Top Ten Research Problems in Nonlinear Control
June 1995
Here is my personal list of the biggest research problems in nonlinear
control theory (including some relevant links, where appropriate). If
you don't agree with these (which is likely), feel free to send me e-mail. This is more or
less a way for me to think online, so I wouldn't take any of this too
seriously.
10.
Integrating algorithmic control with dynamical control
Modern controllers are implemented on computers and often consist of a lot
of logic surrounding a core of feedback control algorithms. Figuring out
how to integrate the logic with the controllers and how to design
controllers which are compatible with higher level algorithms is basically
an unsolved problem. We aren't doing a lot of work on this problem in my
group right now, mainly because it hasn't yet come up in any of the problems
we are working on (but it will...).
9.
Writing software for implementing theory
In this day and age, the only way anyone is going to use your personal
technique for synthesizing controllers is if you write software to implement
it. There is a strong need for a software protocol for nonlinear control
which allows easy integration of modules from a variety of sources. Our
initial work in this area has so-far been limited to
Sparrow,
RobotLinks, and
EDSpack.
A lot more needs to be done.
8.
Building representative experiments for evaluating controllers
One of the hardest parts about doing controls research in a university is
figuring out how to validate your results on an experiments that are
representative of real engineering systems while at the same time being
simple enough to be built, maintained, and used by faculty and graduate
students (as opposed to a full-time, technical support staff). Two
experiments that we have built at Caltech that I am reasonable happy with
are the ducted fan and a
low-speed compressor system.
Other examples are the the manufacturing experiments at University of
Michigan and the PATH program at UC Berkeley.
7.
Recognizing the difference between regulation and tracking
For linear control systems, regulation and tracking are essentially
identical. For nonlinear systems, and particularly motion control systems, the
problem of tracking is significantly different and considerably harder. The
role of trajectory
generation is very important in nonlinear problems and is the motivation
for much of our work in
differential flatness,
nonholonomic motion planning,
and
mechanical systems with
symmetries.
6.
Quantifying relative performance vs model complexity for nonlinear methods
Everyone seems pretty sure that in order for nonlinear control to give
good performance, you need to have accurate models. Of course, this is
also true for linear systems (good ol' Bode integrals...), but somehow
it is even more critical for nonlinear systems (or so they say). We
need to do meaningful (experimental?) comparisons between different linear
and nonlinear control methodologies to try to determine how model
complexity affects performance. In particular, we need to find out
more about how much modeling is needed before nonlinear control
techniques can outperform linear ones (including gain scheduling, which
is pretty hard to beat).
Some of the dynamic inversion techniques that Honeywell is promoting
(and possibly testing) should provide some good insights in this regard.
5.
Integrating good linear techniques into nonlinear methodologies
People who work in nonlinear control need to figure out how to make use of
all of the latest advances in linear control techniques when they apply.
The fact is that for a lot of control problems, the dynamic, error
correction (feedback) portion of the controller can be made linear. And in
that case, you may as well use a good linear controller with gauranteed
robustness and performance rather than just using static, linear or
nonlinear feedback (like pole placement). This is what we are trying to do
on the ducted fan and is the basic idea underlying two degree of freedom design
4.
Recognizing the difference between performance and operability
One of the things that nonlinear control can do is increase the range
over which a system can run without catastrophic failure. This is
different than providing good performance and is a particularly hard
problem because you have to know about the global behavior of the
system in order to define something like operability. An example that
has motivated me is active control rotating stall and surge in
compression systems, where the main issue is to keep the system from
getting stuck in deep stall in the presence of disturbances. Good
performance is only required in normal operating conditions, so the
real issue is dealing with system nonlinearities that appear when
operating near the (uncontrolled) stability limits of the system.
3.
Finding nonlinear normal forms for control
Most of the research in nonlinear control to date has concentrated on
extending linear methodologies to nonlinear problems. In essense, we
convert or approximate nonlinear systems by linear ones and then
applying traditional ideas. It is often very expensive (in terms of
control energy) to convert a nonlinear system to a linear one and
linear approximations are becoming increasingly inaccurate as we push
the envelope of controller performance. Even more nonlinear approaches
like backstepping really only apply to problems that are absolutely equivalent to
linear systems.
I envision a time when there is a big (online) catalog of nonlinear normal
forms for control, with software for determining how close a given system is
to each normal form listed, and methods and techniques for control of that
normal form. All of this in some consistent format, so that an engineer can
get a first cut design by combining existing results to attack their
problems (kind of like a set operating system interface functions or a
programming library in the world of computers).
2.
Exploiting special structure to synthesize controllers
You can't build a theory for nonlinear control that works for
everything. Nonlinear systems are a lot more complicated than
that. Concentrating on special classes of systems, like mechanical systems and propulsion systems, is the
most likely way make significant progress in synthesizing nonlinear
controllers.
1.
Convincing industry to invest in new nonlinear methodologies
The biggest research problem in nonlinear control is figuring out how
to get people to use it. To many of the potential users of our
research, much of the theoretical work in nonlinear control is just
that: theory. In order to make control theory useful, we need to spend
more energy convincing industry that they should take that theory and
spend the time and money necessary to develop it.
Richard Murray (murray@indra.caltech.edu)
Last modified: Fri Jan 12 09:45:25 1996