Mathieu Pouliquen

Output error identification for multi-input multi-output systems with bounded disturbances

In this paper, we present the adaptation of an Optimal Bounding Ellipsoid (OBE) type algorithm for output error systems with unknown but bounded disturbances. Two identification algorithms are derived in a multi-input multi-output context and the paper proposed stability and convergence analysis. An iterative scheme is introduced and simulation are performed, showing the excellent performance of the proposed approach.

An indirect closed loop subspace identification method

In this paper, we consider a closed-loop subspace identification problem. An indirect method is developed using exogenous input and knowledge of a part of the controller impulse response. The idea is to extract dynamic of the plant from dynamic of the closed loop system. Two main result allows this double estimation. Only the deterministic behavior of the plant is considered in this paper. A simulation example is given to illustrate the performances of the present method.

Bounded-error identification for closed-loop systems

This paper presents a scheme for the identification of a system which operates in closed-loop and in the presence of bounded output disturbances. Two algorithms are proposed to solve this identification problem. The first algorithm is an Optimal Bounding Ellipsoid (OBE) type algorithm. This first algorithm is analyzed and sufficient conditions for stability and convergence are established. Relaxation of these conditions leads to a second identification algorithm. The implementation of that second algorithm is realized in an iterative scheme. A numerical example is provided to show the efficiency of the scheme.

Closed loop output error identification with bounded disturbances

The problem of closed loop system identification in presence of bounded disturbances is considered. In this paper two recursive algorithms are proposed to solve this identification problem. Stability and convergence properties are demonstrated. Simulation results validate the proposed solutions.

Recursive identification for Hammerstein systems with bounded noise

In this paper, we consider the identification of Hammerstein systems in presence of bounded disturbances. The proposed identification algorithm is a recursive algorithm. It ensures that the estimation error is bounded and allows a real-time separation of the nonlinear part and linear part. Furthermore, some stability and convergence results are demonstrated and a simulation example is given to illustrate the performance of the presented method.

Subspace identification of Hammerstein systems with nonparametric input backlash and switch nonlinearities

In this paper, a subspace identification algorithm for a class of Hammerstein systems is developed. We consider dynamical systems subject to input backlash or switch nonlinearities. The idea is to use a specific input signal allowing the estimation of the nonlinear part and the estimation of a state space model for the linear part. The identification algorithm is a subspace type algorithm. A simulation example is given to illustrate the performances of the present method.

Proportional integral observer design for time delay nonlinear triangular systems

In this work an proportional integral observer for a class of delayed nonlinear triangular systems with multiple and simple time-delay is proposed. First, the proportional integral observer synthesis is presented for nonlinear systems with variable time-delay and is extended for nonlinear systems with multiple time-delay. The observer performance is evaluated through numerical simulations.

Blind equalisation in the presence of bounded noise

This study addresses the blind equalisation problem in the presence of bounded noise using an optimal bounding ellipsoid algorithm. This provides an adequate blind equalisation algorithm with an accurate parameter estimation. A fundamental analysis of the involved equaliser is performed to emphasise its underlying properties. This fundamental result is corroborated by promising simulation results.

Identification of circadian rhythm

We present an identification algorithm for determining the variables characterizing a circadian rhythm. This method is based on a recursive least square algorithm combined with an appropriate observer. It has the advantage of determining all the parameters of a time-varying circadian rhythm without requiring the knowledge of the period. A comparison study with respect to the Lomb & Scargle method has been carried out to show the effectiveness of the proposed method. The presented algorithm has been applied to data collected during a laboratory experiment.

Estimation des paramètres électriques de la machine asynchrone à partir des méthodes des sous-espaces à temps continu

This paper proposes a method for the identification of physical parameters involved in a model of induction motors. Such an identification is performed using a subspace identification method based on a continuous time parsimonious parametrization of the moteur dynamics. The performances of the proposed method are illustrated throughout experimental data.