The problem of observer design naturally arises in a system approach as soon as one needs some internal information from external (directly available) measurements. In general, it is clear that one cannot use as many sensors as there are signals of interest characterizing the system behavior, for example, for cost reasons or technological constraints, especially since such signals can come in large numbers and can be of various types. They typically include time-varying signals characterizing the system (state variables), constant signals (parameters), and unmeasured external signals (disturbances).

This need for internal information can be motivated by various purposes: modeling (identification), monitoring (fault detection), or driving (control) the system. All those purposes are jointly requested to maintain a controlled system. This makes the reconstruction - or observer - problem the heart of a general control problem.

The goal of this Special Issue is to give contributions on possible tools for observer design and the related problems of control, fault detection, or parameter identification. In short, an observer relies on a model with on-line adaptation relaying on available measurements and aiming at information reconstruction, that is to say, it can be characterized as an information reconstructor – a model-based, measurement-based, closed-loop. Actually, the model is a state-space representation, and it will be assumed here that all pieces of information to be reconstructed will be borne by state variables. Faced with this, we can design an explicit dynamical system whose state gives an estimate of the actual state of the considered model, or just settle the problem as one of optimization.

Potential topics include but are not limited to the following:

• Observation analysis of certain classes of nonlinear systems
• State reconstruction for physical nonlinear systems, including electrical, mechanical, or biological systems
• Observers, model-based controllers, and separation principles
• Fault estimation
• Stochastic observation
• Identification via state estimation model
• Genetic algorithm-based observer design