Statistical learning theory and randomized algorithms for control
Vidyasagar, M.
Centre for Artificial Intelligence & Robotics, Bangalore;
This paper appears in: Control Systems Magazine, IEEE
Publication Date: Dec 1998
Volume: 18,
Issue: 6
On page(s): 69-85
ISSN: 0272-1708
References Cited: 37
CODEN: ISMAD7
INSPEC Accession Number: 6129785
Digital Object Identifier: 10.1109/37.736014
Current Version Published: 2002-08-06
Abstract
The topic of the present article is the use of randomized
algorithms to solve some problems in control system designs that are
perceived to be “difficult”. A brief introduction is given
to the notions of computational complexity that are pertinent to the
present discussion, and then some problems in control system analysis
and synthesis that are difficult in a complexity-theoretic sense are
described. Some of the elements of statistical learning theory, which
forms the basis of the randomized approach, are briefly described.
Finally, these two sets of ideas are brought together to show that it is
possible to construct efficient randomized algorithms for each of the
difficult problems discussed by using the ideas of statistical learning
theory. A real-life design example of synthesizing a first-order
controller for the longitudinal stabilization of an unstable fighter
aircraft is then presented to show that the randomized approach can be
quite successful in tackling a practical problem
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