WINTER TERM 1999

Thursday, March 18

4:30 - 5:30 PM

Room 1001 EECS

Abstract-

Friction is a highly nonlinear process that depends on many
physical parameters. In this research, we focus on
identifying the
mechanism of the friction process of lubricated lip seal,
sliding
against steel shaft at low speed, where "low speed" means a
sliding
speed where the lubricant film is on the order of surface
roughness
dimensions or less.

Lubricated sliding lip seals are important
components in many
hydraulic devices. The requirements imposed by today's high
precision
machines motivate the precise simulation of friction between
these
seals and sliding components. The objective of this
research is to
develop models which successfully simulate the friction
process with
the velocity data of the sliding shaft, and the lip seal
friction data
as the input and output signals, respectively.

The complexity of friction process makes it very
difficult
to develop a physically based models. This motivates us to
use an
empirical system identification techniques. In this
research, we take
a macroscopic point of view, and instead of incorporating
(microscopic)
physical parameters into the model, we assume the system
consists of
nonlinear and linear components whose characteristics can be
described
by nonlinear functions or scalars. There are many
advantages of this
approach as follow. The modeling process can be much
simpler than
that based on physics, and often gives a model with fewer
parameters.
To some extent, the macroscopic parameters could give more
intuitive
interpretation of the friction process than the physically
based models.

In this research, we present two different approaches of
nonlinear
system identification. As our first approach, we develop
Hammerstein
type models. Because of the non-stationary nature of
friction process
and time invariant assumption of the model, an adaptive
algorithm
cannot be used for the estimation of the model parameters.
We use
least squares gradient search algorithms to estimate the
models
parameters. As our second approach, we present state space
model.
In this model, we estimate some internal signals which are
not
accessible for measurement. For the estimation of these
internal
signals, Extended Kalman filter is used. Since both the
internal
signals and the model parameters are unknown, they must be
estimated
at the same time. The method used for the simultaneous
estimation is
called an "Extended Least Squares". A recursive Extended
Least Squares
algorithm for our state space model is presented.

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