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.


For experts and researchers in the field of signal processing.

Table of Contents

Selected Papers. Plenary Paper.
Nonlinear identification and adaptive control of combustion engines (R. Isermann, N. Müller)

Model Reference and Predictive Control.
Identification-oriented predictive control (L.C. Kammer, G.A. Dumont). An alternate new gap metric for robustness measure (L. Keviczky, Cs. Bányász). Model reference adaptive control of non-linear multivariable systems using an interactor structure estimation (Y. Mutoh). Robust MRAC of a linear time-varying parabolic system with bounded disturbance (K-J. Yang et al.).

Applications to Mechanical and Bio-Mechanical Systems.
Identification for the moving objects by using the observed image data (X. Chen, G. Zhai) Identification of a biomechanical system using neural networks (T. Schauer et al.). Decentralized adaptive continuous-time control of coupled drives apparatus (V. Bobál et al.). A simplified model for head-neck segment fast movements in the frontal plane (A. Pedrocchi et al.).

Adaptive Control and Learning.
A Comparison of optimal iterative learning control schemes (M. Rzewuski et al.). Virtual reference feedback tuning: a new framework for data-based design of PID and linear controllers (A. Lecchini et al.). Efficient linear MIMO adaptive inverse control (G.L. Plett). Norm optimal iterative learning control applied to chain conveyor systems (T. Al-Towaim et al.).

Neural Networks for Control.
RBFN model adaptation based on orthogonal decomposition


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© 2002
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About the editor

S. Bittanti

Affiliations and Expertise

Dipartimento di Electtronica e Informazione, Politecnico di Milano, Italy