Nasreddine Bouguila

Multiple observer design for a non-linear Takagi–Sugeno system submitted to unknown inputs and outputs

In this study, the authors focus on the state estimation of a non-linear system described by a Takagi–Sugeno multiple model submitted to unknown inputs and outputs. The proposed approach consists of a mathematical transformation which enables to consider the unknown outputs as unknown inputs that can be eliminated by a designed multiple observer. To evaluate the efficiency of the proposed approach, the convergence conditions of the state estimation error are formulated as linear matrix inequalities. Simulation examples are given to illustrate the proposed methods.

Design of unknown inputs and multiple integral observers for Takagi-Sugeno multiple model

In this paper, a proportional multiple integral observer (PMI) and a proportional integral observer with unknown inputs (PIUI) are proposed in order to estimate the state, the actuator and the sensor faults of nonlinear systems described by a Takagi-Sugeno multiple model. The convergence of the estimation errors between the system and each observer are studied using the Lyapunov theory. Academic examples are provided in order to illustrate the proposed methods. A comparaison between the two observers is made through mobile robot.

Online process monitoring based on kernel method

This paper discusses the monitoring of dynamic process. In recent years, Kernel Principal component analysis (KPCA) has gained significant attention as a monitoring method of nonlinear systems. However, the fixed KPCA model limit its application for dynamic systems. For this purpose a new Variable Moving Window Kernel PCA (VMWKPCA) method is introduced to update the KPCA model. The basic idea of this technique is to vary the size of the moving window depending on the normal change of the process. Then the VMWKPCA method is performed for monitoring a Chemical reactor (CSTR). The simulation results proved that the new method is effective.

Identification of nonlinear systems with kernel methods

This paper present a nonlinear system identification based kernel methods, such as regularization networks, support vector regression and kernel principal component analysis. In this case, black-box models are used in a particular space named reproducing kernel Hilbert space (RKHS) which only considered the input/output signals of the nonlinear system. In this particular space, the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. To prove the performances of the kernel methods, identification examples are illustrated with three single-input single-output (SISO) benchmark models.