The Modeling of Uncertainty in Control Systems: Proceedings of the 1992 Santa Barbara Workshop

by Roy S. Smith (Editor), Mohammed Dahleh (Editor)

Overview

This book is a collection of work arising from a NSF/ AFOSR sponsored workshop held at the University of California, Santa Barbara, 18-20th June 1992. Sixty-nine researchers, from nine countries, participated. Twelve keynote essays give an overview of the field and speculate on future directions and nineteen technical papers delineate the state of the art in the field. This book serves both as in introduction to the topic and as a reference on the current technical problems and approaches.

Product Details

ISBN-13:	9783540198703
Publisher:	Springer Berlin Heidelberg
Publication date:	01/14/1994
Series:	Lecture Notes in Control and Information Sciences , #192
Edition description:	1994
Pages:	396
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Identification and robust control.- An essay on identification of feedback systems.- Thoughts on identification for control.- An essay on robust control.- On the character of uncertainty for system identification and robust control design.- Extensions of parametric bounding formulation of identification for robust control design.- Connecting identification and robust control.- On nominal models, model uncertainty and iterative methods in identification and control design.- An informal review of model validation.- From data to control.- Is robust control reliable?.- Modeling uncertainty in control systems: A process control perspective.- A note on H— system identification with probabilistic a priori information.- A worst case identification method using time series data.- Identification in H— using time-domain measurement data.- Identification of feedback systems from time series.- Input-output extrapolation-minimization theorem and its applications to model validation and robust identification.- Identification of model error bounds in l 1- and H—-norm.- Asymptotic worst-case identification with bounded noise.- Sequential approximation of uncertainty sets via parallelotopes.- A robust ellipsoidal-bound approach to direct adaptive control.- On line model uncertainty quantification: Hard upper bounds and convergence.- A mixed deterministic-probabilistic approach for quantifying uncertainty in Transfer Function Estimation.- Estimation for robust control.- Non-vanishing model errors.- Accuracy confidence bands including the bias of model under-fitting.- Iterative identification and control design: A worked out example.- Frequency domain identification for robust control design.- Time domain approach to the design of integrated control and diagnosis systems.-Identification of Ill-conditioned plants — A benchmark problem.- Control design and implementation based on experimental wind turbine models.