Periodic control has reached a notable degree of maturity thanks to developments over the last few decades. We have seen not only major theoretical achievements but also new significant applications.

The IFAC workshop on "Periodic Control Systems" (PSYCO 2001), held at the Villa Erba Congress Centre in Cernobbio-Como (Italy), August 27-28 2001, aimed at presenting the full picture of the area by gathering experts in the field and all interested researchers, coming from universities, research institutions and industries.

The program consisted of technical sessions, organized in two parallel streams and two plenary lectures, given by Jason L. Speyer (University of California at Los Angeles, USA) and Yutaka Yamamoto (Kyoto University, Japan). The technical sessions included 42 papers covering the following subjects:

Periodic Systems Analysis
Application
Time-Series
Hybrid and Sampled-Data
Aerospace Applications
Periodic Systems Control
Numerical Methods
Multirate and Batch Processes
Repetitive and Nonlinear Control

A dozen of the papers were devoted to a number of applications, including aerospace, jet and diesel engines, gas turbines, nuclear reactors, power systems, satellites, environmental sciences and finance.
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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.