Organizers: Ali Zemouche, Zehor Belkhatir, Ankush Chakrabarty, Rajesh Rajamani
Submission code: 7f2x9
Abstract
The objective of this open invited track proposal, to be included in the IFAC world congress 2023,
which will be held in Yokohama, Japan, July 09-14th, 2023, consists in inviting contributions in the field
of linear and nonlinear estimation and their applications in many theoretical and practical problems.
The main goal is to bring together experts in the field of estimation and application-oriented
researchers to create fruitful discussions on the recent advances and identify some future directions.
Keywords: Estimation; observer design; nonlinear systems; learning-based techniques.
Description
The proposed open invited track session focuses on fundamental issues in estimation theory
as well as on applications to real-world models such as biomedical systems, biological processes, smart
mobility, economics, and so on. It aims to provide conference attendees with the opportunity to
experience state-of-the-art solutions and tools to address any estimation problem, namely
optimization-based estimation methods and observer design.
Estimation theory in general, and more importantly state observer design, are increasingly
becoming a hot research topic for control design problems under all their aspects. Although they have
considerably marked the last decades with remarkable advances in the field, with well-thought-out
theoretical contributions, they are coming back even stronger for two reasons. Firstly, with the
integration of new technologies in control design systems, new applications have appeared, whose
estimation is essential. For instance, we can mention cyber-physical systems where estimation plays
an important role for resilience and security, namely detection and estimation of cyber-physical
attacks. More recently, estimation has a considerable place in artificial intelligence; there are several
estimation methods and observer design based on online learning techniques. On the other hand,
there has recently been an awareness of the weaknesses of existing methods that many works
attempt to overcome. Among these weaknesses, to name just one, are the required assumptions on
the system.
Several powerful techniques for nonlinear estimation and observer design have been
developed and explored in the last few years. Further, the need for nonlinear filters and observers has
been felt and pursued in many new and essential applications.
Besides the nonlinearity of the system, there are several other families of linear systems which
are complex from estimation point of view. Without being exhaustive, we can mention linear systems
with unknown inputs, systems with unknown constant parameters, systems in the presence of
unknown time-varying delays, and so on. These classes of systems require the development of
unknown input observers, adaptive observers, and robust observers. Several challenging issues are
related to these topics, namely the requirement of strong rank conditions for the existence of an
algorithm allowing estimation of the unknown inputs, the constant parameters, or the unknown
delays.
Topics include, but are not limited to:
- State observer design for nonlinear systems: LMI-based techniques, high-gain methodology, sliding mode technique;
- Estimation algorithms based on minimization of cost functions: moving horizon estimators, Kalman filtering;
- Statistical predictors and filters based algorithms;
- Unknown input observers, descriptor systems;
- Application to fault diagnosis ;
- Cyber-physical attacks detection ;
- Real-world applications: biomedical models, biological processes, vehicle dynamic, economics; and so on.
