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Such decisions usually rely on mathematical models that may have to be built, either from basic principles and laws of physics, chemistry, biology, etc. (white-box or knowledge-based models) or purely from input-output behaviour (black-box models), with all the possible grey-box models in between.

Thus modelling, and parameter estimation, often called identification, are important ingredients. Also important is the ability to infer, from the available data and using the model, the values taken by variables that are not directly accessible to measurement but play a critical role in the decision process. Depending on the community, this is known as state estimation, or observation or software sensing. Because of the imperfect nature of the model and of the unavoidable perturbations, control usually involves some feedback, whereby output information collected via sensors, and suitably processed, is used to modify the input signals as implemented by actuators, in order to improve performance of the actual system to be controlled. Critical then is the ability to ensure the stability of the controlled system. When accurate models are available and the objectives to be pursued easily translate into an optimality criterion, as is often the case in spatial control problems, optimal control is a natural approach. In many cases, however, the situation is far less clear-cut. Robustness of performance to discrepancy between the actual system and its mathematical model and adaptability of the control system to changing conditions may then become more important than optimality.

The techniques and tools to be mobilised in the design of decision and control systems pertain to artificial intelligence, control theory, optimisation, signal processing and statistics.

Digiteo is interested in all aspects of decision and control systems, with a strong emphasis on black-box modelling, parameter and state estimation, nonlinear dynamical systems, the robust control of uncertain systems, constrained control and trajectory planning. Much is expected from the collaboration of Digiteo groups that attack the same problems with different tools and from different angles.

Here is a non-limitative list of examples:

• In nonlinear state estimation, specialists of control, signal processing and statistics need to work together to deal with nonlinear, uncertain dynamical models and produce estimators that take uncertainty explicitly into account, as well as the intended use of the estimate.

• In black-box modelling, artificial intelligence, control theory and statistics each contribute their set of tools. Attempts to bridge gaps should be encouraged.

• In control, it is often assumed that the model and state of the system are available at the outset. The interactions between modelling, state estimation and control in a context of a very partial knowledge on the system and of unavoidable perturbations need to be investigated.

• Some teams that may not consider themselves as working on decision and control systems do deal with actual uncertain dynamical systems that may need to be modelled and on which decisions have to be taken. Other teams that develop control and decision methodologies do not have the equipment, skills and manpower required to deal with actual applications, e.g., in biology, communication networks, grid computing, physics, robotics. Both types of teams should benefit from common application projects.

• Control devices may be embedded in the system to be controlled, distributed on a network, which may itself be used to control distributed objects. Interactions between control theory, telecommunications and computer science should therefore play an important role. See also the section about hybrid systems.