Randomization in Systems and Control
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Lecture Details
Marco Claudio Campi is Professor of Automatic Control at the University of Brescia, Italy.
In 1988, he received the doctor degree in electronic engineering from the Politecnico di Milano, Milano, Italy. From 1988 to 1989, he was a Research Assistant at the Department of Electrical Engineering of the Politecnico di Milano. From 1989 to 1992, he worked as a Researcher at the Centro di Teoria dei Sistemi of the National Research Council (CNR) in Milano and, in 1992, he joined the University of Brescia, Brescia, Italy. He has held visiting and teaching positions at many universities and institutions including the Australian National University, Canberra, Australia; the University of Illinois at Urbana-Champaign, USA; the Centre for Artificial Intelligence and Robotics, Bangalore, India; the University of Melbourne, Australia; the Kyoto University, Japan.
Prof. Campi is an Associate Editor of Systems and Control Letters, and a past Associate Editor of Automatica and the European Journal of Control. From 2002 to 2008, he served as Chair of the Technical Committee IFAC on Stochastic Systems (SS) and he is currently vice-chair for theTechnical Committee IFAC on Modeling, Identification, and Signal Processing (MISP). Moreover, he has been a distinguished lecturer of the Control Systems Society. Marco Campi's doctoral thesis was awarded the "Giorgio Quazza" prize as the best original thesis for year 1988. In 2008, he received the IEEE CSS George S. Axelby outstanding paper award for the article "The Scenario Approach to Robust Control Design", co-authored with G. Calafiore.
The research interests of Marco Campi include: randomized methods, robust convex optimization, system identification, adaptive and data-based control, robust control and estimation, and learning theory.
Designs in systems and control are traditionally carried out through deterministic algorithms consisting of a sequence of steps set by deterministic rules. This approach, however, can be generalized by the introduction of randomization: a randomized algorithm is an algorithm where one or more steps are based on a random rule, that is – among many deterministic rules – one rule is selected according to a random scheme. Randomization has turned out to be a powerful tool for solving a number of problems deemed unsolvable with deterministic methods.
A crucial fact is that randomization permits one to introduce the notion of ``probabilistically successful algorithm''. In many cases, when deterministic successfulness cannot be achieved, probabilistic successfulness offers a valid alternative.
In the talk, the use of randomized algorithms will be discussed in relation to several problems:
- robust design (e.g. by means of linear matrix inequalities - LMIs) is computationally difficult. Randomized methods come into play by offering alternative design methodologies;
- in estimation and identification, randomization can change our perception of what constitutes an impossible problem, and estimation problems which were deemed impossible can be successfully solved in a probabilistic sense;
- trading robustness for performance is a central issue in decision making and new methods for achieving a compromise can be obtained through randomization.