This volume collects most of the papers presented at the International Workshop on Robustness in Identification and Control, held in Torino (Italy) in 1988. The main focal point of the workshop was Unknown But Bounded uncertainty and associated robustness issues in identification and control. Recent years have seen a growing interest in studying models which include un- known but bounded uncertainty. The motivation for dealing with such models is derived from robustness considerations. In many applications, some performance specification must be met for all admissible variations of the uncertain parameters. A second motivation for models with this type of uncertainty stems from the fact that the statistical description of uncertain variables may not be well known or even not suitable. For example, in some cases, only a small number of measurements is available and the resulting errors are due to analog-digital conversion, modelling ap- proximation or round-off, so that a statistical description may actually be unreliable. The interest in unknown but bounded setting is certainly not new. In fact, en- gineering practice demands for appropriate algorithms in dealing with finite sample properties, finite parameter variations, tolerance analysis, etc. Despite the natural need for such methods, the lack of sufficiently well assessed theoretical results and algorithms prevented a systematic use of these procedures until recent years. How- ever, in the last few years, important advances have been made both in estimation theory and in stability analysis.

This volume collects most of the papers presented at the International Workshop on Robustness in Identification and Control, held in Torino (Italy) in 1988. The main focal point of the workshop was Unknown But Bounded uncertainty and associated robustness issues in identification and control. Recent years have seen a growing interest in studying models which include un- known but bounded uncertainty. The motivation for dealing with such models is derived from robustness considerations. In many applications, some performance specification must be met for all admissible variations of the uncertain parameters. A second motivation for models with this type of uncertainty stems from the fact that the statistical description of uncertain variables may not be well known or even not suitable. For example, in some cases, only a small number of measurements is available and the resulting errors are due to analog-digital conversion, modelling ap- proximation or round-off, so that a statistical description may actually be unreliable. The interest in unknown but bounded setting is certainly not new. In fact, en- gineering practice demands for appropriate algorithms in dealing with finite sample properties, finite parameter variations, tolerance analysis, etc. Despite the natural need for such methods, the lack of sufficiently well assessed theoretical results and algorithms prevented a systematic use of these procedures until recent years. How- ever, in the last few years, important advances have been made both in estimation theory and in stability analysis.