15th Triennial World Congress of the International Federation of Automatic Control
  Barcelona, 21–26 July 2002 
A GENERAL FRAMEWORK FOR ITERATIVE LEARNING CONTROL
Ola Markusson*, Håkan Hjalmarsson* and Mikael Norrlöf**
* Dept. of Signals, Sensors, & Systems,
Royal Institute of Technology (KTH)
Stockholm, SWEDEN
ola.markusson@s3.kth.se hakan.hjalmarsson@s3.kth.se,
** Dept. of Electrical Engineering
Linköping University
Linköping, SWEDEN
mino@isy.liu.se

In this contribution we place ILC in the realm of numerical optimization. This clarifies the role played by the design variables and how they affect e.g. convergence properties. We give a model based interpretation of these design variables and also a sufficient condition for convergence of ILC which is similar in spirit to the sufficient and necessary condition previously derived for linear systems. This condition shows that the desired performance has to be traded against modeling accuracy. It is also shown how the design variables should be selected to eliminate noise effects. Finally, one of the main benefits of ILC when non-minimum phase systems are concerned, the possibility of non-causal control, is given a comprehensive coverage.
Keywords: Iterative methods, Learning control, Nonlinear systems, Convergence analysis, Non-minimum phase systems
Session slot T-We-A21: Posters of Nonlinear Systems/Area code 2c : Non-linear Systems