Model Reduction of Nonlinear Control Systems in Reproducing Kernel Hilbert Space

Add to your list(s) Download to your calendar using vCal

• Dr. Boumediene Hamzi, Imperial College London
• Wednesday 16 March 2011, 14:00-15:00
• CAP Seminar Room .

If you have a question about this talk, please contact Alessandro Astolfi.

We introduce a novel data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction. The method rests on the assumption that the nonlinear system behaves linearly when lifted into a high (or infinite) dimensional feature space where balanced truncation may be carried out implicitly. This leads to a nonlinear reduction map which can be combined with a representation of the system belonging to a reproducing kernel Hilbert space to give a closed, reduced order dynamical system which captures the essential input-output characteristics of the original model. Empirical simulations illustrating the approach are also provided. This is joint work with J. Bouvrie (Duke University).

This talk is part of the Control and Power Seminars series.

This talk is included in these lists:

• Andrea Picciau's list
• CAP Seminar Room
• COMMSP & CP list
• COMMSP Seminar
• Control and Power Seminars
• Featured talks
• SAP Talks

Note that ex-directory lists are not shown.