Keywords: Robust control and Hinfty control, Control applications, Estimations and identification
Abstract: The vibration testing system of a large structure spacecraft presents inaccurate precision tracking and high oscillations in the neighborhood of its vibrational modes, particularly at higher frequencies. In the presence of varying modal parameters of a spacecraft such as the mode frequency and corresponding damping ratio, the performance of the controlled system degrades. Robust high precision tracking control of such systems with varying modal parameters is rarely addressed in the literature. A new closed-loop system architecture is proposed in this paper, based on a feedforward-feedback tracking control strategy involving an H controller. Simulation results show that the new architecture allows precise tracking of a sine sweep acceleration reference signal avoiding vibrations when sweeping through modes. Furthermore, it appears to be highly robust against varying parameters, model uncertainties, as well as the presence of sensor noise. The proposed controller also limits the control effort to avoid the actuator saturation. A minimal order controller is derived which makes it tractable for further industrial implementation.

Keywords: Robust control and Hinfty control, Industrial control, Modeling and simulation
Abstract: Industrial valves are widely used components for many applications and can be integrated in processes for controlling different physical quantities. However, they encounter system parameter dispersions from a valve to another, its actuator exhibits hysteresis and depending on the applied process, the time response of the controlled physical quantity can vary because of the system’s time response. These phenomena make the control of those systems challenging and limit their performance. In this paper, a Reference Model Sliding Mode Control (RMSMC) controller, which belongs to a class of robust controllers known to deal with nonlinear dynamical systems exhibiting parameter variations and external disturbances, is designed. Process output time responses are mastered regardless of the system large hysteresis and its possible variations by the tracking of dynamical reference model defined by pole placement. The simulations with the synthesized controller provide excellent control performances for a very large range of process time response, as well as robustness to system parameter dispersion.

Keywords: Robust control and Hinfty control, Signal processing, Linear and nonlinear systems
Abstract: The H∞ Static Output Feedback (SOF) Controller Design with finite frequency (FF) specification for Two-Dimensional (2D) Discrete Systems in Fornasini-Marchesini second (FM-II) model is investigated, with the use of the Generalized Kalman Yakobovich Popov (GKYP) lemma. More precisely, a new condition is derived for FF SOF H∞ controller design in terms of Linear Matrix Inequalities (LMIs). This study reduces the conservatism of the existing entire-frequency (EF) design. Some examples are provided to show the effectiveness of the derived results.

Keywords: Robust control and Hinfty control, Time-delay systems, Linear and nonlinear systems
Abstract: This paper presents the static output feedback control synthesis problem for a class of positive polynomial fuzzy systems with time delay using a sum of squares (SOS) approach. To study the considered analysis and design problems, the polynomial Lyapunov-Krasovskii function is proposed. Based on The sum of squares technique, the polynomial conditions can be directly solved. Finally, a simulation example is given to testify the validity of the analysis result.

Keywords: Robust control and Hinfty control, Time-delay systems, Multivariable control
Abstract: This paper deals with the problems of robust stability and stabilization of 2D singular continuous systems with delays and parameter uncertainties. The aim of the robust stability problem is to give conditions such that the delayed 2D singular uncertain system is regular, impulse free, and stable, while the purpose of the robust stabilization is to design a state￾feedback control law such that the closed-loop system is robustly stable. Sufficient conditions are given under strict linear matrix inequality (LMI) conditions. Finally, a numerical example is used in order to show the applicability of the presented results.

Keywords: Autonomous Ssystems, Robust control and Hinfty control, Control applications
Abstract: This paper presents an adaptive attitude control strategy based on a finite-time convergent differentiator for an unmanned aerial vehicle (UAV) using a sliding mode control combined with an auxiliary system with finite time differentiator as an estimator. Initially, the sliding mode control approach is proposed to stabilize the quadrotor by controlling the rotation angles under the external disturbance, uncertainties and actuator saturation compensator. Then, a finite-time differentiator is designed and included in the system feedback to ensure an accurate estimation of the full state even in the presence of noise, to address the parametric selection drawback a fuzzy system is proposed for the purpose of tuning the differentiator parameters. The stability of the overall system is proved by the Lyapunov theory and the simulation results shown the effectiveness of the proposed control are presented.

Keywords: Motion control, Optimal control, Networks optimization
Abstract: The formation control of a team of agents has been addressed by methodologies that bring together systems, graph, and game theories. In contrast to most methodologies based on noncooperative game theory, we propose a cooperative game theory approach for the problem centered on the Nash bargaining solution concept. The formation control problem for a team of agents with double integrator dynamics is formulated as a graphical game with an affine-quadratic structure. After characterizing the Nash equilibrium and the Pareto optimal solutions of the game, the Nash bargaining solution is obtained by a simple numerical procedure. An illustrative example shows the superiority of the Nash bargaining solution over the Nash equilibrium solution in terms of ease of implementation and performance of the team of agents.

Keywords: Renewable Energy, Fuzzy and neural systems, Power systems
Abstract: This study presents a new, efficient Neural Synchronous Reference Frame technique (New-Neural-SRF) for the control of a three-phase PV-integrated a Shunt Active Power Filter system (PVSAPF). This multifunctional model guarantees an uninterrupted power injection from the photovoltaic panels, and/or the AC source according to the irradiance profile variation. Further, the model offers additional services as harmonic attenuation, reactive current compensation and DC-link voltage regulation. The effectiveness of the PV-SAPF compensation is principally related to the control method which first extracts the load harmonic currents, and then generates the compensating reference current. Therefore, in order to enhance the harmonic extraction algorithm and to deal with the limitation of the classical SRF technique, mainly under online varying system parameters, a novel and a simple Neural-SRF controller using a feed forward backpropagation artificial neural network is proposed. The suggested method of control is tested under a MATLAB/Simulink environment. The results validate the robustness of this Neural technique to ensure an accurate and a fast harmonic-extraction, fewer overshoot, a quick response time and lower value of the Total harmonic distortion THD, in accordance with the IEEE-519 standard requirements, under different simulation cases.

Keywords: Renewable Energy, Power systems, Modeling and simulation
Abstract: The concern for the environment and the current economic situation result in the growing search for renewable means of energy generation. However, renewable sources of electrical energy interfaced by inverters do not have the same inertia and damping that exists in synchronous machines. The lack of inertia resulting from the high level penetration of renewable energies can lead to increased frequency excursions and, consequently, to system instability. In order to reduce this unwanted effect, this study, proposes the development of a control strategy to emulate inertia through a grid-following inverter. After simulations in a system with solar photovoltaic energy generation connected to a grid with infinite bus, results show the effectiveness of inverter control with virtual inertia.

Keywords: Power systems, Renewable Energy, Modeling and simulation
Abstract: Synchronous inertia plays a fundamental role in the stability of electrical power systems (PS), as systems with synchronous generators manage to balance variations between generation and load with the storage of kinetic energy. The lack of this parameter might generate imbalance and instability in an electrical system. The aim of this work is to analyse a PS with high penetration of renewable energy sources (RES). Kundur’s Two-Area system is studied for four different conditions: 0%, 25%, 50% and 75% of penetration of renewable sources. It is possible to verify that the lack of synchronous inertia generates a gradual instability in the presented system as synchronous generators are replaced by wind generators.

Keywords: Fault detection and Diagnostics, Renewable Energy, Time-delay systems
Abstract: The problem of active Fault Tolerant Control (FTC) for a Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems with time delays subject to an actuator fault, is investigated in this paper. This new approach consists on constructing an adaptive observer in order to estimate states and to detect faults using a linear parameter varying model firstly and to design a control law secondly. Based on linear matrix inequality (LMI) techniques, the FTC linear parameter-varying (LPV) control scheme is developed for the LPV system.The stability of the proposed FTC via closed-loop system is verified by Lyapunov approach. Finally, the effectiveness of the proposed method is illustrated via simulation results

Keywords: Optimization, Industrial control, Modeling and simulation
Abstract: Dynamic optimization is employed to calculate the optimal operating conditions for grade transitions in polymer production. The optimization is based on a detailed process model of a fluidized bed reactor and polymer properties, as well as a thermodynamic model that accounts for interactions between the different gases. It calculates the optimal flow rates of comonomer and hydrogen that ensure the production of the desired polymer (high density polyethylene HDPE, or linear low density polyethylene LLDPE). Constraints are considered on the inputs. Besides, a constraint is put on the bed temperature to ensure that it remains below the polymer melting temperature. The simulations show that the transition time can be reduced, and the final operating conditions allow producing polymer with the desired properties, without risking polymer melting or sticking.

Keywords: Optimization, Modeling and simulation, Renewable Energy
Abstract: The design of distributed energy systems requires to implement methodologies within the framework of sustainable development. Accordingly, this work deals with the conceptual design of an isolated energy system by analyzing the economic, environmental, and social dimensions of sustainability. The case study corresponds to a remote location in the Colombian Amazon. The design of the energy system was performed through an optimization-based approach. The optimization problem consists in the multicriteria minimization of the total annualized cost, the CO2 emissions, the water consumption, and the inherent safety index. According to the obtained results, it was determined that at least a PV surface of 9800 m2 must be installed to satisfy the electricity needs of the considered community (4160 inhabitants). In this case, the demand of methane should be entirely covered by biomethane from an anaerobic digestion process. Nevertheless, by installing a PV area of 24150 m2, electricity and methane demands could be supplied from PV-based electricity and by using the power-to-gas pathway. In such a case, around 854 ton CO2/year are required for the methanation process, which must be captured from the atmosphere.

Keywords: Estimations and identification, Robust control and Hinfty control, Modeling and simulation
Abstract: In this paper, the robust state estimation problem for a continuous stirred-tank reactor is addressed. Two different robust estimators are studied for the set-membership state estimation. Here, a strip-based observer is compared to a set-propagation observer. In the first case, we consider the classic recursive optimal parallelotopic outbounding algorithm. In the second case, we use a Zonotopic Kalman filter (ZKF). The comparison takes into consideration the CPU time and the estimation accuracy (in terms of the final volume of the estimation set). Our findings indicate that the differences in CPU time are insignificant, yet the strip-based method showed a major reduction in the final reachable set.

Keywords: Intelligent and AI based control, Control algorithms implementation, Control applications
Abstract: In crystallization processes, the control of particle size distribution, shape and purity are crucial to achieve the targeted critical quality attributes of the final drug product and meet the pharmaceutical regulatory requirements. This work presents novel trajectory tracking control strategies for a batch cooling crystallization process using Reinforcement Learning (RL). The cooling crystallization of paracetamol in water was used as a case study. A model-based reinforcement learning technique is implemented to achieve large crystal size by reducing the deviation from 3 targeted reference trajectories namely process temperature, supersaturation and particle size. An off-policy RL method based on a deep deterministic policy gradient (DDPG) algorithm was used. The performance of the RL control was compared and validated against MPC. To address the tracking control problem in in the case of noisy measurements and model uncertainties, the RL approach was combined with a Kalman filter. Finally, Transfer Learning (TL) was implemented to investigate optimal and cost- effective training strategies of the RL agent.

Keywords: Process control and instrumentation, Modeling and simulation, Industrial control
Abstract: Control strategies performances are usually assessed based on well-established indicators. These ones are accurately selected according to the property of the dynamic closed loop response to be optimized. During the last decades, flexibility has become an aspect of major interest in process industry due to the need to deal with market volatility, renewable energy sources and biomass based raw materials. That is why, in the perspective of a unified approach for design [1], scheduling and control, recent studies have coupled flexible process unit and control strategy design. In this research work, an innovative switchability index, recently proposed in literature, is then applied on chemical process case studies with the purpose of assessing the impact of dynamics under uncertain operating conditions and test different control strategies from a flexibility point of view. The outcome of this study successfully proves that the provided indicator is suitable both for the comparison of different control strategy for a given design from a dynamic flexibility perspective and the assessment of the required unit oversizing for a given switchability target.

Keywords: Control applications, Modeling of complex systems, Estimations and identification
Abstract: This paper deals with an observer design methodology that allows estimating the nucleation rate of particles from the in-line solute concentration measurements in crystallisation processes. The nucleation rate is used to calculate the time variations of the overall number of crystals in suspension, which is necessary to on-line estimate the CSD. To do so, the population balance equation describing the dynamic evolution of the CSD is reduced to a system of ordinary differential equations. The state variables of the system are then defined by the moments of the population of crystal sizes. The reduced model is then used as a basis for the design of an estimator of the nucleation rate with time. Two types of observers are compared: Kalman filter and Luenberger like observer. This methodology is finally evaluated through a simulation study based on real experimental process data.

Keywords: Fault detection and Diagnostics, Estimations and identification
Abstract: This paper deals with dynamics estimation and fault detection of unmaned aerial vehicles (UAV) using the Upper Bound Interval Kalman Filter (UBIKF). An upper bound for all positive semi-definite matrices included in an interval matrix is calculated. This upper bound is used to generate envelopes for the variables to be estimated which are the dynamics of the UAV. It allows to provide a guaranteed estimation envelope for the considered dynamics. Then, the fault detection scheme is used based on a Chi-2 test. The faults concern sensors and actuators. Simulations on a discrete uncertain UAV model highlight the efficiency of the proposed filter for both UAV dynamics estimation and fault detection.

Keywords: Linear and nonlinear systems, Estimations and identification, Fault detection and Diagnostics
Abstract: This paper proposes a soft sensor to estimate the elementary fuel characteristics in combustion-thermal power plants. The proposed approach is data-driven. The input-output data is generated by a digital twin. Application targets circulating fluidized bed boiler, where furnace (combustion) side is considered only. First, the nonlinear dynamics of the furnace is approximated with a linear time-invariant dynamic model. Then two separate methods, Kalman filter and internal governor, are applied for state estimation. Results show that the approach is viable and has low computational complexity, but the weakly observable modes are difficult to predict accurately.

Keywords: Fault detection and Diagnostics, Modeling and simulation
Abstract: The paper presents a strong and realistic model for induction motors including space harmonics of magnetic induction in the sinusoidal distribution model under inter-turn short circuits faults. To represent as much as possible, the reality of the asynchronous machine, we include space harmonics of magnetic induction in sinusoidal distribution model and also an analytical calculation of inductances under shorted fault. Hence, the proposed model is based on coupled magnetic circuit theory. To validate and prove the effectiveness of this model, simulations and experimental results for an induction motor with inter turn short circuit fault, are presented.

Keywords: Fault detection and Diagnostics, Transportation systems, Control of telecommunications systems
Abstract: Cables are ubiquitous in modern vehicles and the rate of failures has grown with the complexity of the vehicular communication network. The severity of the faults in cables begins in a mild manner, known as soft faults, where the network is still able to perform its required functions. Such faults if not caught in early stages, may develop into hard/severe faults which can lead to serious breakdowns of the whole system. It is thus of prime importance to design and implement an efficient soft fault detection system. In this paper, a bus Ethernet network composed of one source and multiple receivers is studied. A soft fault, which may be caused by a localized degradation of the cable quality, is equivalent to an additional series resistor. Since all the receivers are linked to the same line (the backbone), a fault in the network may affect all the receivers which make it difficult to pinpoint the faulty branch. The transmission transfer function measured between the source and each end of line is monitored. A new health indicator inspired from the classical chain matrix model of the cables is proposed. This indicator is computed at each receiver to detect a soft fault and to locate the faulty branch. Based on the studied topology, a signature matrix is generated, which will be used for fault detection and localization. Simulation results are presented to illustrate our approach.

Keywords: Power systems, Intelligent and AI based control, Fault detection and Diagnostics
Abstract: This paper deals of the application of deep-learning and support vector machine algorithms for the power system fault detection. The power system considered is a standard IEEE 14-bus. Faults are integrated to the power system and simulated using SimPowerSystems toolbox of Matlab. All data obtained, the voltages and the currents of each bus, are saved and used to test both algorithms. Simulation results show the superiority of the deep-learning algorithm compared with support vector machines algorithm.

Keywords: Fault detection and Diagnostics, Modeling and simulation, Control applications
Abstract: The intensified heat exchanger/reactor systems, which combine the heat transfer and chemical reactions in one unit, became very popular and interesting in the chemical engineering field. However, to ensure the safety and maintain the performance of these systems, supervision, diagnosis, and fault tolerant control are highly demanded. In this paper, a fault tolerant control system, based on a bank of interval observers with a backstepping-based control law, is employed in a new intensified heat exchanger/reactor, in order to detect, isolate and recover all possible dynamic faults.

Keywords: Linear and nonlinear systems, Fuzzy and neural systems, Control applications
Abstract: This paper is concerned with the stabilization problem for a class of continuous-time singular Takagi–Sugeno fuzzy systems. By using the Lyapunov method and some slack matrices, sufficient conditions on stability and stabilization via static state feedback controller (SSFC) are proposed in terms of strict parametric linear matrix inequalities (PLMI) for T–S fuzzy models. A simulation example is given to illustrate the effectiveness of the proposed method.

Keywords: Fuzzy and neural systems, Fractional order systems, Renewable Energy
Abstract: This paper proposes a design of adaptive interval type 2 fuzzy logic robust nonlinear controller using the fractional sliding mode approach. The developed control strategy is applied to the generator side converter for a variable speed wind energy conversion system equipped with a permanent magnet synchronous generator. Adaptive interval types 2 fuzzy systems aim to approximate the sliding discontinuous signals and allow to reduce the chattering problem.The resulting adaptive control laws are obtained from the lyapunov’s stability condition. To compare the tracking performance and system robustness of the proposed fractional control method to the conventional sliding approach, simulation tests via Matlab/Simulink environment, are carried out under external disturbances and parameter variations.

Keywords: Intelligent and AI based control, Linear and nonlinear systems, Control algorithms implementation
Abstract: Due to their efficiency in standard control problems, proportional-integral-derivative (PID) controllers are widely used in industrial control systems. Although this controller has been established as a control standard, tuning of its parameters and finding their optimal combination still represents a challenge, particularly under changing operating conditions, where control designer cannot rely on the invariance of the plant model. Tuning of the proportional, integral and derivative gain of a PID controller represents an optimization task, for which we propose in this work a solution based on artificial intelligence (AI) approach using radial basis (RB) function for activation of neural networks (NN) which adapt the controller gains and learn the plant model in order to account for the controller influence on the control outcome. The controller is implemented in a discrete-time system which enables real-time learning and implementation. The effectiveness of the proposed controller is tested on a benchmark example of a discrete-time model of a clamped flexible aluminum beam, obtained through the subspace model identification.

Keywords: Control education, Fuzzy and neural systems, Modeling and simulation
Abstract: This paper presents the state of the art of the main hardware families used for teaching methods of automatic and control during the COVID-19 pandemic. Three case studies based on practical labs in Junia HEI: Fuzzy logic for control, modeling and simulation of a photovoltaic system under Matlab/Simulink/Simpower and application development workshops in Java are analyzed. The feedbacks and the analysis of advantages show that automatic teaching continued to combine theory and practice despite the sanitary crisis and emphasize the importance of Matlab tool in automation and control teaching and learning.

Keywords: Fuzzy and neural systems, Optimization, Estimations and identification
Abstract: Abstract—This work is concerned with the problem of finite frequency (FF) filter design in two-dimensional (2D) discrete-time nonlinear systems in Takagi-Sugeno (T-S) fuzzy model described by Roesser model. The goal is to find an FF H∞ T-S fuzzy filtering design in such a way that the filtering error system is asymptotically stable and has a reduced FF H∞ performance over FF areas with noise is established as aprerequisite. Via the use of the generalized Kalman Yakubovich Popov (gKYP) lemma, Lyapunov functions approach, parameterize slack matrices, new design conditions guaranteeing the FF H∞ T-S fuzzy filter method of Roesser model are developed by solving linear matrix inequalities (LMIs). At last, the simulation results are provided to show the effectiveness and the validity of the proposed FF T-S fuzzy of FM LSS models strategy by a practical application has been made.

Keywords: Motion control, Modeling and simulation, Linear and nonlinear systems
Abstract: Developing a lateral controller specialized for evasive maneuvers is crucial for increasing vehicle occupants' and road users' safety. The controller must use all the vehicle's dynamic capacity to avoid or mitigate the crash severity in critical situations. In this work, we have developed a new lateral controller based on sliding mode control (SMC), considering a vehicle dynamic envelop. Its primary function is to define the steering angle while addressing tire lateral slip saturation. We have evaluated the controller using IPG CarMaker. The simulation results reveal that the vehicle with the developed controller can perform an evasive lane change in less than 1.1 seconds for velocities up to 130 km/h on a dry road. Therefore it is beneficial to implement our controller in extreme conditions where a crash is foreseen up to 2 seconds, and longitudinal operations alone are not enough to avoid the crash.

Keywords: Modeling and simulation, Power systems
Abstract: The issue of energy and pollutant emissions has become more prominent over the past few years. Increasingly, power sources with high efficiency and low emissions are in demand. The application of oxygen-enriched air in diesel engines has been proven to be an effective in-cylinder strategy for soot reduction and to improve combustion effectiveness. This however results in an increase in the NOx level. A simulation study using a three-dimensional computational fluid dynamics (CFD) model has been conducted to investigate the effects of oxygen-enriched air on a DI diesel engine operating in order to reduce soot emissions with minimal changes in NOx emissions. A parametric study has been initially performed to examine the impact of intake air oxygen content from 21% to 35%v/v under a constant fueling rate. Then sixteen different strategies have been proposed to consider the effects of intake air oxygen fraction from 21% to 25% under four different fueling rates. In this study, 24% O2 were found to achieve the best-operating conditions for engine performance and reduced NOx emissions.

Keywords: Modeling and simulation, Mechatronics, Control applications
Abstract: In the area of control theory, the model of the system under study is a fundamental part for the control design and for its validation process. The system model helps in the realization of numerical simulations and is necessary for rigorous convergence and stability proofs of the closed-loop system. On the other hand, for mechanical systems, although there are well-known methods for their modelling, such as the Euler-Lagrange equations, new tools have emerged in recent years to include a most extensive class of these systems such as systems with holonomic or non-holonomic constraints. The Udwadia-Kalaba equations are one of this recent methods that, unlike the equations with Lagrange multipliers, can include non-linear constraints on velocities. Other less used but important technique are the Poincaré-Chetaev-Rumyantsev equations, which uses quasi-velocities to include a wide variety of system constraints. In this work, a comparison is made between the three mentioned methods which are applied to model a five-bar mechanism. By contrasting the three methodologies, specific advantages and disadvantages of each technique are identified.

Keywords: Modeling and simulation, Control applications
Abstract: This paper studies a design and simulation of multi-phase interleaved boost DC/DC converter suitable to use in automotive systems. The control strategy used in this converter is based on a Pulse Width Modulation (PWM) technique with a Proportional-Integral-Derivative (PID) controller. The proposed converter has a 1KW power and 42V output voltage to satisfy the requirements of power system used in automotive applications. This design is a technically viable solution to the dual-voltage power system for passenger car in the near future. The conception, analysis and simulation of a six-phase interleaved DC/DC converter for automotive power systems are presented. One kilowatt, six-phase interleaved boost DC/DC converter designed to operate in a Discontinuous Conduction Mode (DCM). The effect of parameter variation on the performance of the converter is described. Simulation results are provided to demonstrate the advantages of the proposed converter and controller scheme. All the advantages of interleaving technique, such as higher efficiency and reduced input / output ripples for both voltage and current, are also Achieved. The results show that the system is stable and well worked under input voltage variations and the output voltage remains within the desired specified limits presented in automotive standards, also in the case where the output load changes.

Keywords: Modeling and simulation
Abstract: The purpose of this paper is to present a novel approach to handle all-sensors losses of the internal greenhouse environmental data due to the power cut throughout the greenhouse. The proposed method is based on the principal components analysis (PCA) and the seasonal autoregressive integrated moving average model with exogenous variables (SARIMAX). The exogenous variables are derived from the external meteorological dataset provided by the weather station of the city where the greenhouse is located. The role of the PCA method is to analyze the correlation between exogenous and the available endogenous variables and then reduce the dimensions of the exogenous dataset. After selecting the best choice of the training set for the SARIMAX model, the obtained results show that the proposed approach represent a promising solution for completing the bulk missing data in internal greenhouse environmental dataset.

Keywords: Modeling and simulation, Mechatronics, Motion control
Abstract: Dynamic modeling of complicated mechanical systems using physical component models created through MATLAB® Simscape™ Multibody™ software has been gaining increasing attention. This paper considers the application of this software to accurately model a belt driven H-frame type XY positioning table. Before this could be accomplished, a method for modeling belt compliance had to be developed, as this capability (at the time of the investigation) was not innately built into the software. The method that was developed involves breaking down system-level belt compliance into components and lumping the resultants as “springs” within the mechanical model for each degree of freedom of the end effector. This approach was used to add compliance to the Multibody model, at which point it was simulated against an 8th-Order State-Space model of the system that was developed and verified in a previous work. The results show that the belt compliance modeling method for the Multibody model presented here is an effective way to do this and provides an option for more accurate physical modeling of compliant members in Multibody which is a viable approach to model complicated physical systems.

Keywords: Embedded Systems, Process control and instrumentation, Control applications
Abstract: This paper proposes a scheduling algorithm applicable to both interactive and soft real time systems. It is derived from LLREF algorithm and is based on priority queues and Round Robin algorithm. The goals of this algorithm are reducing the processes normalized response time (calculated as ratio between process response time and process computation time), reducing the global system response time, reducing the number of context switches and meet the processes' deadlines. The algorithm was implemented using Visual DSP++ integrated development environment with support of VDK real time kernel. The obtained results show an improved performance of proposed algorithm comparing with other scheduling algorithms used in interactive and soft real time systems. The proposed algorithm is easily to implement using the existing VDK primitives, so it can be integrated fast into embedded systems developed with Visual DSP++ from Analog Devices with VDK support.

Keywords: Modeling of complex systems, Time-delay systems
Abstract: An algorithm for synchronization of a linear multi-agent system with jointly connected topology is presented. The system is composed of identical agents. It is assumed that the control of the agents is sampled. The resulting control is obtained as a solution of a set of linear matrix inequalities. An example illustrates the results.

Keywords: Linear and nonlinear systems, Modeling of complex systems, Modeling and simulation
Abstract: In this paper, the synchronization of the complex network with nodes being nonidentical chaotic systems is studied. The master-slave synchronization scheme between unidirectionally coupled nodes of the complex network is solved theoretically. Each node of the complex network is represented by the generalized Lorenz system (GLS) with different parameters. Here, the generalized synchronization in a complex network with nodes being the chaotic systems (the particular cases of the GLS, namely Lorenz and Chen chaotic systems) are studied. The application of the theoretical results for the generalized synchronization between nodes of the complex networks with tree topology is verified by the numerical simulations. The duplicated system approach is applied for the detection of the generalized synchronization in a complex network with a tree topology. Highlighting, the obtained results are applied to synchronize complex networks consisting of GLSs with different parameters but belong to the same class (Chen, Lorenz) of the chaotic systems.

Keywords: Modeling of complex systems, Linear and nonlinear systems, Modeling and simulation
Abstract: This paper presents the development and mathematical analysis of an open-channel flow model. A tanks-in-series model relying on systems engineering perspectives is constructed. Through a series of assumptions, the obtained nonlinear model is approximated by a linear dynamics, and its asymptotic modal behavior is analysed in infinite dimensions using rigorous mathematical machinery. The analysis exploits the repeating tridiagonal structure of the system matrix using Chebyshev polynomials and elaborates the dependence of the modal dynamics on the so-called flow coefficient, which accounts for the description of the flow of the model. The analysis presented in this paper reveals that using constant flow coefficient for flow description in cascade arrangement of liquid storages results in physically meaningless asymptotic modal behavior. The asymptotic modal behavior is well defined if the flow coefficient is an affine function or higher-order polynomial of the number of elements in the cascade structure.

Keywords: Power systems, Modeling and simulation, Control applications
Abstract: This manuscript describes a comparative analysis between the variable-bandwidth (VBW) of the hysteresis modulation (HM) and the unipolar pulse-width modulation (PWM) by double intersection, both based on the conventional sliding mode (CSM) strategy. The two adopted techniques are applied to a new topology of a bidirectional asynchronous multichannel DC-DC Buck converter. The cells are parallel-connected and operating in continuous conduction mode (CCM). Our study contributes to the control method and the implementation, the command is applied at the same time without phase shift to an identical three cells of a non-isolated and asynchronous converter, this topology is rarely studied. The aim is to fix the switching frequency of the converter while referring to an adaptive feedback approach. Therefore, we integrate a hysteresis modulator and develop a variable hysteresis band function to attenuate the non-linearity phenomenon of the conventional sliding mode. Then, we apply an adaptive feedback current control technique to surpass the dilemma of the variable switching frequency for high power converters. The results demonstrate that the studied system became more stable, more efficient, and able to cope with variations in input voltage, load, and desired output voltage. The same results clearly show the areas where significant differences have been found between the two strategies. Simulation studies in the MATLAB® / Simulink™ environment are performed to analyze system performance and assess its robustness and stability under parametric variation.

Keywords: Modeling of complex systems, Embedded Systems, Real time systems
Abstract: The analysis of certain pathological movements of Parkinson's disease often allows the neurologist to establish a precise diagnosis and to propose an adapted treatment for the patient. This work considers a motion capture device with embedded sensors such as inertial measurement unit without using camera. This paper presents a movement modeling method in a matrix representation by exploiting the Ordinary Least Squares approach. The quality and simplicity of the model allow movement recognition in real time. This method was tested as part of an experiment including 5 movements, each performed by 14 people wearing the motion capture device (Perception Neuron Pro suit). The global accuracy involved by this method is about 92.85%.