Elom Ayih Domlan

Switching systems: active mode recognition, identification of the switching law

The problem of the estimation of the discrete state of a switching system is studied. The knowledge of the switching law is essential for this kind of system as it simplifies their manipulation for control purposes. This paper investigates the use of a model-based diagnosis method for the determination of the active mode at each time point based on the system input/output data. The issue of the parametric identification of the switching law is also addressed.

Active Mode Estimation for Switching Systems

Switching systems are a particular class of hybrid systems. They are described by several operating regimes, called modes, each of them being active under certain particular conditions. When the switching mechanism is perfectly known, it is easy to handle such systems because the knowledge of the active regime at any moment is available. On the other hand, in the case where there is no information about the switching mechanism, the situation is more complicated. In this configuration, it is difficult to carry out a fault diagnosis scheme or to synthesize a control law. This paper addresses the issue of the determination of the active mode at any moment, using only the systems input/output data. Conditions that guarantee the uniqueness of the determined active mode are also given.

Switching Time Estimation of Piecewise Linear Systems. Application to Diagnosis

Analysing the behaviour of physical systems often leads to realise that a natural representation consists in building mixed continuous / discrete models. Multimodels or hybrid models are adapted representations for complex physical systems by introducing transitions (smooth or not) between local behaviours. This paper presents some technical points dealing with the determination of the time transition from one local model to another one. More generally, our purpose is to estimate, at each time, the state of the associated process.

Multiple Model Approach for Inferential Instruments Design

Abstract In this paper, an application of a multiple model approach for the design of inferential instruments is reported. The multiple model of interest presents a decoupled structure in the sense that the sub-models do not share the same state variable. A two-stage identification procedure is developed for the model identification and a soft sensor application is later conducted with the decoupled multiple model structure. The soft sensor aims at predicting a quality variable for an industrial separation unit. The decoupled multiple model structure allows obtaining a dynamical model for the soft sensor despite the presence of practical constraints related to multi-rate sampling problem. Real-time implementation results are presented.