Practical Methods for Real World Control
Systems
A halfday
workshop, preceding CCTA 2020.
Sunday, August
23, 2020, 1pm to 5:30pm, Eastern Daylight Time (EDT, GMT4)
CCTA 2020
the workshop are online through the CCTA 2020 Website.
15 Minute Invite/Intro Video About
Workshop
Printable version of this flyer
Workshop registration at CCTA 2020 (PaperPlaza) (reduced to $50 USD for online)
Presenter: Daniel Abramovitch
Rationale: The
proverbial “gap” between control theory and practice has been discussed since
the 1960s, but it shows no signs of being any smaller today than it was back
then. Despite this, the growing ubiquity
of powerful and inexpensive computation platforms, of sensors, actuators and
small devices, the “Internet of Things”, of automated vehicles and quadcopter
drones, means that there is an exploding application of control in the
world. Any material that allows controls
researchers to more readily apply their work and/or allows practitioners to
improve their devices through best practices consistent with well understood
theory, should be a good contribution to both the controls community and the
users of control. This workshop is
intended as a small but useful step in that direction.
Prerequisite skills (of participants): Undergraduate
level knowledge of feedback systems, sampled data systems, and
programming. An honest interest in being
able to translate control theory into physical control systems.
Intended Audience:
We believe that
this workshop will be of great interest to three types of audience members:
1)
Academic
researchers who are well versed in control theory but would like to learn more
about issues practicing control engineers often encounter as well as techniques
and methods often used outside of standard textbook solutions to enhance their
students’ experience in the classroom and laboratory.
2)
Practicing
engineers who work on physical control systems and products that use control
with an interest in connecting their work to “best practices” motivated by
theory.
3)
Students
who may be interested in adding laboratory experiments to their research or
want to know how to make what they have learned applicable in industry.
For each of
these groups – and those that are somewhere in the intersection of them – this
workshop will address the gap from both sides, so as to give the participant a
more complete understanding of how it applies to their particular situation.
Topic overview:
The general
style for each topic will be to present the issue, discuss rational ways of
thinking about a solution, and where possible, show a demo to illustrate the
idea.
·
Overview, a.k.a. “Mind the Gap.” (1:00 pm [20 min])
We will
frame the workshop by taking a walk around a “practical” control loop, pausing
to consider each element in a firstpass to set up the rest of the workshop.
·
System Models and Characterizing Them with Measurements, or why
it’s both important and annoying to be discrete. (1:20 pm [40 min])
Beginning
with simple models, we will look at discretization and identification, exploring
what a step response can tell us and when frequency methods are needed.
·
Simple Controllers for Simple Models, or why so many controllers
are PIDs, and why some are not. (2 pm [60 min])
The
choice of PID control is intimately related to what we can measure and model. We
will explore this connection and look at how to tune your controllers and how
to tweak them to get the most from their simple form.
·
Coffee/Bio Break (3 pm [15 min])
· Practical
Loop Design, Or Why Most Open Loops Should Be an Integrator, and How to Get There (3:15 pm [30 min])
Here we will dive
into loop shaping, including straightforward steps for loop shaping on a well
instrumented system. We also introduce Bode’s
Integral Theorem and Stein’s Dirt Digging to understand the effects of loop
shaping on closedloop sensitivity. We
will also mention some of the implementation details that often limit this.
·
Filtering and Equalization for Mechatronic Systems and How to
Actually Make State Space Useful for Them (3:45 pm [60 min])
If
everything could be handled with a PID, the world would be a much stiffer
place. In order to get to that ideal
open loop, we need to filter in an intelligent way. Along the path, someone may mention state
space. We will try to understand why
this is so rare in mechatronic systems and introduce a structure that may
change that.
·
Coffee/Bio Break (4:45 pm [15 min])
· Integrating
in Feedforward Control (5:00 pm [30 min])
Feedforward control can make a controller better, if
it’s done right. In this section we’ll explore the basic structures and uses of
feedforward, and how to integrate feedforward into your setup.
Workshop outline,
topic details, and tentative schedule:
We expect that
there will be more written material for the workshop than can be presented in a
half day. Any one of these topic areas
could fill up half a day. However, these
are the areas we hope to illustrate in the time we have.
This will frame
the style and topics of the workshop. Control
engineers will all recognize the block diagram above, but in going from that to
implementation, we need to consider a much richer block diagram, a version of
which is shown below.
If we take a “walk
around the loop” in this diagram, we discover a large set of pieces that need
to be “gotten right” in order for the physical system to be properly
controlled. Often, these blocks have not
been discussed since the second week of the first undergraduate controls
class. Specifically, we will first
visit:
· The walk around the loop
with a pure feedback loop, and with a feedback/feedforward loop. How and when do we choose to add in
feedforward components? When can we not
do this? This ties into basic questions
about the physical problem, what information is available, and what can be done
with that information.
· Physics and modeling, and
modeling that we can use. How do we get
from first principles (science) to models that help us do better control
designs? What parts of our models can we
verify from actual measurements? How do
we work knowledge of current working loops into modeling for improved
performance of the same system?
· Realizing that most modern
implementations are on digital computers, how do we reconcile thinking in
analog while implementing in digital?
When is “sampling fast” sufficient and what insights and improvements
can we gain from paying attention to how we discretize things?
· Time constants, physical systems, and
the controls that they push. Process
control, motion control, PLL (phase control and synchronization), mechatronic
systems and other things that vibrate.
This
introduction will introduce, frame, and motivate what we will do in the rest of
the session, so while the topics are deep, they are intended as a “first pass”
for the topics that will be more deeply discussed in the remaining sessions.
While advanced
research often starts with some complex model, most practical control systems
are based on an explicit or more often an implicit simple, low order,
model. In this segment, we will start by
calling out these basic implicit models, discussing the systems that motivate
and demonstrate them, and discuss what measurements can be made on such models.
We must accept
that all of our measurements will be made in discrete time, and so our derived
models must deal with the effects of sampling.
At the same time, we will show simple examples of why conventional
discretetime models can obscure the physical intuition of the original system
so as to make tuning to physical parameter changes next to impossible for many
systems. A few simple examples will make
this obvious and motivate the rest of the measurement and modeling discussion.
Specific topics will include the following.
· The assumptions and limitations of timedomain
identification on discretetime models.
· First and second order
system models, and where to find them.


· Step response methods. Using the simple models above, what can we
hope to gain from a step response method?
How do we implement them in our control software so as to not be
embarrassed by the better results from the digital oscilloscope? What are the limitations of step response
methods? When is a step response
measurement the only game in town?
· Frequency response
methods. When are they better than step
response? When are they necessary in
model characterization? When can you actually do such a measurement? What are the tradeoffs between FFT frequency
response methods and stepped sine (known as sweptsine in industry)?
·
Curve
fitting for frequency responses
·
Effects
of delays (NMP from Padé) and what it means for design
While PID
controllers are the “Brand X” of most control Ph.D. candidates’ theses and
spent the 1990s being derided by the denizens of fuzzy control, they remain
today the most ubiquitous example of feedback controller design, by some
measures accounting for 97% of all controllers in the field. Rather than dismissing this as an alternative
and boring reality, we will examine the underlying implicit assumptions about
modeling the physical system – and how those models derive from what can be
measured (from the previous segment), to motivate the generic and fundamental
utility of PID controllers. With that
context, we will show in the first half of this segment:
· A unified framework for discussing PID
controllers, which is helpful not only in generating a design, but also in
understanding the underlying structures of offtheshelf, commercial PID
controllers. How do PID controllers relate to lead/lag controllers?
· A discussion for representing PID
controllers in discrete time without losing the intuition of the continuous
time framework.
· How PID controllers can be
expected to behave in closedloop for various low order models.
· Tuning PID controllers: from step response and from frequency
response.
· Why using ‘D’ in PID often fails to
improve performance and how to fix that. Where is ‘D’ most often beneficial?
· Things to consider with the
integrator depending on control goals, including thoughts on integrator
antiwindup.
4.
Practical Loop Design, Or Why Most Open Loops Should Be a Constant or an
Integrator, and How to Get There (30 minutes)
This section
builds on the prior ones with a practical view of what is commonly called loop
shaping. The idea is to talk about how
loop shaping affects practical stability margins (i.e. gain and phase margins)
and how things that affect those margins affect the behavior of the closedloop
system. With this context, we can
discuss:
·
Effects
of dynamics, and how we can handle these with our filters as equalizers.
·
Desired
open loop shapes (integrator) and closedloop shapes (smooth low pass filter)
and how they are related.
·
Bringing
it all together with Bode's Integral Theorem and Stein's Dirt Digging
In the movie
trailer, this section could be labeled, “when simple models go bad”. Specifically, we will discuss system models
with higher order dynamics, and what this means for control design. In many frameworks, the first resonant mode
signifies the frequency at which all control effort should stop. The commonly
used PI controllers generally stop at ¼ the first resonant frequency. For other systems, such as chemical process
control, the performance limiting negative phase is dominated by delays in the
system. Getting beyond these limitations
involve:
· Having a requirement to control faster.
· Having a reliable model of the higher
frequency dynamics from measurements on the system itself. We will discuss ways
to make these measurements more automated, more built in to the controller,
thereby minimizing the per measurement costs.
·
Having
a design methodology for compensating for those dynamics. Filters as equalizers,
and why mechatronic systems usually use biquad cascades.
· Filters in control loops: do’s and don’ts
· Illustration
using simulation model of a motion control system that has a resonant peak.
Show how notch filter + PID controller accomplishes much faster response than
PID alone.
With this framework, we will move to the “third
rail” of mechatronic control of high Q systems.
We will discuss what makes this so hard and discuss how the Biquad State
Space structure allows us to move beyond the typical limitations. We will also show how the BSS restores some
of the original promise of state space methods, allowing us to have digital
models that more closely mirror our physical models, which in turn allows us to
compare models with lab measurements – to put virtual and physical scope probes
in corresponding parts of the model and physical system.
6.
Integrating in Feedforward Control (30 minutes)
This section will discuss practical application of
feedforward control to a feedback loop.
In large part, feedforward can remove a lot of the potential error from
the control loop, unburdening the feedback control system. But in some situations,
it can introduce error. There are two
basic forms of FF: Plant Injection (PI) and Closed Loop Injection (CLI).
· When
can we use feedforward? When is it a good idea?
What is the benefit?
· How
should the feedback loop be designed for feedforward (idea: integrator OL >
LPF closedloop > multilead feedforward).
What about PI form? How to
choose.
· What
do each of FF choices (PI and CLI) imply for feedback controller design?
· Repetitive
control and adaptive feedforward cancellers
· Feedforward
control from auxiliary sensors
Presenter’s short bio:
·
Dr. Daniel Abramovitch (Agilent
Technologies)
Danny Abramovitch earned degrees in
Electrical Engineering from Clemson (BS) and Stanford (MS and Ph.D.), doing his
doctoral work under the direction of Gene Franklin. Upon graduation, and after
a brief stay at Ford Aerospace, he accepted a job at HewlettPackard Labs,
working on control issues for optical and magnetic disk drives for 11 1/2
years. He moved to Agilent Laboratories
shortly after the spinoff from HewlettPackard, where he has spent 19 years
working on test and measurement systems.
He is currently in Agilent’s Mass Spectrometry Division working on
improved realtime computational architectures for Agilent’s mass
spectrometers, including the new Ultivo Tandem Quad product.
Danny is a Senior Member of the IEEE and
was Vice Chair for Industry and Applications for the 2004 American Control
Conference (ACC) in Boston. He was Vice
Chair for Workshops at the 2006 ACC in Minneapolis, for Special Sessions at the
2007 ACC in New York, and for Industry and Applications for the 2009 ACC in St.
Louis. He was Program Chair for the 2013 ACC and was General Chair of the
recent 2016 ACC in Boston. He has helped organize conference tutorial sessions
on topics as varied as disk drives, atomic force microscopes, phaselocked
loops, laser interferometry, and how business models and mechanics affect
control design. He served as the Chair of the IEEE CSS History Committee from
2001 to 2010. Danny is credited with the
original idea for the clocking mechanism behind the DVD+RW optical disk format
and is coinventor on the fundamental patent.
He was on the team that prototyped Agilent’s first 40Gbps Bit Error Rate
Tester (BERT) and was able to cite a Douglas Adams book in one of his patents
relating to that device. Along with his
coauthor, Gene Franklin, he was awarded the 2003 IEEE Control Systems Magazine
Outstanding Paper Award. His favorite paper remains the one prompted by a
question from his then 3yearold son, which showed that the outrigger was a
feedback mechanism that predated the water clock by at least a 1000 years. He
was a Keynote Lecturer at the 2015 MultiConference on Decision and Control in
Sydney, Australia and a plenary speaker at the 2020 Conference on Control
Technology and Applications (this conference).
His recent work for Agilent was on future atomic force microscopes and
high precision interferometers. His
current work involves improving the realtime data collection and signal
processing on Agilent’s Mass Spectrometers, and is part of the team that
created Agilent’s multiaward winning Ultivo Tandem Quad LC Mass Spectrometer. He
is the holder of over 20 patents and has published nearly 50 reviewed technical
papers.