Mohamed Ben Ali Kamoun

Adaptive observers for nonlinearly parameterized class of nonlinear systems

In this paper, one proposes adaptive observers for a class of uniformly observable MIMO nonlinear systems with general nonlinear parameterizations. The state and the unknown parameters of the considered systems are supposed to lie in bounded domains which size can be arbitrarily large and the exponential convergence of the observers is shown to result under a well-defined persistent excitation condition. Moreover, the gain of the observers involves a design function that has to satisfy a simple condition which is given. Different expressions of such a function are proposed and it is shown that adaptive high gain like observers and adaptive sliding mode like observers can be derived by considering particular expressions of the design function. The theory is supported by simulation results related to the estimation of the biomass concentration and the Contois model parameters in a bioreactor.

New approach to block-diagonalization of singularly perturbed systems by Taylor expansion

Block-diagonalization of a singularly perturbed system requires the solution of the Riccati equation and the Lyapunov equation. A new approach is suggested for both equations, using Taylor expansions. The convergence is studied in detail; it is shown that it is ensured under restrictions which are less restrictive than the case of previously suggested methods. >

POWER FLOW ANALYSIS AND EFFICIENCY OPTIMIZATION OF A DOUBLY FED SYNCHRONOUS MACHINE

ABSTRACT This paper deals with the energy balance of the doubly fed synchronous machine (DFSM). The proposed method is based on the principle which consider that the electro–mechanical conversion takes place between stator circuits and the machine shaft, so that the electrical power applied to rotor circuits is taken as a command. From this point of view, the study stresses are: Stability, A low ratio of the rotor command referred to the converted energy, High efficiency. Results are presented in the rms value of rotor applied voltage (V2a) – angle between the stator and the rotor voltages vectors (5) plane. We have found, when we vary the slip (s), that the high efficiency areas move in such way that the ratio of s over V2a remains constant (which means a constant rotor flux).

Stabilization of unknown nonlinear systems using neural networks

This paper deals with stabilization of unknown nonlinear systems using a nonlinear controller made with a backpropagation neural network. Control strategies based on an inverse state neural model built from an off-line learning step are proposed. The proposed strategies can be implemented following two approaches. The first one consists on computing control horizon based on actual state vector and desired one at a future instant. The second approach applies control action in the sense of a receding horizon. Adaptive control has been considered where the updating of the neural controller is accomplished to optimize different control objectives.

Observer-Based Output Feedback Controller for a Class of Nonlinear Systems

Abstract The design of an observer based output feedback controller for a class of uniformly observable nonlinear systems with an admissible tracking capability, is proposed. Two fundamental features of the proposed control scheme are worth to be mentioned. The first one consists in the high gain nature of the underlying state feedback control and observer designs. More specifically, a unified high gain control design framework is proposed thanks to the duality between control and observation. The second feature consists in incorporating a filtered integral action into the control design. The filtering is mainly motivated by measurement noise sensitivity reduction while the integral action allows to achieve a robust offset free performance in the presence of step like disturbances. The features of the proposed approach are illustrated using the induction motor model and simulation are performed in order to highlight the performance of the underlying observer based output feedback controller.

Investigation on the Excitation Capacitor for a Wind Pumping Plant Using Induction Generator

This paper presents a SEIG-IM system using a self excited induction generator driven by wind turbine and supplying an induction motor which is coupled to a centrifugal pump. A method to describe the steady state performance based on nodal analysis is presented. Furthermore, a dynamic analysis and performance characteristics are examined. The pro-posed methodology is discussed in order to optimize the quantity of the pumped water. Therefore an optimal excitation capacitor for a given wind rotor speed is determined and a suitable operation mode of the system is established.

Two step three time scale reduction of doubly fed machine models

A three time scale reduction method is presented, which consists of two successive two time scale seductions. The advantage is that only small order systems of equations are dealt with. This method is applied to doubly fed synchronous machines, and is compared with other methods. A discussion of the dynamic behavior according to inertia and operating point is also included. >

Control of a loaded induction machine using a feedforward neural network

Abstract Neural networks have been the subject of great interest in the control field. They seem to offer good ability to solve complex tasks. In this paper, some results of using a neural network for the nonlinear control of an induction machines speed are presented. The neural controller is a feedforward neural network identified off-line. The backpropagation learning algorithm has been used for the off-line identification of the plant inverse neural model which provides the control action. Simulations under measurement noise and environmental condition variations have been investigated and comparison of performance has been made with an adaptive neural controller. The obtained results show the efficiency and the implementation simplicity of the presented strategy

Brief paper: Adaptive observers for nonlinearly parameterized class of nonlinear systems

In this paper, one proposes adaptive observers for a class of uniformly observable MIMO nonlinear systems with general nonlinear parameterizations. The state and the unknown parameters of the considered systems are supposed to lie in bounded domains which size can be arbitrarily large and the exponential convergence of the observers is shown to result under a well-defined persistent excitation condition. Moreover, the gain of the observers involves a design function that has to satisfy a simple condition which is given. Different expressions of such a function are proposed and it is shown that adaptive high gain like observers and adaptive sliding mode like observers can be derived by considering particular expressions of the design function. The theory is supported by simulation results related to the estimation of the biomass concentration and the Contois model parameters in a bioreactor.

Field-oriented control induction machine and control by sliding mode

Abstract This paper deals with the implementation of an “optimal” field oriented control on an induction machine. In a first step, an optimal control law is applied, which minimizes the time, to establish the required flux for a field oriented control. When the flux magnitude and direction reach their references, the machine speed control is performed by a sliding mode regulator. Simulation results show that the machine presents a high dynamic. They also show that the sliding mode control is robust.