1. Robust Identification and Complexity.- Estimation and prediction in the presence of unknown but bounded uncertainty: a survey.- Optimal sampling design for parameter estimation and p-widths under stochastic and deterministic noise.- How useful is nonadaptive information for ordinary differential equations?.- Fast algorithms for the computation of fixed points.- Bounding techniques for model-structure selection.- Robust linear and nonlinear parameter estimation in the bounded-error context.- 2. Robust Stability and Control.- Generalized Nyquist tests for robust stability: Frequency domain generalizations of Kharitonov's theorem.- Extending Kharitonov's theorem to more general sets of polynomials.- Strong Kharitonov theorem for discrete systems.- Polytopes of polynomials with zeros in a prescribed region: new criteria and algorithms.- Robustness bounds for classes of structured perturbations.- Markov's theorem of determinants and the stability of families of polynomials.- An application of state space methods to obtain explicit formulae for robustness measures of polynomials.- Robust stability and stabilization of interval plants.- Shaping conditions and the stability of systems with parameter uncertainty.- Structured and simultaneous Lyapunov functions for system stability problems.- Robust stability of polynomials with multilinear parameter dependence.- Stability conditions for polynomials via quadratic inequalities in their coefficients.- Boundary implications for interval positive rational functions: preliminaries.- Guaranteeing ultimate boundedness and exponential rate of convergence for a class of uncertain systems.- On measures of stability robustness for linear uncertain systems.- Robust stabilization of linear time-invariant systems via linear control.- U-Parameter design: feedback system design with guaranteed robust stability.- New criteria for robust stability.- Author Index.
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.
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