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In control and signal processing, adaptation is a natural tool to cope with real-time changes in the dynamical behaviour of signals and systems. In this area, strongly connected with prediction and identification, there has been an increasing interest in switching and supervising methods. Moreover in recent years, special attention has been paid to the ideas evolving round the theory of statistical learning as a potential tool of improved adaptation.



The IFAC workshop on Adaptation and Learning in Control and Signal Processing in 2001 gathered together experts in the field and interested researchers from universities and industry to present a full picture of the area. This proceedings volume presents papers covering the following subjects: Model reference and predictive control; Multiple model control; Adaptive control I/II; Adaptive control and learning; Learning; Adaptive control of nonlinear systems I/II; Supervisory control; Neural networks for control; PID design methods; Sliding mode; Adaptive filtering and estimation; Identification methods I/II.

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Table of Contents

Plenary paper. Model reference and predictive control. Applications to mechanical and bio-mechanical systems. Adaptive control and learning. Neural networks for control. Multiple model control. Adaptive control I. Learning. PID design methods. Adaptive control of nonlinear systems I. Applications to power plants and software tools. Supervisory control. Sliding mode. Plenary paper II. Adaptive filtering and estimation. Application to mechanical systems. Identification methods I. Adaptive control of nonlinear systems II. Identification methods II. Adaptive control II

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