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.Robustness in identification.- Comments on model validation as set membership identification.- SM identification of model sets for robust control design from data.- Robust identification and the rejection of outliers.- Semi-parametric methods for system identification.- Modal robust state estimator with deterministic specification of uncertainty.- The role of experimental conditions in model validation for control.- Modeling and validation of nonlinear feedback systems.- Towards a harmonic blending of deterministic and stochastic frameworks in information processing.- Suboptimal conditional estimators for restricted complexity set membership identification.- Worst-case simulation of uncertain systems.- On performance limitations in estimation.- Design criteria for uncertain models with structured and unstructured uncertainties.- Robustness and performance in adaptive filtering.- Nonlinear identification based on unreliable priors and data, with application to robot localization.- Robust model predictive control: A slsŒ