There is always a need to know the states of a system in order to make important decisions, control the system, or predict reliably its future state. A simple but sure method for knowing the states of a system is to measure them directly. However, in a general complex system, it is not feasible or even possible to directly measure all the states of the system. An alternative method for knowing the states of the system instead of measuring them all is to create and use a model for the system. If the model is perfect, we can accurately estimate the states when the inputs and the initial conditions of the system are known exactly. However, the initial conditions are never known and some inputs are unknown (external disturbances for example). Then, the efficient method to estimate the state of a system is the observer (called also a software sensor). It concerns the reconstruction of the state of a system based on the available measurements and the model of the system, generally uncertain. The objective of this talk is to give an overview of the past and recent developments in the field of the observers design. It also presents some open theoretical and practical problems.

 In this paper, fault detection problem for uncertain discrete-time switching systems with delay is studied. Sufficient conditions of building an observer are obtained by using multiple Lyapunov function. These condition are worked out in a new way, using cone complementarity technique, to obtain new LMIs with slack variables and multiple weighted residual matrices. The obtained results are applied on a numerical example with all the entries of the same sizes.

 Advanced model-based FDI (Fault Detection, Identification) challenges range from pre-design and design stages for upcoming and new programs, up to the improvement of the performance of in-service flying systems. However, today, their application to real aerospace world has remained extremely limited. The paper underlines the reasons for a widening gap between the advanced FDI methods being developed by the academic community and technological solutions demanded by the aerospace industry.

 This paper presents a new approach for active diagnosis bsaed on adaptive Generalized Likelihood Ratio (GLR) test for multiple Faults Detection and Isolation (FDI) in dynamic stochastic linear systems. For sequential fault detection, the adaptive GLR test confronts the fault hypothesis ${H}_{k}{h}_{j}$ to the reference hypothesis $H_{k}$ updating on line after each detection and isolation of one fault. The proposed scheme is based on the design of the Fault Detection Filter (FDF) and on the GLR test applied on the innovation sequence of the FDF.

 This paper presents a strategy based on fault diagnosability maximization to optimally locate sensors in complex systems. The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of diagnosability and does not exceed a maximum sensor configuration cost. The strategy is based on the structural system model. Structural analysis is a powerful tool for dealing with complex nonlinear systems. The proposed approach is successfully applied to a Fuel Cell Stack System.

 This paper studies sensor fault detection and the associated Fault-Tolerant-Control (FTC) algorithm for Takagi-Sugeno (TS) fuzzy system. Fault detection scheme based on a descriptor approach is considered. A descriptor fuzzy observer is adopted which estimate both the system state and the occurring sensor fault. The convergence of the proposed observer is performed by the search of suitable Lyapunov matrices. Stability conditions of the closed loop system via static output feedback control are also established and expressed in the form of Linear-Matrix-Inequalities (LMI). Furthermore, pole placement technique is used to locate the system pole in a desired region in order to guarantee a satisfactory behavior of the response of closed loop system. A simulated tree tank system is used to demonstrate the effectiveness of the proposed strategy.

 This paper presents sufficient conditions for the stabilization of constrained switched discrete- time linear systems with polytopic uncertainties. A strategy of conception of switched laws from the solution of Lyapunov-Metzler inequalities is developed. The state variables are supposed to be not accessible so that the feedback strategy depend on given output variables. A numerical example is used to illustrate the proposed techniques.

 This paper deals with the synthesis of a fuzzy controller of DC-DC boost converter by using the integral action performance. First, a bilinear model of the boost converter is developed which is subsequently converted into a Takagi-Sugeno (TS) model. Then, a TS fuzzy controller is designed to stabilize the output voltage and guarantee a perfect disturbance rejection. It should be noted that the boost converter is used as an interface between the photovoltaic (PV) panels and the loads connected to them. Thus, the purpose of the proposed fuzzy controller is to adapt the duty cycle of the power MOSFET even in the case of the input voltage variation, so that the tracking performance is guaranteed. The control gains are obtained by solving a set of Linear Matrix Inequality (LMI). Finally, simulation result is given to demonstrate the controllers effectiveness.

 The purpose of this paper is to introduce the synergic control theory for a design of a diagram of adequate and powerful control. Therefore and initially, the basic concepts of this technique are presented and followed by the stages of design to develop a single synergic controller speed and rotor flux in the diagram of field oriented control for induction motor squirrel cage that is deemed by its strength, high torque mass, and its relatively low cost ... etc, meanwhile, it benefited from the support of industry since its invention. Despite these advantages, these actuators are complex dynamic systems that exhibit a strong non-linearity, which makes them difficult to be controlled. So, the synergetic control is a solution for this problem because it is robust control despite the uncertainty on the model and external disturbances. A simulation results revealed some very interesting features. Key-Words Induction motor, field-oriented control, synergetic control theory.

 This paper deals with the problem of Hinfty performance analysis for 2D discrete state delayed system. A sufficient condition to have a disturbance attenuation bound of this class of systems described by the Fornasini-Marchesini state space model is addressed via Lyapunov techniques. This stability criterion is established using Leibniz-Newton formula with additional free weighting matrices. A numerical example is introduced to show the efficiency of the proposed criterion.

 In this article, we present at the first place the concept of the direct model reference adaptive control (DMRAC) algorithm which will be applied to the systems with time delay and with unmodeled dynamics. These unmodeled dynamics are represented as either additive or multiplicative incertitude in the transfer function, this algorithm will be applied to the linear variable systems such as SISO (Single-Input Single-Output). The Simulations show that the developed algorithm leads to an asymptotically stable error and the validity of the algorithm is illustrated by means of examples.

 Neural networks have successfully used for prediction and exchange rate forecasting. However, due to a large number of parameters to be estimated, the task of selecting an appropriate neural structure and its parameters is difficult. The performances of neural network are critically depending on learning algorithms for obtaining the best control parameters. Even when a suitable setting of parameters (weights) can be found, the ability of the resulting network to generalize the data during learning may be far from optimal. For these reasons it is suitable and attractive to apply an optimization method as genetic algorithms. In this paper, a Genetic algorithm is used for automating the design of neural network for exchange rates prediction. It provides a useful tool for neural parameters optimization. Simulation results show effectiveness of the proposed method for optimization of the predictor model using foreign exchange rate. This hybrid model exhibits effectively improved accuracy. Results are compared and discussed.

 The sensorless control of induction motor drives based on the properties of the observability constitutes a vast subject, and the technology has further advanced in recent years. In this paper we propose an adaptive neuro-fuzzy inference system for high performance induction motor drive. The simultaneous observation of rotor speed and stator resistance in induction drive is obtained through a neuro fuzzy observer trained with a backpropagation algorithm. The dynamic performance and robustness of the proposed neuro fuzzy adaptive observer are evaluated under a variety of operation conditions. The results demonstrate the effectiveness of the proposed structure.

 In this work, a genetic algorithm based approach for designing fuzzy inference systems (FIS) from data is developed. The FIS is implemented through an interconnected multi-layer network including neurons able to perform fundamental fuzzy operations called fuzzy neural network (FNN). Connections between neurons are weighted through binary and real weights. Then a simple and easy to implement multiobjective evolutionist algorithm is used to perform both parameters and structure optimization of the FNN by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The approach is tested on two examples: modeling of a well known temporal series and the control of the pole and cart system.

 This paper investigates the problem of robust control design for a class of uncertain nonlinear systems with time varying delay. By evaluating the derivative of a new fuzzy weighting-dependent Lyapunov-Krasovskii functional (FLKF), less conservative convex conditions derived in terms of Linear Matrix Inequalities (LMIs),are established.Two new results are presented. The first one is based on FLKF and the so-called the free weighting matrices and the second uses the bounding techniques. Finally, to compare the effectiveness of the proposed methods in term of the conservatism reduction, the simulation example is presented. Keywords: Takagi-Sugeno(TS), Stability, Stabilization, Linear Matrix Inequalities (LMIs), Fuzzy Lyapunov-Krasovskii functions( LKF).

 In this paper, the Takagi-Sugeno representation is used to represent the nonlinear behaviour of a saturated actuator. The control design is based on a state feedback controller function of the saturation levels. Stabilization conditions in the sense of Lyapunov method are derived and expressed as a linear matrix inequality problem. An academic example is presented with a comparaison between the proposed approach and a conventional anti-windup controller.

 In this paper, we are interested in the modeling and the regulation of discrete nonlinear systems affine in control. In this sense, we propose, first, to apply, in a discrete representation in input-output fuzzy description of nonlinear systems considered based on the structural properties of fuzzy systems of Takagi-Sugeno at constant conclusions, and secondly, to apply the fuzzy internal model control F.I.M.C as a robust control strategy.

 This paper presents the fuzzy control of switching Photovoltaic (PV) DC-DC power converters subject to parameter uncertainties and the state variables unavailable for measurements based on the Takagi- Sugeno (TS) fuzzy modeling approach. First, we derive the TS model for the switching converter. Second, a nonlinear robust fuzzy controller is designed accordingly. The TS fuzzy model is adopted for fuzzy modelling of the nonlinear system and establishing fuzzy state observers. The concept of General Design Approach (GDA) is employed to design fuzzy control and fuzzy observers from the TS fuzzy models. The proposed algorithm regulate the produced voltage and able to maintain the system stable during the parameter uncertainties. The design procedures are applied to a dynamics model of typical PV power control system using a DC-DC buck converter to illustrate the effectiveness of the proposed control techniques.

 This study investigated a linear quadratic AMD controller for five-story building under seismic ground motion. A critical review of classical optimal control algorithms is presented in this paper for the control of the electric-type active mass driver (AMD) system. Composed primarily of an electric servomotor and a ball screw, the electrical AMD system is free from noise problems, oil leakage, and labor-intensive maintenance that commonly are associated with hydraulic AMD systems. A numerical simulation is performed using a five-story steel frame under the Kanai-Tajimi seismic ground motion. The AMD system performances for different weighting factors valuesare assessed. The reductions of the peak responses can reach as high as 65% and the proposed system is recommended for practical implementation.

 This paper presents a modified control strategy of three-phase Photovoltaic grid-connected generation system. The PV system is connected to an electrical network where it contributes to frequency as well as terminal and DC-link voltage regulations. The innovation of the proposed control strategy is that it allows the Photovoltaic system to have a similar reaction to alterations in grid frequency as that of a traditional generator. The efficacy of the method is assessed by simulation in MATLAB/SIMULINK

 This paper presents a methodology of robust control applied on a nonlinear MIMO system, on the air path of a turbocharged diesel engine. The methodology consists in first analyzing the MIMO system to observe dynamic properties with several tools, i.e. condition number, relative gain array and Gershgorin bands, before designing the controller. Hence, a sequential robust controller synthesis is proposed to obtain a decentralized control taking into account coupling and uncertainties of the nonlinear system. The results obtained from a Mean Value Engine Model (MVEM) engine simulation model show the real time applicability of the proposed method and the good control performances (good response time, no steady state error) for various engine speeds

 The paper presents an advanced method of recognition of patient's intention to move of multijoint hand prosthesis during the grasping and manipulation of objects in a dexterous manner. The proposed method is based on two-level multiclassifier system (MCS) with heterogeneous base classifiers dedicated to EMG and MMG biosignals and with combining mechanism using a dynamic ensemble selection scheme and probabilistic competence fuction. Additionally, the feedback signal derived from the prosthesis sensors is applied to the correction algorithm of classification results. The performance of two MCSs with proposed competence function and combining procedure were experimetally compared against three benchmark MCSs using real data concerning the recognition of six types of grasping movements. The systems developed achieved the highest classification accuracies demonstrating the potential of multiple classifier systems with multimodal biosignals for the control of bioprosthetic hand.

 This paper presents a method for neural network sliding mode control design (NNSMC) to track the maximum power point (MPP) for a photovoltaic pumping system. For the best use, the photovoltaic (PV) panel must operate at its maximum power point (MPP). Sliding mode control (SMC) can be used for non linear systems with small uncertainties. However, for complex nonlinear systems, the uncertainties are large and produce higher amplitude of chattering due to the higher switching gain. In this work, sliding mode control approach is combined with the neural network (NN) to adjust the duty cycle control law. NN is used for the prediction of model unknown parts. Performance of the proposed controller is compared with the traditional SMC and investigated by simulation

 Several studies have considered control theory tools for traffic in communication networks, as for example the congestion control in IP (Internet Protocol) routers. In this paper, we will apply the Quantitative Feedback Theory to obtain an improved Active Queue Management control scheme. The resulting control law is validated through numerical network estimations

 In this short paper we present an example of output regulation of periodic signals for a plant (perturbed wave equation) in the setting of polynomially stable SISO system with bounded control and observation operators. The periodic signals are assumed generated by a suitable infinite-dimensional exosystem. the solvability of this problem is strongly related to the solvability of a pair of linear regulator equations.

 The Hinfty filtering problem for a class of discrete-time Takagi-Sugeno fuzzy systems is studied. Attention is focused on the design of on Hinfty filter such that the filter error system is asymptotically stable and preserves a guaranteed Hinfty performance. By using the fuzzy Lyapunov function approach and adding slack matrix variables, the coupling between the Lyapunov matrix and the system matrices is eliminated. Then, a linear matrix inequality (LMI)-based approach is developed for designing the Hinfty fuzzy filter. Finally, an illustrative example is provided to show the effectiveness of the proposed approach and less conservatism.

 This paper describes a neural direct torque control instead of the conventional DTC to reduce stator flux and electromagnetic torque ripples due to the hysteresis based scheme in open and closed loop. The results obtained from simulation confirmed the feasibility of the proposed technique compared to the conventional one.

 In this paper, a hybrid generation system for remote site is proposed. Two renewable energy sources : photovoltaic (PV) and wind, are controlled to deliver energy at optimum efficiency. Fuzzy logic control (FLC) is employed to achieve maximum power point (MPP) for both wind and PV generator using direct method, which requires no information about the generators characteristics nor climatic conditions. Several simulation results are given to show the effectiveness and the good performances of the proposed control.

 A simple sliding fuzzy type-2 controller scheme for nonlinear uncertain perturbed systems is proposed in the paper. To overcome the constraint on the knowledge of the system model, local models related to some operating points were used to synthesize a nominal fuzzy type-2 global model. The controller uses integral sliding mode concept and contains two parts. The first one leads to achieve finite time stabilization of the higher order input output dynamics without uncertainties. The second part has the object to reject bounded uncertainties throughout the entire response of the system. In order to eliminate the chattering effect, the sign function in this part control is substituted by a smooth variation fonction. Its structure conserves the characteristics of the original function. The sliding surface is computed using Slotine equation, this choice leads to reduce the relative degree of the system to one, which simplifies the design of the proposed method. The advantages of the controller are that its implementation is easy, the convergence time is chosen in advance and the robustness is ensured. A robot arm actuated by a DC motor is used as illustrative example. The obtained results show the good tracking performances and the applicability of the method.

 This paper deals with the problem of stabilization by output feedback control of Takagi-Sugeno (T-S) fuzzy discrete systems with a fixed delay by linear programming (LP) and cone complimentarity while imposing positivity in closedloop. The stabilization conditions are derived using the single Lyapunov-Krasovskii Functional (LKF). An example of a real plant is studied to show the advantages of the design procedure.

 This paper presents a method for a component fault detectability and isolability analysis based on bond graph model properties. The used analytical and graphical approaches need models under state space or set of constraints for this task. The innovative interest of proposed method is based only on the architectural representation of bond graph model deduced from physical system to be monitored before real design of online monitoring system. A developed methodology is illustrated by an application to a two tanks hydraulic system.

 This paper addresses the problem of designing a nonlinear observer for a general class of nonlinear systems in the presence of disturbances. Two criteria are combined in this approach : the modified H-infinity criterion and the modified mean value theorem. The first criterion is used to solve the problem of nonlinear observer design in the presence of disturbances. While the second allows to express the nonlinear error dynamics as a convex combination of known matrices with time varying coefficients. The sufficient conditions are derived and given in terms of linear matrix inequality. The advantage of the proposed method is that it can be applied to a wider class of nonlinear systems. Performances of the proposed approach are shown through the application to a diesel engine model.

 This paper contains a description of a new IMC controller design method, for a first order system with time delay. This new design method uses a low pass filter to fit model inverse, which offers parameters to ensure the robustness of the process behavior. This document shows the influence of filter order on the system behavior in order to obtain an optimal filter that ensures robustness and rapidity for the process. In this paper a brief description of the Internal Model Control will be given, a brief description of time delay effects on system behavior, a description of the new IMC controller design method and effects of filter order variation on the system behavior.

 The present work proposes a state observer with a cascade structure for a class of nonlinear systems in the presence of delayed output measurements. The first system in the cascade allows to estimate the delayed state while each of the remaining ones is a predictor. Each predictor estimates the state of the preceding one with a prediction horizon equal to a fraction of the time delay in such a way that the state of the last predictor is an estimate of the system actual state. The design of the observer is achieved by assuming a set of conditions under which the exponential convergence of the estimation error to zero is established. It was in particular shown that the number of the systems in the cascade depends on the magnitude of the considered delay.

 AbstractIn large-scale system, the infinite dimension and complexities interconnection of sub-systems are the major factors which the deteriorate overall system dynamic performance and potential overall interconnected system stability. Complex system which can decomposed in N-interconnected sub-systems is investigated in this paper. The continuous-time adaptive fault-tolerant control (FTC) and the quantitative feedback theory are used together to control the overall complex system with sensor failures. The global stability of overall system in the inner loop without affecting the nominal performance based on the generalized internal model control (GIMC) structure is studied. This existing technique is used to obtain the necessary global control performance in the outer loop and robustness as well. The simulations which are of evaluative example, are given to demonstrate that the centralized controller prove better reconfiguration of the overall system dynamic performance and global stability in presence of an additive sensor fault.

 This paper addresses the problem of H∞ control for a class of linear discrete-time periodic system. The obtained results are then extended for the periodic system with Linear Fractional Representation (LFR) uncertainty. Furthermore, linear matrix inequality (LMI)-based suffcient conditions for H∞ control are established. Numerical examples are introduced to illustrate the contribution of this paper.

 The regulation by sliding modes is especially recognized for its qualities of robustness and dynamic response. This paper will briefly point out the principles of the regulation by sliding mode as well as the extension of those to the continuous adjustment of the output voltage of a rectifier in cascade with a chopper and the improvement of the power-factor at the entry. Based on the choice of the sliding surface, various fashions of control are studied. Accordingly, a proposed sliding surface utilizing all the variables of state will be used, thus improving the performances of the system in terms of robustness and dynamic response. Real-time implementation is performed on an experimental test bench using a rapid prototyping tool. Results show that the proposed controller gives better performances under large load disturbance and plant uncertainties.

 This paper presents an observer based fuzzy control design method applied to an hydraulic system with a guaranteed Hinfty tracking performance. Our approach is based on Takagi-Sugeno (TS) fuzzy modeling method. For that, a fuzzy observer based tracking control with unmeasurable premise variables is developed to estimate the system states. The parallel distributed compensation (PDC) fuzzy controller is related to the tracking error. Stability conditions are derived from robust control techniques such as quadratic stabilization, Hinfty control theory and Linear Matrix Inequalities (LMIs), to obtain the gains of fuzzy control and fuzzy observers. Finally, simulation results will show the effectiveness of the fuzzy observer-based controller.

 A large, segmented space telescope requires high precision and accuracy in its mirror shape to obtain clear images. The SPACE Telescope Testbed at the Structures, Propulsion, and Control Engineering (SPACE) Laboratory must maintain a pointing control accuracy of 2 arc seconds. A Peripheral Pointing Architecture (PPA) has been designed to demonstrate the telescope pointing accuracy. A finite element model of the PPA is developed using FEMAP. Modal analysis is performed on the finite element model resulting in the natural frequencies, mode shapes, eigenvalues, mass matrix, stiffness matrix, and degree of freedom mapping of the PPA structure.

  In this paper, we present a numerical solution for an inverse heat problem to estimate 2D space-wise coefficient in a parabolic system describing heat transfer. The inverse problem is recast into an optimization problem solved with a conjugate gradient method. This approach use the nonlinear optimization formulation by minimizing a cost function taking into account both the measured outputs of the system and the outputs calculated by means of the model. The solution is performed using the free finite element software FreeFem. Numerical example is carried out to check the validity of the proposed method.

 This paper proposes a new approach of optimization in order to resolve multi-objective reactive power dispatch problem. This approach is called matrix multiobjective particle swarm which incorporates vector function as objective function and uses matrix computation to develop the Pareto front unlike the existing multi-objective algorithms which use an external archive. We apply this approach to resolve multi-objective reactive power dispatch problem of IEEE 10-bus system. Numerical tests are encouraging.

 Rigid link manipulators have attracted more and more attention from robot control theorists and robot users because of its various potential advantages. However, their nonlinear dynamics present a challenging control problem, since traditional linear control approaches do not easily apply. For a while, the difficulty was mitigated by the fact that manipulators were highly geared, thereby strongly reducing the interactive dynamic effects between links. At the same time more and more constraints, stemming for example from environmental and safety considerations, need to be satisfied. Often these demands can only be met when process constraints are explicitly considered in the controller. In this work a fixed step on head predictive control with input constraints has been applied to compute time optimal solutions for a two link manipulator operating in the horizontal plane subject to control angle positions in presence of input constraints (Torques) constraints. Tracking performances of the controller are investigated via some simulations, where the comparisons are done for the cases of unconstrained and constrained input