Keywords: Linear and nonlinear systems
Abstract: This paper investigates the design of an extended high gain observer to estimate the state together with some unknown constant parameters for a class of nonlinear systems. We firstly consider the case where all the parameters are known. A high gain state observer is proposed and its exponential convergence is carried out under a well defined persistent excitation condition. Then, the case where some parameters involved in the system equations are unknown is considered using a suitable augmentation of the nonlinear system with the parameters dynamics. Thanks to the introduction of the notion of characteristic indices associated to the unknown parameters, an extended high gain observer is designed to simultaneously estimate the state together with the unknown parameters. It is shown that the exponential convergence of the extended high gain observer is ensured under the persistent excitation condition that has been already considered for the state observer together with an additional persistent excitation to handle the parameter estimation process.

Keywords: Linear and nonlinear systems, Autonomous Ssystems, Control applications
Abstract: In this paper, stability analysis in a class of fractional-order autonomous chaotic systems is studied, using the fractional Routh-Hurwitz criteria. A necessary conditionfor these systems to remain chaotic is obtained. The fractional Routh-Hurwitz conditions are used to control chaos in the proposed systems to its equilibriums, using specific choice of linear controllers, it is shown that the fractional-order systems are controlled to its equilibrium points; however, its integerorder counterpart is not controlled. Numerical results show the effectiveness of the theoretical analysis.

Keywords: Linear and nonlinear systems, Control applications
Abstract: This paper is concerned with the problem of robust Sliding Mode Control (SMC) design for Maxpid system with uncertainty. The uncertainties are assumed to satisfy norm bounded condition in the state matrix. By applying a Variable Structure Output Feedback Control (VSOCF), a sufficient condition for the existence of stable sliding surface is given in terms of Linear Matrix Inequalities (LMIs). Based on this existence condition, the synthesized VSOCF can guarantee the sliding mode reaching condition of the specified sliding surface for all admissible uncertainties. An application to the model of a Maxpid system is given to show the VSOCF methodology.

Keywords: Linear and nonlinear systems, Mathematical systems theory
Abstract: This paper mainly deals with switched linear systems defined by a pair of Hurwitz matrices that share a common but not strict quadratic Lyapunov function. Its aim is to give sufficient conditions for such a system to be GUAS.

We show that this property of being GUAS is equivalent to the uniform observability on [0,+infty) of a bilinear system defined on a subspace whose dimension is in most cases much smaller than the dimension of the switched system.

Some sufficient conditions of uniform asymptotic stability are then deduced from the equivalence theorem, and illustrated by examples.

To conclude we focus our attention on the convergence rate of the solutions of linear switched systems.

Keywords: Linear and nonlinear systems, Mathematical systems theory, Autonomous Ssystems
Abstract: This paper deals with two dimensional (2D) systems with variable delays. More precisely, conditions are developed to study the asymptotic and exponential stability of 2D Roesser-like models with variable independent delays affecting the two directions. Based on proper definitions of 2D asymptotic and exponential stability, sufficient conditions are developed, expressed using Linear Matrix Inequalities, based on Lyapunov-Krasovskii functionals

Keywords: Modeling and simulation
Abstract: In this paper, a theoretical model for a DC glow discharge at low pressure is developed. The model allows the determination of plasma characteristics distribution. The proposed theoretical model is applied to collisional argon plasma in a reactor consisting of two plane parallel electrodes. The simulation is carried out using finite difference method. Matlab software is used to solve the partial differential equations that govern the behavior of the discharge. The simulation results are illustrated in terms of spatial distribution of electron and ion densities, potential, electric field and electron energy, for a gas pressure of 0.1 Torr and DC voltage of -250 V. Since geometry plays an important role in plasma technologies, it is important to investigate the scaling of plasma reactor parameters such as the distance between the reactor electrodes.

Keywords: Modeling and simulation
Abstract: In this paper, a numerical study of electrical characteristics is investigated for a argon micro discharge, generated by a radio- frequency excitation (13.56 MHz) at high pressure (100Torr) and low voltage (150V). Using a software Comsol, the results show that the rf voltage is responsible for the increase of plasma potential. Moreover, we found that the rise of gas temperature causes a reduction of plasma density inside and outside the hole of the micro hollow cathode and also affects a decrease of plasma potential at the sheaths. In consequently the sheath’s thickness becomes large. The evolution of electron temperature in the sheaths is affected by the temperature of the gas in the micro-reactor. The results show its growth on the sheath regions if the gas temperature increases from 300 kelvins to 500 kelvins.

Keywords: Modeling and simulation, Linear and nonlinear systems
Abstract: This paper proposes a new nonlinear model entitled decoupled state Laguerre multiple models approach used for modeling nonlinear systems. This representation is a combination between the classical multiple models approach and the Laguerre orthogonal bases used for modeling the sub-systems. The goal of this combination is to reduce the complexity of the identification algorithm of multiple models approach and to make a parametric reduction. The resulting multiple models prods with its parameter complexity reduction and with its simple structure offers the possibility to applying online identification algorithms and, therefore, the synthesis of adaptive control algorithms. The parameter identification of the Laguerre multiple models is achieved by using the Gauss-Newton algorithm. The performances of the Laguerre multiple models and the proposed identification approach are illustrated by numerical simulations.

Keywords: Modeling and simulation, Optimization, Transportation systems
Abstract: As their high specific power and long cycle life over conventional batteries, Ultracapacitors have been thought as a good alternative/supplement for energy storage for city buses which operate considerable fast acceleration, deceleration and stop. In the current study, the powertrain system for a PEV (Pure Electric Vehicle) bus with batteries and ultra-capacitors for energy storage were developed. While high vehicle performance and low systemic cost were considered as main factors for the system configuration, different designs for the combination between batteries and ultra-capacitors were proposed and investigated. Based on optimized control strategies for energy management with and without stop-charging, the system performance was numerically simulated with Matlab/Simulink and ADVISOR under the UK bus driving cycle. The results show the involvement of Ultracapacitors can improve the performance of the vehicle and also reduce the cost. When stop-charging is employed, the battery pack can be completely removed and the Ultracapacitor can provide all the electric energy required by the bus.

Keywords: Modeling and simulation, Process control and instrumentation, Control applications
Abstract: This paper presents the search for errors in the measurement instruments in a dynamic system of metering liquid or gas and sees the tolerance defined by the international standards and recommendations. We will implement a program on Matlab / Simulink which is calculated based on the ISO-5167. This program will take the system parameters on considerations such as: the willingness plates, the size of the orifice, the given design conditions, reference conditions, find pressure drop for a given flow, or flow for a loss of given load. The results are considered very good and satisfactory because the errors identified of measuring instruments system are within the margin of error limit by the regulations.

Keywords: Mathematical systems theory, Modeling of complex systems, Control applications
Abstract: A boundary control method for nonlinear partial differential equations (PDEs) is developed, with the use of differential flatness theory. First, following the procedure for numerical solution of nonlinear PDEs, it is shown that the PDE is equivalently represented by a set of nonlinear ordinary differential equations (ODEs) and an associated state equations model. For the local subsystems, into which a nonlinear PDE is decomposed, it becomes possible to apply boundary control. Next, the controller design proceeds by showing that the state-space model of the nonlinear PDE stands for a differentially flat system. Moreover, for each subsystem which is related to a nonlinear ODE, a virtual control input is computed, that can invert the subsystem's dynamics and can eliminate the subsystem's tracking error. From the last row of the state-space description, the control input (boundary condition) that is actually applied to the nonlinear PDE system is found. This control input contains recursively all virtual control inputs which were computed for the individual ODE subsystems associated with the previous rows of the state-space equation. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally find the control input that should be applied to the nonlinear PDE system so as to assure that all its state variables will converge to the desirable setpoints. By applying the proposed boundary control, it is shown that the nonlinear PDE dynamics can be made to track the designated variation profile.

Keywords: Multivariable control, Modeling and simulation, Robust control and Hinfty control
Abstract: Modern controllers are based on the use of mathematical models. However the models are always obtained through a reduction in the complexity of reality. Consequently, their ability to properly represent the general behavior of the processes is very limited. Therefore, it is advantageous to analyze the problem resulting from model uncertainty in the control of irrigation main canal pools. This problem has often been ignored in theoretical studies and in practical process control. A new method is proposed for controlling main irrigation canals; first we show a principal gains method on how to achieve the benefit of feedback in the face of uncertainties. Then we present an irrigation main canal pools example which illustrates the use of robust control to provide satisfactory performance despite of irrigation main canal pools.

Keywords: Optimal control
Abstract: This paper investigates a new optimal control stabilization for continuous switched linear systems under arbitrary switching. In fact, based on the aggregation techniques and the application of the Borne-Gentina stability criterion. New stabilization conditions under arbitrary switching are deduced. These results issued from vector norms correspond to a vector Lyapunov function. Indeed, the switched system to be controlled will be represented in the Companion form. A comparison system relative to a regular vector norm is used in order to get the simple arrow form of the state matrix that yields to a suitable use of Borne-Gentina criterion for the establishment of sufficient conditions for global asymptotic stability of the close loop system.

Keywords: Optimal control, Linear and nonlinear systems
Abstract: This paper presents an optimal predictor and a self-tuning control scheme to solve the regulation problem of large-scale systems. We consider the class of large-scale nonlinear system which can be decomposed into single-input single-output interconnected nonlinear subsystems. Each interconnected subsystem can operate in a stochastic environment and described by discrete-time Hammerstein mathematical model, with unknown time-varying parameters. Self-tuning regulator algorithm for large-scale nonlinear stochastic systems is developed on the basis upon the minimum variance approach with implicit scheme. The performance of the proposed self-tuning regulator is evaluated by simulation example.

Keywords: Optimal control, Power systems, Optimization
Abstract: This paper deals with an optimal tuning of the Proportional-Integral-Derivation (PID) controller for both Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR) of two-area interconnected power system using Particle Swarm Optimization (PSO) algorithm. The active and reactive powers are controlled separately. The LFC loop controls the frequency and active power and the AVR loop adjusts the voltage and reactive power. In order to analyze the system frequency, the tie line power flow, and the system voltage, the two-area interconnected power system is simulated for a step load disturbance in Area-1. The main primary objective is to suppress all the fluctuations of the system due to the disturbance and get back the frequency and voltage at nominal values. The results are compared with the ones obtained by the traditional Ziegler-Nichols (Z-N) method, Genetics Algorithm (GA) and Bacterial Foraging Optimization Algorithm (BFOA), and the proposed method has proven to be very efficient.

Keywords: Robotics, Control algorithms implementation, Intelligent and AI based control
Abstract: Nowadays, physically impaired people (or people with special needs) still struggle with daily tasks when using mobility aid devices, whether for crossing doors, parking or manoeuvring. In this context, assistive robotics can offer solutions to those problems thus increasing the users’ quality of life. In this work we propose a collaborative navigation strategy for predicting, and thus improving, the users’ skills for driving assistive vehicles. In particular, two semi-autonomous navigation modes are presented, in which the vehicle is able to predict the user’s motion intentions, reducing his/her workload. The systems were tested in a simulated environment with a population of seven volunteers, using a friendly interface and later validated in a real world scenario. Results showing that the system aids the user to safely navigate the environment and to decrease his/her workload are also included in this work.

Keywords: Robotics, Mechatronics
Abstract: Recently, the use of assistive vehicles in industrial or daily day tasks started to grow rapidly. Therefore, it is important to guarantee safety to the robot and to any other moving element in the environment (either people, animals or other robots). In this work, we develop and implement a navigation assistive system based on collision risk estimation using depth sensors. Speed and steering constraints are applied to semi-autonomous assistance vehicles to avoid hazardous situations and to improve the users welfare. We calculate a collision risk indicator based on the tracking of moving elements from the scene, by means of a visual tracking approach and a proposed motion model. The performance of the system is tested in selected situations. Furthermore, the motion model associated with people is empirically validated. Finally, the simulation results included here, show the effectiveness of the system in reducing the imminent collision risk up to 90%, without imposing drastic decisions over the vehicle movement.

Keywords: Robotics, Motion control, Linear and nonlinear systems
Abstract: In this paper, a control algorithm based on Backstepping technique for differential drive mobile robot path tracking is developed. The use of backstepping theory to solve the problem of path tracking is relatively new in the field of mobile robots control. Numerical simulations were conducted to show the effectiveness of the proposed algorithms to solve the nonholonomic constraints of the mobile robot, and its ability to achieve satisfactory performance. The stability of the system is proven via Lyapunov theory.

Keywords: Transportation systems, Modeling and simulation, Estimations and identification
Abstract: Knowledge of the torque and the tire forces at each wheel is an important intermediate variable for system design of advanced driver assistance. This paper describes how it is possible to estimate online the torque and the longitudinal force of the vehicle wheel. We use a simplified model to synthesize the non-linear high-gain observer. The observer is validated on a comprehensive model of the vehicle. The necessary measurements is the usual rotation speed of wheel. Simulation results show effectiveness and robustness of the proposed method.

Keywords: Linear and nonlinear systems, Optimization, Control algorithms implementation
Abstract: The main contribution of this paper is the stabilization of bilinear systems by state feedback control. An extension of nonlinear feedback control based on the NonLinear Pole Placement technique (NLPP) is proposed. Then a polynomial feedback control approach is developed based on Linear Matrix Inequality resolution technique (LMI). Those approaches are applied on the two common classes of bilinear systems (non-homogeneous and homogeneous) in both cases of single and multi input. An example of vehicle suspension is then considered to illustrate the effectiveness of the proposed approaches.

Keywords: Linear and nonlinear systems, Robust control and Hinfty control, Estimations and identification
Abstract: In this paper we investigate the problem of the design of a controller based on Hinfinity functional filter, in a finite time stability context for linear continuous time-invariant systems affected by norm bounded disturbances. Finite time bounded-Hinfinity criterion of the closed-loop system is investigated. the order of the controller is reduced since the designed functional filter has the same order than the dimension of the control input of the system. The design conditions are expressed as linear matrix inequalities. A numerical example is provided to show the validity of the proposed approach.

Keywords: Mathematical systems theory, Autonomous Ssystems, Optimization
Abstract: In a previous work, we showed how the flatness of the heat equation with two controls at both ends of the domain could be used to generate solutions to create solutions to Burgers' equation. The created solutions were thought as a motion planner for a multi-agent systems of e.g. ground robots, UAVs. The trajectories of the different particles of the PDE are to be used as input for a feed-forward control. In this paper, we show a practical implementation of this method, oriented toward computational efficiency based on Particles Swarm Optimization. In a first section we introduce the flatness based mathematical framework of our planner. We present the implementation in a second part. We present examples of trajectories in a last section and study and discuss the performances and the properties of the generated trajectories before drawing conclusions and future use of the framework in real UAVs systems.

Keywords: Robust control and Hinfty control, Optimal control, Linear and nonlinear systems
Abstract: In this paper, we investigate the problem of the decentralized robust guaranteed cost control (GCC) for robust stabilization of uncertain linear interconnected multimachine power systems. Sufficient conditions for the existence of guaranteed cost controllers and formulation in a minimization problem are given in terms of linear matrix inequality (LMI) which resolution yields the decentralized control gain matrices, to ensure the asymptotic stability of the linear uncertain large scale system. The effectiveness of the proposed control techniques are demonstrated through numerical simulations on a linear uncertain power system with three interconnected machines, for different cases of perturbations.

Keywords: Modeling and simulation, Linear and nonlinear systems, Estimations and identification
Abstract: This paper proposes a new online kernel identification method of a nonlinear system in the Reproducing Kernel Hilbert Space. The proposed algorithm entitled online RKPLS-RN kernel method uses the Reduced Kernel Partial Least Square (RKPLS) technique in an offline phase to construct a RKHS model with reduced parameter number. Then the Regularized Network (RN) method is used on online phase to update the reduced parameters of the RKHS model. The considered measure of performance is the Normalized Means Square Error (NMSE). The proposed online kernel method is evaluated by handling the Cascades Tanks system

Keywords: Fuzzy and neural systems, Control algorithms implementation, Optimization
Abstract: In this paper, we propose a robust minimum variance controller for nonlinear systems based on feedforward neural networks. Based on input output system measurements, a neural network model with uncertain parameters is trained to approximate the unknown dynamic behavior of the system. The control law is formulated as a minmax optimization problem which minimizes the worst case of the quadratic objective function subject to the uncertain parameters of the model and the control signal constraints. When classic optimization methods are used to optimize this kind of problem, a local solution is then obtained. In order to reach the global solution of the control problem which corresponds to the optimal control actions, the Generalized Geometric Programming technique is used to reduce the constrained non convex problem to a convex one. The performances of the proposed neural controller are illustrated by a simulation example.

Keywords: Power systems, Fuzzy and neural systems, Control algorithms implementation
Abstract: In this paper an improvement of the Conventional Direct Torque Control (CDTC) of an induction motor is presented using the artificial intelligence to control the motor speed and an implementation on the FPGA (Field Programmable Gate Array). The conventional Proportional Integral (PI) controller of speed is widely used in industry due to its simplicity and robustness, but when the dynamics of the system changes with the time and operating conditions, the effectiveness of this controller decreases. In order to overcome the conventional PI limitations the fuzzy logic is used. Also, the digital implementation of the CDTC with fuzzy controller of speed is based on the FPGA whose purpose is to overcome the software solutions (DSP, Microcontrollers) limitations, like the processing time which increases the sampling period due to its sequential processing. The FPGA is characterized by high processing speed due to its parallel processing. The obtained simulation and implementation results using Xilinx System Generator (XSG) and the FPGA Virtex 5 demonstrated that the proposed control strategy is able to obtain high performances.

Keywords: Image processing, Embedded Systems, Real time systems
Abstract: Particle Swarm Optimization (PSO) is a metaheuristic algorithm based optimization technique for continuous search problem. It is among the most used algorithms in various areas of application. Its popularity has exceeded the deferred-time problems to the real-time problems that require the use of embedded architectures. Many real-time applications include mobile robots and medical image processing has been widely developed and improved using PSO by many researchers. We have succeeded the implementation of the real-time segmentation of MRI medical images based on PSO algorithm in previous work. In this paper, we try to extend the work by adding a control unit that controls each task of the various blocks of the architecture. Therefore, the new obtained synchronous architecture of MRI images segmentation based PSO allows to save execution time and thus narrow the search procedure of the optimal threshold. The performance of the proposed synchronous hardware architecture is evaluated and validated using a set of MRI medical images.

Keywords: Linear and nonlinear systems, Control applications, Process control and instrumentation
Abstract: This paper presents a bioreactor control using a nonlinear autoregressive moving average (NARMA-L2) neural network based feedback-linearization. The NARMA-L2 neural network is trained off-line for forward dynamics of the bioreactor model with redefined output and is then inverted to force the real output to approximately track a command input. The controller has been able to take care of nonlinearly aspect of the system. Simulation results show that the NARMA-L2 neural control strategy has a better trajectory tracking ability than the use of the inverse neural model control strategy, where the control scheme is not very fruitful since the inverse model developed by the neural network is not accurate enough to exercise effective control.

Keywords: Renewable Energy, Transportation systems, Control applications
Abstract: Fuel cell systems offer clean and efficient energy production and are currently under intensive development by several manufacturers for both stationary and mobile applications. Therefore, their slow dynamics and difficulty to respond to the abrupt changes of load leads to prefer the association of a FC with one or more power sources of high dynamics, such as supercapacitors or batteries. In this paper, a hybrid DC power sources system using FC as a main source, a DC link and battery as an auxiliary power source is studied. The main purpose of this work is the control of the hybrid DC source by Pssivity-Based Control (PBC) where the equilibrium points are computed as function of the desired battery current. PBC is a very powerful nonlinear technique dealing with important system information like the system's total energy. The second aim is to determine the desired battery current by a Fuzzy Logic Control (FLC) as function of battery state of charge SoC and the hydrogen quantity QH_2 in order to ensure the desired behaviour of the system. Stability proof and simulation results are given.