Structured Lipschitz Models: Definition, Examples, and Behavior
Dr. Ronald K. Pearson
Zeit: Dienstag, 10. 07. 2001, 16:00
Ort: Hörsaal V 9.31 Pfaffenwaldring 9,
Many authors have noted the difficulty of developing the models
required for nonlinear model predictive control (NMPC) and other
model-based computer control strategies. Part of this difficulty
lies in the extreme range of behavioral characteristics that are
possible in the enormously broad class of nonlinear dynamic models.
In particular, it is difficult to select nonlinear model structures
that exhibit desirable qualitative behavior or exclude undesirable
behavior because connections between nonlinear model structure and
nonlinear behavior are not well understood. This difficulty motivates
detailed examinations of specific nonlinear model structure classes.
This talk introduces the class of structured Lipschitz models, a
relatively rich class of nonlinear discrete-time dynamic models
that includes as special cases all nonlinear FIR models, both
recurrent and nonrecurrent dynamic artificial neural networks,
the Lur'e model class, and a variety of other less well-known
model structures like the EXPAR and TARMAX classes. Further, the
structure of this model class lends itself nicely to stability
analysis and gives some interesting insights into the differences
between BIBO stability and stronger stability notions (e.g.,
exponential stability) in these model classes.
Dr. Pearson received his PhD in electrical engineering from M.I.T.
in 1982, working in the area of optimal fixed-structure compensators
for distributed parameter systems. Upon completing his degree, he
joined the DuPont Company in Wilmington, Delaware in the U.S.A. where
he remained until 1997, working in a variety of areas including the
development of on-line process measurement sensors, analysis of
process operating data, and various practical aspects of dynamic
model development (in particular, data pretreatment procedures and
nonlinear model structure selection). In 1997, Dr. Pearson joined
the Institut fuer Automatik at ETH Zuerich, where he taught courses
on exploratory data analysis and nonlinear dynamic model development
and completed two books: "Discrete-Time Dynamic Models," published by
Oxford University Press in 1999 and "Identification and Control
Using Volterra Models," co-authored with F.J. Doyle, III and
B.A. Ogunnaike, to be published this summer by Springer-Verlag.