Parameter set estimation (PSE), a class of system identification schem
es which aims at characterizing the uncertainty in the identification
experiment, is philosophically different from traditional parameter es
timation schemes which seek to identify a single point (model) in the
parameter space. The literature has seen a good deal of attention paid
to PSE techniques in recent years, primarily because it is projected
that they will play a vital role in robust identification for control.
An important step in current research along these lines is developmen
t of PSE algorithms for systems which are time varying in nature; this
is particularly true if the identified model set is to be used in an
adaptive setting, such as for gain scheduling or autotuning. In this p
aper, we extend an ellipsoid algorithm for PSE of time-invariant syste
ms to time-varying systems. We show how knowledge of dependences in th
e parameter variations can be exploited to reduce the number of comput
ations in the resulting algorithm. Finally, scalar bound inflation, a
second strategy for PSE of time-varying systems, is optimized for volu
me, and a comparison of the two algorithms is made.