the diagnosis effectiveness, which is studied and discussed for the first time.

Keywords: Networks optimization, Optimization, Power systems
Abstract: One of the key points in the development of fuel cell technologies is to increase their lifetime. For this, it is necessary to be able to detect a degraded state in order to correct it, but also to be able to estimate the future state, which can facilitate predictive tasks such as control and maintenance. Echo State Networks are good candidates to estimate the future performance of proton exchange membrane fuel cells. They are part of the reservoir computing family and process time series using a neural reservoir. This paper proposes a method to predict the future voltage of a low-temperature proton exchange membrane operating in a steady state. For this purpose, several bidirectional reservoirs are used in parallel which improves the captured dynamics but also reduces the reservoir optimization effort which is currently one of the limitations in the development of Echo State Networks. The forecasting performance is quantified in three different ways: First, to simulate the temporal evolution of a system, the network is trained by varying the size of the training base. This allowed observing that the network can correctly capture the degradation dynamics while having an adaptation capacity when new data are added. Secondly, a comparison with a unidirectional ESN shows that bidirectional reservoirs will more easily capture non-periodic recovery and noise phenomena. Lastly, a comparison with 3 stacked bidirectional long-term memories shows that the proposed approach provided similar results while facilitating the optimization due to its low number of parameters to be set.

Keywords: Estimations and identification, Control applications, Renewable Energy
Abstract: For a correct management of a PEMFC fuel cell (Proton Exchange Membrane Fuel Cell) , it is necessary to incorporate the degradation phenomenon of performance in the control decisions, which is the reason why it is important to be able to quantify its performance throughout its useful life, because ignoring degradation has a direct impact on the system. Therefore, in this work, heat produced during the reaction is proposed as another indicator of degradation. For this purpose, experimental results will be presented that consider the operating conditions and the electrical and thermal energy of a PEMFC stack at two stages of its lifetime. The relationship between the decrease in stack voltage, which is often used as a predictive tool, and the increase in heat generated during the reaction will be demonstrated experimentally.

Keywords: Power systems, Fuzzy and neural systems, Control applications
Abstract: The typical performance of the fuel cell based systems varies greatly with its operating conditions, thereby necessitating the inevitable inclusion of maximum efficiency point tracking (MEPT) controllers. In context, the aim of this research work is to design and investigate the performance of the Fuzzy Logic Controller (FLC) to track the maximum efficiency point (MEP) region. This technique is implemented on a real-time small-scale fuel cell emulator based test bench. The experimental results demonstrate the validity of the FLC algorithm in rapid tracking of the MEP. However, parameterization of membership functions requires a thorough knowledge of the system. Furthermore, the response remains oscillating around MEP, which can degrade the FC. The outcomes of this study testify the applicability of FLC based MEPT controller on a real fuel cell system.

Keywords: Estimations and identification, Autonomous Ssystems, Fuzzy and neural systems
Abstract: In this paper, we present a methodology that ensures a priori that all possible unknown dynamics of the system within a compact set of operation will be excited. A controller is used to make sure that the system with unknown dynamics will follow the reference trajectory and Radial Basis Function (RBF) neural networks are employed to estimate the unknown nonlinearities. The persistency of excitation condition is guaranteed as a prerequisite to achieve accurate estimation of the unknown nonlinear terms and efficient learning. A simulation example clarifies the proposed approach and verifies the aforementioned assertions.

Keywords: Estimations and identification, Fault detection and Diagnostics, Linear and nonlinear systems
Abstract: The faults can be classified into additive and multiplicative according to their effects on the outputs of the system and its dynamics. Although component faults and some actuator/sensor faults appear in multiplicative form that correspond to parameter changes in the system model or a loss of precision in the actuator/sensor, which can lead to additional complexity in observer design in the estimation framework, due to parameter changes within the process. In a nonlinear system, it is not easy to distinguish the factor of the real multiplicative fault from the mixed term. Most of the work in the literature on the estimation of faults has paid attention to those with additive effects that lead to changes only in the mean value of the system output signal. This motivated us to develop a proportional integral unknown input (PIUI) observer for uncertain systems with multiplicative sensor faults in the presence of measurement noise. Based on the TS structure and the properties of the diagonal matrix of the multiplicative faults, we transformed the multiplicative effect into an additive effect to facilitate the estimation. The application of the proposed strategy will be illustrated for a non-isothermal CSTR continuous stirred tank reactor.

Keywords: Estimations and identification, Fault detection and Diagnostics, Optimization
Abstract: This paper presents leak detection and localization in the pipeline system. We proposed the Finite Memory Observer (FMO) because it has finite-time convergence. We have realized the leak detection in the pipeline system and proven it. To detect and localize the leak, we presented the scheme with two FMOs coupled with Luenberger Observer (LO). The first FMO (called FMO detection or FMO₁) is used to detect the leak, and if a leak is detected, then LO is utilized to have fast leak localization and amplitude estimation of the leak. At each time, the leak localization and amplitude become time-varying. The take into account of time-variant is beneficial and utilized to feed the second FMO (called FMO localization or FMO₂) to update leak localization adaptatively. In other words, FMO forced the LO to upgrade its capability. The update continuously happened to both of the coupled observers, so leaks were detected and located accurately in finite time. The effectiveness and robustness of the proposed schemes are both analyzed via the simulation

Keywords: Robotics, Estimations and identification, Control applications
Abstract: The objective of this work is to simultaneously control and identify the nonlinear longitudinal dynamics of small-scale fixed-wing Unmanned Aerial Vehicles (UAVs). The main difficulty in this endeavor lies in the satisfaction of the Persistence of Excitation (PE) condition, which eventually ensures accurate learning. Towards this direction, our key components comprise Radial Basis Function - Neural Networks (RBF-NNs), which are suitable mathematical models for universal function approximation, alongside with: i) the recently developed Dynamic Regression Extension and Mixing (DREM) technique; a new procedure for designing parameter estimators with enhanced performance, as well as ii) a novel control design for the longitudinal UAV dynamics utilizing the Prescribed Performance Control (PPC) methodology, which enables robust trajectory tracking with predetermined transient and steady state quality, even in the presence of model uncertainties.

Keywords: Optimization, Linear and nonlinear systems, Renewable Energy
Abstract: Nowadays, the word knows an increase in energy demand and a scarcity in energy sources. Renewable energy sources are promising alternatives in the actual conditions. Particularly, several efforts are spent in system sizing and energy management strategies’ development to make application as much profitable as possible in terms of autonomy and benefits. Genetic algorithm is widely used in these fields and demonstrated high performances in optimization processes. On the other hand, linear programming algorithm is known by its simplicity and its high performances in energy management strategies optimization. In this paper, we propose a combined model composed by genetic algorithm and linear programming method to optimize both the sizing of the installation and the energy management strategy optimization.

Keywords: Optimal control, Modeling and simulation, Linear and nonlinear systems
Abstract: This paper presents a nonlinear model of the parallel-type double inverted pendulum system with an elastic spring that produces opposing torques on the pendulums. The effects of solid and viscous frictional forces are considered in the developed model. The stabilising control of the nonlinear system is investigated by designing an offset-free model predictive control (OfMPC) scheme based on the linearised model. The use of OfMPC makes it possible to optimize the system performance while also ensuring that the physical limits of the system are not exceeded. Numerical simulation is used to show the effectiveness and prospective benefits of using the proposed controller over the conventional method.

Keywords: Fault detection and Diagnostics, Renewable Energy, Power systems
Abstract: Photovoltaic (PV) generators generally exhibit high power production losses due to their non-linear and highly variable characteristics and also to unstable weather conditions. The aim of maximum power point tracking techniques is to optimise the energy transfer and ensure maximum power production from PV generators with respect to their different characteristics. Besides, and in order to ensure high system reliability, a switched model-based fault detection strategy is developed, which is able to supervise online the health state of the different MPPT control unit components. The detection unit parameters are calculated off-line using the linear matrix inequalities (LMIs) concept, the Lyapunov approach and the H_infinity technique.

Keywords: Fault detection and Diagnostics, Signal processing
Abstract: Anomaly detection is an imperative problem associated with emerging fields such as the Internet of Things, Telecommunication and Manufacturing Industries. Timely detection of anomalies and attribution to its sources enables to enforce preventive measures so as to maintain optimal process operation and avoid undesirable down-times. In this work, we propose an unsupervised method which enables detection, quantification and diagnosis of amplitude anomalies from multivariate data. The proposed method utilizes the ideas of sparse optimization to effectively decompose the data contributions into with and without anomalies for the purpose of anomaly detection and quantification. Further, the ideas of the directed graph are implemented on the estimated textit{anomaly-free data} to determine the root cause of anomaly to enable preventive maintenance. The efficacy of the proposed method is demonstrated on three case studies comprising of synthetic and real-time datasets.

Keywords: Intelligent and AI based control, Estimations and identification
Abstract: The knowledge of the states of a vehicle is a necessity to perform proper planning and control. These quantities are usually accessible through measurements. Control theory brings extremely useful methods -- observers -- to deal with quantities that cannot be directly measured or with noisy measurements. Classical observers are mathematically derived from models. In spite of their success, such as the Kalman filter, they show their limits when systems display high non-linearities, modeling errors, high uncertainties or difficult interactions with the environment (e.g. road contact). In this work, we present a method to build a learning-based observer able to outperform classical observing methods. We compare several neural network architectures and define the data generation procedure used to train them. The method is evaluated on a kinematic bicycle model which allows to easily generate data for training and testing. This model is also used in an Extended Kalman Filter (EKF) for comparison of the learning-based observer with a state of the art model-based observer. The results prove the interest of our approach and pave the way for future improvements of the technique.

Keywords: Intelligent and AI based control, Estimations and identification
Abstract: Starting from a data set consisting of input-output measurements of a dynamical process, this paper presents a training procedure for a specifically control-oriented model. The considered dynamic model adopts a particular neural state- space representation: its structure guarantees its linearizability by state feedback. Moreover, the linearizing control law follows trivially from the parameters of the learned model. The method relies on a parameterized continuous-time neural state-space model whose structure is inspired from well-known exact linearization. The feasibility and efficiency of the approach is illustrated on a nonlinear identification benchmark, namely the Silverbox one. The quality of learning and linearizing feature of the control design are validated on two nonlinear models by comparing the input-output behavior of each closed-loop and its best linear approximation.

Keywords: Renewable Energy, Signal processing, Intelligent and AI based control
Abstract: We study a NILM (Non intrusive load monitoring) problem by considering the classification in two steps: a first step centered on the short duration signal of an electrical device (around 1 second) and a second step centered on the sequence of operating modes of this electrical device (separated by several seconds). The second step is mainly based on an RNN (recursive neural network) and will be described in future work. In this article, we study the first step which consists of a simple classification of certain electrical devices in stationary mode. We show that two parameters and some harmonics constitute a characteristic set of features and we look for a minimal set of features that preserve good results. We compare (both theoretically and practically) the meaning of odd and even harmonics. We illustrate our results using the PLAID database and some of our own electrical devices, in order to validate our real-time embedded system.

Keywords: Optimal control, Optimization, Fuzzy and neural systems
Abstract: This paper is concerned with the problem of finite-frequency (FF) model reduction design for continuous two-dimensional (2D) system described by Roesser model in Takagi-Sugeno (T-S) fuzzy model. The objective is to design a reduced-order model in FF area able a to reproduce the behavior of the 2D T-S fuzzy original system with comparatively small and minimized H_{infty} performance. By utilizing the extended generalized Kalman Yakubovich Popov (gKYP) lemma, sufficient conditions for the existence of the FF fuzzy reduced-order design approach are formulated as feasability of a set of Linear Matrix Inequalities (LMIs). Finally, an example is provided to check effectiveness of the derived results.

Keywords: Control of telecommunications systems, Time-delay systems, Linear and nonlinear systems
Abstract: Active Queue Management (AQM) for mitigating Internet congestion has been addressed via various feedback control syntheses, among which P, PI, and PID regulators are quite popular and often associated to a Smith predictor. Here, to better account for the many uncertainties, like the round trip time or the number of TCP sessions, the above controllers are replaced by an intelligent proportional controller associated to Model-Free Control (MFC) and by forecasting techniques derived from a new approach to time series. Several computer simulations via a well accepted linear modeling, where the delay is assumed to be constant, are presented and discussed.

Keywords: Time-delay systems, Control applications
Abstract: This manuscript presents an observer-based control for the compensation of the input delay in linear systems including pointwise and distributed delays, for which the smoothed spectral abscissa plays an important role in the tuning task. Specifically, tuning the state feedback gains of the observer-based control by optimizing the smoothed spectral abscissa, minimizes the mathcal{H}_2 norm of the corresponding closed-loop system. Furthermore, tuning the observer gains by optimizing the smoothed spectral abscissa of the dynamic observer errors, ensures a fast convergence of the observer-based control to the future state. The proposed controller is applied to the stabilization of combustion in a rocket motor chamber.

Keywords: Time-delay systems
Abstract: Integral delay equations (IDE) have applications in population dynamics and epidemics, they simplify mathematical models and allow tracking the reproductive number which is used to predict how the epidemic is progressing and is a good starting point for implementing actions to reduce the number of infected people. We represent diseases with two IDE the first one with a single delay and the second one with multiple delays and pointwise constant kernels. We use necessary and sufficient stability conditions based on the delay Lyapunov matrix to analyze the stability of IDE when the reproductive number is in matrix form where the elements of such matrix represent the interactions between different groups of the population, we show that the stability results for both IDE are the same.

Keywords: Optimization, Modeling and simulation, Robotics
Abstract: The goal of the proposed work is to develop an algorithm to optimize a manipulation task in industrial pick and place process. A proposed algorithm for stable grasping is presented. It takes into consideration the number of robotic hand fingertips, an Finite Element Model (FEM) generated by a 3D CAD system and restricted areas (RA) that cannot be reached. The grasping configuration is determined under several criteria that ensure the object stability. One criterion is based on Static Stability Margin (SSM) that takes into consideration the position of object center of mass in the polygon of support and a second criterion based on the Force Closure Grasping (FCG) taking into consideration the contact forces applied by the robotic hand fingertips. The proposed optimization problem is based on Genetic Algorithms. The simulation results of the proposed work, applied to an industrial product, are presented for different number of contact points.

Keywords: Robotics
Abstract: Risk assessment is a very subjective process and relies heavily on the expertise of the person conducting it. Especially for HRC applications, the implementation is challenging, as the target group of HRC, the small and medium sized enterprises, often do not have the necessary capacity to develop the expertise. In this work, 25 experts in the field of risk assessment were interviewed to determine their approach and experience in risk assessment. Based on the results, a generic risk assessment for HRC applications is proposed, but also discrepancies and conflicting opinions of the experts were identified.

Keywords: Manufacturing systems, Mechatronics, Robotics
Abstract: The Wood construction industry has integrated the challenges of robotics and introduced the use of five-axis machining for more than 30 years. However, the wood dust and chips are often contaminated with different chemicals which causes health risks. In this paper, we present an experimental analysis of the physical properties of chips by observing the motion behavior with a single high-speed camera. The experiments are led with an industrial gantry robot during the grooving process. The results show that we can estimate and quantify the velocity direction of wood chip emission in a range of 5m/s near the tool-material interaction. We also constructed two suction hoods and we experimentally show that chip collection is better when the tube is tilted near the tool.

Keywords: Robust control and Hinfty control, Estimations and identification, Networks optimization
Abstract: An H∞ state observer-controller for the two-dimensional system Marchesini-Fornasini second model (FMII) is addressed by considering the Round- Robin (R-R) protocol based on the quantization pro- cess. Additionally, the under-consideration system features uncertainties of norm-bounded type, which are managed by two stochastic variables fulfilling the Bernoulli distribution. The R-R protocol is a sort of static scheduling, it grants communication access rights to sensors in chronological order. The objective of this letter is to design an H∞ observer-controller that guarantees the mean square stability of the 2-D error system. Sufficient conditions are derived for H∞ performance analysis and design by involving some auxiliary variables that lead to reduced conservatism. Thereafter, a linear matrix inequality is proposed to allow a one-step design of the observer-controller gains. In the end, an illustrative example borrowed from literature is suggested to highlight the efficiency of the proposed approach.

Keywords: Robust control and Hinfty control, Linear and nonlinear systems, Multivariable control
Abstract: This paper aims at designing an adaptive robust observer-based feedback controller for uncertain and saturated discrete-time linear systems. The uncertainties and the saturation are included in convex hulls and a Lyapunov function is used to ensure stability of the closed loop. By using an iterative algorithm, the controller and observer gains are computed simultaneously using LMI techniques. The method derives a sequence of controllers with increasing gains when the control errors decrease. In this context, two methods for time-varying controllers are presented: a switching algorithm and an interpolation method. The parameter settings and the control performance are discussed using a numerical example and a payload crane system.

Keywords: Networks optimization, Optimal control, Optimization
Abstract: This paper presents a distributed Transactive Energy Control (TEC) model for energy management purposes in distribution networks. The shortages of centralized and decentralized methods have been addressed in this paper, wherein the system operator plays the role of coordinator to not only oversee the system constraints but also maintain the system privacy. Prosumers also can solve their own energy management problem separately without secure data sharing. To solve the proposed model, the adaptive Alternating Direction Method of Multipliers (ADMM) method is utilized in which the penalty factor is specified based on optimality and feasibility condition in each iteration. The implementations express that the proposed model can improve the accuracy and speed of convergence by 40.36 and 49.99 percent compared to regular ADMM.