This book comprises a selection of papers that were first presented at the Robustness in Identification and Control Workshop, held in Siena, July 30 - August 2, 1998. These are the latest contributions to the field, from leading researchers worldwide. The common theme underlying all of the contributions is the interplay between information, uncertainty and complexity in dealing with modelling, identification and control of dynamical systems. Papers cover recent developments in research areas such as identification for control and the classical area of robust control. There are a number of real-world case studies where the most advanced robustness analysis and synthesis techniques are applied to resolve previously unsolved problems. The relevance of the topic to the system engineering field, and the excellent scientific level of the contributions combine to make this book an important acquisition for engineers, control theorists and applied mathematicians.

It is well known that a large number of problems relevant to the control ?eld can be formulatedas optimizationproblems. For long time, the classical approachhas been to look for a closed form solution to the speci?c optimizationproblems at hand. The last decade has seen a noticeable shift in the meaning of "closed form" solution. The formidable increase of computationalpower has dramatically changed the fe- ing of theoreticians as well as of practitioners about what is meant by tractable and untractableproblems. A main issue regardsconvexity. From a theoretical viewpoint, there has been a large amount of work in the directions of "convexifying" nonc- vex problems and studying structural features of convex problems. On the other hand, extremely powerful algorithmsfor the solution of convexproblemshave been devised in the last two decades. Clearly, the fact that a wide variety of engine- ing problems can be formulated as convex problems has strongly motivated efforts in this direction. The control ?eld is not an exception in this sense: many pr- lems in robust control, identi?cation and nonlinear control have been recognized as convex problems. Moreover, convex relaxations of nonconvex problems have been intensively investigated, as they provide an effective tool for bounding the optimal solution of the original problem. As far as robust control is concerned, it is known since long time that several classes of problemscan be reducedto testing positivity of suitable polynomials.