Computing That Serves

Robust and Learning Control for Complex Systems


Thursday, September 13, 2007 - 11:00am


Peter Young, PhD, Colorado State University

It is well known that real physical systems cannot be exactly described
by mathematical models, though such models are a prerequisite for many
controller analysis and design techniques.  Thus one has to deal with
the issue of "uncertainty" in mathematical models.  Robust control theory
deals with this issue by providing controllers which are robust to the
uncertainty, i.e., they work for all allowed values of the unknowns. 
This provides rigorous stability and performance guarantees but necessarily
sacrifices performance.

The approach taken in learning control is to try to "learn" these
uncertainties on-line in real-time, and hence converge to a controller
that is specifically tuned to the dynamics of this particular plant.
This has the potential to deliver "optimized" performance, but the problem
arises as to how to get there.  One typically has no a-priori guarantees
about the performance or even stability of the learning controller.  This
is clearly not acceptable in a practical (non-simulation) environment.

In this talk we will discuss a technique for the development of robust
learning controllers.  These attempt to deliver the best of both worlds,
by using each approach to deal with the shortcomings of the other.  Our
application area for this work is HVAC systems.  These are systems which
are complex, time-varying, nonlinear, with poorly understood, but slow,
dynamics.  This makes them an ideal candidate for our approach, and we
will discuss our results to date with an experimental testbed for HVAC
control that we have developed.


Dr. Peter M. Young received his Ph.D. in Electrical Engineering from California
Institute of Technology in 1993, and worked for two years as a Postdoctoral
Associate at Massachusetts Institute of Technology, before joining the faculty
of Colorado State University in 1995. He has worked extensively on the development
of advanced analysis and design techniques for large-scale uncertain MIMO systems,
subject to both multiple uncertain parameters, and multiple dynamic uncertainties. 
This work provided  a breakthrough in this area, where previous tools could only
handle small problems because of the computational burden, and Dr. Young developed
computational software packages which were released commercially as part of the
MATLAB Robust Controls toolbox.  

Currently Dr. Young is an Associate Professor at Colorado State University. His
recent research interests include the development of analysis and design techniques
for robust learning controllers, capable of adaptation whilst maintaining guaranteed
robust stability.  He has carried out this theoretical work in parallel with efforts
in a number of specific application areas.  These include structural vibration suppression
and disk drive servo control, control of HVAC and energy storage systems, power system
distribution grids and sustainable energy, and control of biological systems - specifically
algae growth for biodiesel production.