Annual Reviews in Control 55 (2023) 1–17
Available online 17 March 2023
1367-5788/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (
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).
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Annual Reviews in Control
journal homepage:
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Vision article
Control education for societal-scale challenges: A community roadmap
John Anthony Rossiter
a
,
∗
, Christos G. Cassandras
b
, João Hespanha
c
, Sebastian Dormido
d
,
Luis de la Torre
d
, Gireeja Ranade
e
, Antonio Visioli
f
, John Hedengren
g
, Richard M. Murray
h
,
Panos Antsaklis
i
, Francoise Lamnabhi-Lagarrigue
j
, Thomas Parisini
k
,
l
a
Dept. ACSE, University of Sheffield, UK
b
Boston University, United States of America
c
University of California, United States of America
d
Department Informática y Automática, UNED, Spain
e
University of California, Berkeley, United States of America
f
University of Brescia, Italy
g
Brigham Young University, United States of America
h
California Institute of Technology, United States of America
i
University of Notre Dame, United States of America
j
CNRS, University of Paris-Saclay, France
k
Imperial College London, UK
l
University of Trieste, Italy
A R T I C L E I N F O
Keywords:
Control education
Outreach
Industry
Curriculum
A B S T R A C T
This article focuses on extending, disseminating and interpreting the findings of an IEEE Control Systems
Society working group looking at the role of control theory and engineering in solving some of the many
current and future societal challenges. The findings are interpreted in a manner designed to give focus and
direction to both future education and research work in the general control theory and engineering arena,
interpreted in the broadest sense. The paper is intended to promote discussion in the community and also
provide a useful starting point for colleagues wishing to re-imagine the design and delivery of control-related
topics in our education systems, especially at the tertiary level and beyond.
1. Presentstateandfutureoutlook
1.1. Backgroundandcontext
Most researchers will routinely be asking themselves lots of ques-
tions and the most significant of these will be: what are the important
problems in society today and can my work make a positive difference
to tackling those? The way we ask questions is also very much influ-
enced by our expertise, employer and personal opportunities; these set
a context from which we contribute. Consequently this paper begins
from a premise that the authors and readers work predominantly in the
control theory and engineering arena, where that topic is interpreted in
a broad sense to include multiple themes such as: modeling, classical
and modern feedback, industrial applications, biological and health
applications, aerospace applications, data handling and data security,
fault diagnosis and detection, and clearly much much more.
∗
Corresponding author.
E-mail addresses:
j.a.rossiter@sheffield.ac.uk
(J.A. Rossiter),
cgc@bu.edu
(C.G. Cassandras),
hespanha@ece.ucsb.edu
(J. Hespanha),
sdormido@dia.uned.es
(S. Dormido),
ldelatorre@dia.uned.es
(L. de la Torre),
ranade@eecs.berkeley.edu
(G. Ranade),
antonio.visioli@unibs.it
(A. Visioli),
john_hedengren@byu.edu
(J. Hedengren),
murray@cds.caltech.edu
(R.M. Murray),
pantsakl@nd.edu
(P. Antsaklis),
email.francoise.lamnabhi-lagarrigue@centralesupelec.fr
(F. Lamnabhi-Lagarrigue),
t.parisini@imperial.ac.uk
(T. Parisini).
The IEEE Control Systems Society (CSS) has set as one of its goals
to support the wider community in answering such questions so that
we can direct our research and educational efforts more wisely to help
tackle societal-scale challenges. Towards this goal, the CSS set up a
working group to provide and disseminate a report. One purpose of this
paper is to ensure the findings of this roadmap report (
Annaswamy,
Johansson, Pappas, et al.
, 2023
) can be disseminated effectively to a
global audience. Moreover, this paper also aims to extend and interpret
those findings to deliver a more holistic message, which the authors
hope will be useful to all readers.
1.2. Scopeofproject
Many of us will at some point have been actively involved in
the delivery of an introductory control theory course while others
https://doi.org/10.1016/j.arcontrol.2023.03.007
Received 9 September 2022; Received in revised form 26 January 2023; Accepted 13 March 2023
Annual Reviews in Control 55 (2023) 1–17
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J.A. Rossiter et al.
may simply recognize the role and importance of such courses. An
introductory course is focused on getting students to understand why
the topic is so important. In simple terms, for numerous aspects of life,
from controlling speeds of motors, temperatures in tanks, growth rates
of plants, drug concentrations in patients and many more, the behavior
of a system (equivalently its output/states) is often critically important.
In control theory and engineering, we seek both to understand where
behaviors come from and how we might influence them for the better.
Significantly, what has changed in recent years is the scope of con-
trol (Antsaklis et al., 1999; Lamnabhi-Lagarrigue et al., 2017; Murray,
2003). Students from an older generation may have viewed feedback
in a rather narrow sense considering largely traditional manufacturing
contexts, PID loops and some exposure to frequency response methods.
More significantly, a control theory course would likely have been
much more focused on a mathematical formalism. However, it has be-
come clear in recent years that feedback loops have far more extensive
presence and impact and that we need to broaden our horizons to
consider areas of significant societal importance such as: (i) climate
change; (ii) efficient and sustainable agriculture/farming; (iii) space ex-
ploration; (iv) transportation; (v) underwater vehicles; (vi) biomedical
science; (vii) economics and finance; (viii) pedagogy and, of course,
this list could easily be extended far more.
One might call this a ‘‘change in awareness’’ but it calls for a
significant change in many respects including a proper discussion on
curriculum design and delivery (Rossiter, Zakova, Huba, Serbezov, &
Visioli, 2020), as well as the focus of academic research. One core aim
of this paper is to give academics the evidence and confidence to argue
for change in their own institutions and research funding bodies. We
all need five-year plans for the control curriculum, but perhaps now is
the time to think even longer term and consider more drastic changes;
how do we develop students and researchers who will have the skills
and awareness to make a difference in the 21st century?
1.3. Paperorganization
The paper is organized into separate themes for clarity.
1. Section 2 focuses on mostly earlier work in the education area.
What skills will control engineering graduates need and what are
the repercussions on curriculum design and delivery? This sec-
tion largely forms the background for the subsequent sections.
2. Section 3 includes the major contribution and talking points of
this paper. It builds on the previous section to anticipate the
skills and projects that students will be working on in the future
and how we might prepare for this and indeed motivate students
to engage with it? This section summarizes the key findings
from the CSS roadmap report (Annaswamy et al., 2023) and
readers will be particularly interested in the concise summary of
Section 3.6 which gives a clear handle on things they can use.
3. Section 4 is also a major contribution in that it gives in substan-
tial detail some case studies from institutions across the globe
and evaluation of good practice that readers can use as templates
for adoption in their own institutions.
For reasons of space and clarity of messaging, the authors have
decided not to discuss the critically important aspects of outreach and
the changing needs of and interaction with industry. It was felt that
these topics would be better served in separate articles.
2. Controleducationinthe21stcentury
This section focuses on tertiary education and open-access learning
resources. Reflecting on the needs of 21st century society, what palette
of undergraduate and postgraduate courses should we be offering stu-
dents? For simplicity and callibration, we take a short course to be
something like a 20 lecture-hour block with associated assessment.
We have to take into account that modern engineers are asked
to face more and more complex systems, which are often both inter-
connected and continuously changing. Thus, engineering education in
general should provide a solid theoretical background but also those
soft skills (e.g. problem solving, team working, adaptivity, learning
capabilities, communication, etc.) that are essential to rise to the chal-
lenge. In this context, control can play an important role because in
addition to being based on rigorous mathematical concepts, it also
naturally takes a multidisciplinary perspective and has a wide variety
of applications. Teaching modalities in control courses should therefore
take into account these aspects and exploit new technologies for this
purpose. This can be accomplished, for example, by suitably mixing
face-to-face and online lectures, by assigning problems to be solved
with take-home laboratory kits, by devising projects to be developed
in teams, and more.
It is noted that this section focuses primarily on undergraduate
provision, but as indicated in Section 4.5, many of the generic findings
implicitly carry forward into postgraduate and further training and how
we as a community support that.
2.1. Recentcommunitysurveys
The technical committees (IEEE, 2022; IFAC, 2022) on control
education for both IFAC and the IEEE CSS recently carried out a
global survey on what would constitute the ideal first course in control
(see Rossiter, Hedengren, & Serbezov, 2021; Rossiter et al., 2020 where
all the details can be found). Almost 500 answers have been collected
from instructors from 47 countries around the word (China, Brasil, USA
and Italy have been the most relevant contributors). One could argue
that the mindset behind this survey was fairly traditional by being fo-
cused primarily on fundamental concepts and associated mathematical
tools. Nevertheless, the most important conclusion was slightly contro-
versial in that it argued for a reduction in the emphasis on detailed
mathematics and proofs and, instead, more stress on conceptual issues
such as: why is control important?
As such, that survey provides a useful foundation for the discus-
sions in this paper which develop that argument further. Indeed, we
also want to expand the question to say: how would you design the
curriculum for a second, third and more courses in control and what
learning would you emphasise and why? It is evident in the following
that the vision being presented here cannot possibly be achieved in a
single course and thus the prime findings of the original survey likely
still hold. However, in order to encourage students to focus more on
control in their later studies, what should we be doing?
In summary, it appears that the main topics related to an intro-
ductory course (that is, the main concepts related to system analysis
and feedback control) are fully covered in the survey, although, of
course, there is always the need to provide solutions that can be easily
customized by the instructor according to their requirements.
2.2. Anidealfirstcourseincontrol(Rossiteretal.,2020)
As this is already published, we keep this summary very brief. A
first course (Rossiter et al., 2021) should focus primarily on concepts,
getting students to understand the critical role of modeling, feedback
and behaviors in the world, using examples from a wide range of
scenarios. While some mathematical content and rigour are important,
these should not be over-emphasised at the expense of enthusing stu-
dents. Software packages and laboratories could be used extensively
(e.g., see the case studies in Section 4) to support the learning and re-
duce the requirement for tedious pen and paper computations. Detailed
mathematical developments for topics such as frequency response,
state-space models and analysis,
푧
-transforms, signal processing and
so forth, which are essential in the preparation of a control engineer,
should be provided in a later (or larger) course.
Annual Reviews in Control 55 (2023) 1–17
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J.A. Rossiter et al.
To finish, it should also be noted that the first course would be
early in the curriculum, which is enabled by slightly reducing the
mathematical requirements, compared to a traditional mathematically
heavy course. This increases the potential impact on the whole student
body. This description is somewhat vague, but exemplars of what could
be included in such a course are given in Section 4 alongside some
aspects of how these broader aims might be delivered.
2.3. Broadeningourviewofwhatcontrolengineeringinvolves
One hope of this paper is to give greater visibility to views on the
content of a first course in control theory and engineering. It is evident
that modern engineers need to be more versatile and adaptable than in
previous generations (Murray, 2003), and this extends to an awareness
of how core insights and understanding may apply across a diverse
range of disciplines.
As described in Murray (2003): ‘‘[a]t its core, control is an
in-
formation
, and includes the information in both analog and digital
representations. Increasingly, control is carried out in a digital fashion,
and the advances in computation and networking, combined with the
role of data-driven techniques, is fundamentally changing what control
engineering involves’’.
Traditionally, control has been taught in aerospace, chemical, elec-
trical, and mechanical engineering departments, often in a manner
tuned to those disciplines. However, there is increasingly an audience
for control insights and tools that goes beyond these disciplines. Per-
haps chief among these is computer science, which is increasingly a
key home to students who go on to work with a variety of feedback
systems (e.g., robotics and autonomous systems, large scale information
systems and networks, on-demand services). Computer science students
typically have a much different mathematical background (focused on
discrete, rather than continuous, mathematics) and an appreciation for
the role that software plays in the modern world. Teaching control
theory to this audience is likely to be different than teaching it to a
mechanical (or electrical or chemical) engineer.
Other domains in which ideas from dynamics, modeling, feedback,
and uncertainty are important include biology, ecology, economics,
mathematical statistics, and physics. Biology is a particularly interest-
ing case from an educational perspective, since the level of mathematics
required in most biology curricula is less than that of a traditional
engineering program. But the concepts and insights of feedback and
control are incredibly relevant in modern biological systems, and we
must seek ways to reach out to this audience if we wish to provide the
tools that biological scientists and engineers need to be successful.
Related to the question of what audiences are trying to reach is what
set of insights and examples we should use for any given audience. As
articulated elsewhere in this article, control engineering is now done at
multiple levels of abstraction and across multiple disciplinary domains.
The core ideas of dynamics, uncertainty, feedforward, and feedback
apply almost universally, but the details can be very different if one
is implementing a scheduling system for on-demand transportation
systems versus an autonomous vehicle versus a modern turbomachine.
Whenever possible, one should seek to at least demonstrate key con-
cepts in a way that transcends the specific domain of a given example,
allowing students to take away the key ideas of using feedback as a tool
to manage uncertainty and to design the (closed loop) dynamics of a
system, along with some of the fundamental limits and tradeoffs that
cut across all layers of abstraction and application domains.
A key question in both reaching out to new audiences and describing
control in a broad way is whether to try to teach a single course
that is accessible to all students (as was advocated in Murray, Waydo,
Cremean, & Mabuchi, 2004) or to develop specialized courses that
are tuned for a given audience/approach but still provide a broad
perspective that demonstrates the utility of control concepts across
multiple domains. To a large extent, the approach will likely depend
on the organizational structure institution, the background of the stu-
dents, and the desired size and breadth of classes. But
any
course in
control should make sure to convey the broad view that we describe
here, illustrating the power and limitations of feedback in modern
applications.
2.4. Openaccessresources
While running the first survey (Rossiter et al., 2020), it became ap-
parent that one thing the community could do better is share resources
with each other. It makes no sense for individuals around the world
to re-create each other’s work, especially when one individual may
have created excellent resources that we all could use, e.g. Albertos
(2017), Douglas (2022), Egerstedt (2022) and Rossiter (2022). Obvious
examples include inexpensive take-home kits (Oliveira, Hedengren,
& Rossiter, 2020; Rossiter, Pope, Jones, & Hedengren, 2019; Taylor,
Jones, & Eastwood, 2013) and virtual/remote laboratories (Brinson,
2015; de la Torre, Sanchez, & Dormido, 2006; Heradio, de la Torre, &
Dormido, 2016), but clearly this argument also applies to lecture slides
and other supporting materials like MATLAB, Python and Julia code
(e.g. Julia, 2022; Koch, Lorenzen, Pauli, & Allgower, 2020; MathWorks,
2022; Python, 2022; Rossiter, 2017) and more.
As part of the IFAC 50-year celebrations, a temporary control ed-
ucation resources website was created and subsequently a member of
the Spanish community offered a website as the base for a more formal
repository (Control, 2022). However, neither of these was particularly
effective in achieving high visibility to the community or gathering a
wide range of resources.
In recent years, the IFAC council also ran a pilot project on the
potential for developing and maintaining a control repository, but sadly
decided that the economic costs of doing this well were beyond their
means and thus, at least for the time being, have paused any work on
this. Hence, in the last 2–3 years (Rossiter et al., 2022) the Technical
Committees on Control Education of both the IEEE CSS and IFAC have
been working together on a vision of what form of repository they can
provide, which is sustainable and, critically, highly visible and useful
to the community.
The main idea that has emerged is that creating and maintaining
a website with all the resources hosted in it is not sensible because
it would require too much effort for one or more individuals who act
on a voluntary basis. It is more feasible to have a website with links
to the different resources, which remain in their original position and
are maintained by their creators. Further, the single resource is not
validated, but its assessment is left to the user. There is, therefore, the
need to constantly check that the links are working, which requires a
much more reasonable effort. The main challenge in this context is to
organize the website so that the users can easily find the most suitable
tool for what they are looking for and do not get lost in it Douglas
(2022). For example, the resources can be categorized according to
the topic, to their level (introductory, advanced) or to their format.
Providing a search engine is also challenging because it requires a
well-defined taxonomy of the topics.
The first step toward this goal has been a survey to collect freely
available resources related to the first control course. Preliminary
results of the survey indicate that the main type of resources are
videos, virtual or web-based interactive tools and remote/take-home
labs. Regarding videos, it is worth mentioning that there are many
open online modular courses and channels on youtube. There are also
holistic resources that aim to cover almost all the topics related to a
first control course. Finally, there is an increasing interest in providing
apps (Quanser, 2022) that can be easily accessible by the students
through their smart phones.
A control repository is really essential for diversity and inclusion.
The availability of open access web resources would allow a significant
improvement in the learning process of people with specific learning
disorders or disabilities. Further, they can serve as a social elevator for
students who have low incomes to increase the level of their education
and therefore to boost their career.
Annual Reviews in Control 55 (2023) 1–17
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J.A. Rossiter et al.
3. Aroadmapforthefutureofcontrolandrepercussionsonedu-
cation
This section describes and slightly extends the main outcomes of the
CSS working group (2019–2022) (Annaswamy et al., 2023).
3.1. Presentstateandfutureoutlook
As outlined in the introduction, a core aspect of control is a proper
understanding of system behaviors. How do different systems behave
and why do they behave that way? Building on an understanding of
behavior, the next critical question is: what behavior would we like and
what controls do we have to enable the desirable behavior? Of course,
the natural follow on is: how do we use these to control degrees of
freedom in a systematic way?
What is changing in the modern world is our realization of which
systems or system behaviors we as engineers can work on. As our
theoretical understanding has developed alongside a huge increase in
computational power at relatively low cost, it is now feasible to tackle
far more complicated problems than considered even just a decade ago.
In parallel, we can equally demand far better performance than it was
possible to deliver with more simplistic or traditional control strategies.
Finally, engineers are increasingly noticing the synergies between the
classical view point of control engineering and numerous societal prob-
lems which are amenable to similar solutions. Examples include: (i)
Feeding the world and digital farming (Cabrera, Pedrasa, Radanielson,
& Aswani, 2021; Caceres, Millan, Pereira, & Lozano, 2021); (ii) climate
change (Satue, Castano, Ortega, & Rubio, 2021); (iii) space, the final
frontier (Negri & Prado, 2021); (iv) underwater exploration (Rentzow
et al., 2021); (v) biomedical and disease control (Cassany et al., 2021;
Estigarribia, Bliman, & Schaerer, 2021); (vi) the sharing economy (Pers-
son, Andersson, Fattouh, Ekstrom, & Papadopoulos, 2021) and indeed
the reader could easily expand this list in many different directions. The
realization above sets a critical challenge to universities and the control
engineers within them: How do we prepare the engineers of the future
for this changing world?
It is clearly apparent that the classical control courses focused on
electro-mechanical or chemical processes and PID control are far too
limited. We need to expose students to the huge breadth of potential
applications and to the skills and techniques they will need. Follow-
ing on from the above challenge, some recommendations of the CSS
roadmap working group are:
1. Existing first courses in Control Systems should emphasise and
discuss the broader applicability of control.
2. Courses in Control Systems should be introduced earlier in the
curriculum and, where local conditions allow, teach fundamen-
tal control concepts to a broader class of students.
3. Modular organization alongside open-access (creative commons)
availability of teaching material allows more flexibility in course
design and delivery. This facilitates better targeting of content
and delivery to different cohorts.
The following sections explore these recommendations in more
detail.
3.2. Updatingthefirstcourseincontrolforbroaderapplicability
As noted above, we are now able to deal with far more complicated
systems, which include non-linear dynamics, event-driven dynamics,
hybrid dynamics, cyber–physical systems, and much more. This variety
of applications needs to be properly represented, even in an introduc-
tory control module, so students can see where their skills will be
needed. Moreover, there is a need for different mathematical skills and
approaches that go beyond the traditional linear ordinary differential
equations.
3.2.1. Frequencydomainvstimedomain
The recent survey (Rossiter et al., 2020) already made the point
that frequency domain, while still a useful tool, could be considered
somewhat of a ‘‘niche’’ and less appropriate to a first course seeking
to motivate students and expand their horizons. The working group
re-emphasised this point and argued that it would be more bene-
ficial to students to expand their awareness of a broader range of
modeling and analysis tools (see Section 4 for examples). Frequency
response and indeed root-loci methods apply to linear models, but
many of the challenges to society involve non-linear or discrete event
or hybrid models, where linear approximations are not appropriate.
As academics, we need to forge a balance in what we teach, so while
traditional design methods, including frequency response, still have a
role (and indeed PID approaches will still dominate many applications),
these are relatively straightforward to pick up as required. Control
education should strive to expand the student’s horizons rather than
solely preparing them for the mundane.
Many frequency response insights and approximations were re-
quired in a pre-computing era, whereas now it is more productive to
spend the time ensuring core concepts are understood and perhaps to
use modern computing tools (Lynch & Becerra, 2011; Rossiter, Giaouris,
Mitchell, & McKenna, 2008) and optimization to short-cut unnecessary
details; no doubt students who choose to become control engineers will
study and learn the fine details and coding when required.
Recommendation:
Students are often not interested in mathemat-
ically elegant approaches and these are less important to the current
average student.
3.2.2. Adaptingtoourstudents
Current students have grown up in an age of smart phones and
tablets with intuitive software interfaces and the ability to search the
web for any topic, anytime, anywhere. This has colored their percep-
tions of learning and indeed, more importantly the way they can learn.
If we try to painstakingly develop complex and abstract mathematical
foundations under the justification that it will come together by the
end of the course, students can easily become disengaged and choose
another course at a loss to our community.
Recommendation:
Although it may go against our historical prin-
ciples, it is better to begin with applications to motivate, computer
tools (like MATLAB and/or Python, which both offer a great catalog
of ready-to-use control applications and examples) to solve some in-
teresting problems and only then gradually introduce the mathematics
and physics that students will ultimately need.
3.3. Introducingacontrolsystemscourseearlier,forbroaderaudiences
Consider the titles: ‘‘Control systems’’ and ‘‘Building autonomous
systems’’ or ‘‘Introductory AI for engineering systems’’. While it sounds
trite, the buzz words of ‘‘AI’’ and ‘‘autonomous systems’’ are more
likely to garner interest so that students investigate further. Moreover,
even where a module is core, a title that communicates modern and
personal relevance will impact student attitudes and expectations as
they begin lectures; indeed, one author’s experience is that the exact
same course/delivery gets very different student evaluations from dif-
ferent engineering cohorts even though they sit in the lecture together;
perceptions of relevance impact experience! An enthusiastic student is
more likely to thrive and study hard.
Recommendation:
It is timely for institutions to reflect on their
choice of course titles.
It is important to recognize the different educational systems world-
wide. For many engineering programs, exposure to a single control
theory course is mandatory, but this may or may not be a separate
course or embedded in other larger courses; moreover, it may not be
until year 2 or even year 3. Later exposure is an obvious disadvantage
should one want to engage interest in the many possible electives that
Annual Reviews in Control 55 (2023) 1–17
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J.A. Rossiter et al.
could follow and thus there is an argument that the first course should
be in year 2 at the latest.
Given the importance of control and, in modern parlance, topics
such as AI, it would be hard to defend many engineering courses where
control was not included early enough and we have a duty to argue for
this with our colleagues. However, more critically we need to ensure
these courses are well-received and relevant to the modern world. It
is more important that a first course enthuses and motivates students
using a broad range of scenarios rather than dealing with detailed
mathematics. Without a broad range of motivated students, where will
the future control engineers come from?
3.3.1. Keyconceptstoemphasize
To some extent, control is about managing system behaviors and
thus the fundamental part of a first course is to engage students with
an understanding of what are ‘‘behaviors’’ and how are these classified
for convenience and comparison/evaluation? This view leads naturally
into the topic of modeling, as behaviors follow the model. Thus, a
good amount of time needs to be spent unpacking the different types
of models that can represent real systems and scenarios; these could
be linear ODEs, PDEs, discrete event or hybrid systems, data driven
systems, and more. Naturally, there is no time to introduce such a wide
range of models and associated behaviors in detail, so decisions need
to be taken as to the extent to which detailed mathematical analysis is
used as opposed to or in addition to the use of computer simulations
and/or hardware experiments. Also, the institutional context is critical
here, as there may be many interesting electives students can be
pointed to covering these topics.
Some mathematical precision is likely to be essential. There are
many core concepts in behavior such as stability, transient behavior,
speed of convergence and damping, which need quantifying so that we
can define meaningful criteria for comparing and contrasting activities
and design.
Nevertheless, a key aspect of modeling is the realization that models
are always approximations and thus the quantifications of behavior are
likewise approximations. A closely related characteristic of behavior
is adaptability/resilience or, in more traditional terms, the ability to
retain its behavior in the presence of unknown factors such as distur-
bances and faults (parameter changes). A core motivation for the use
of feedback is to deal with uncertainty so there should be a seamless
story. The challenge is to communicate the core concept of feedback,
and its critical presence in numerous diverse scenarios, without being
too occupied by detailed mathematical analysis whereby students lose
the connection to the core concepts. One could further argue about
whether true adaptive control is a seamless extension to this and could
or should also be discussed as a motivational device, obviously using
computational tools rather than detailed analysis which is more suited
to a later elective course.
3.3.2. Modelingandsimulationmethodologies
It seems more reasonable for the modeling aspect of a first course
to be focused on the time domain using, for example, component
equations and balance relationships. Typically, simple examples lead
to low-order ODEs and simple state automata, while more complicated
examples are best handled using simulation packages that generate
trajectories from more complicated ODEs or state automata rather
than developing modeling equations whose explicit solutions may not
be feasible to obtain. While it may be appropriate to use Laplace
transforms to support behavior analysis in some institutional contexts,
as a general rule, frequency response would be avoided in a short first
course and left for an elective.
A core point here is that solving ODEs (or Laplace transforms) by
hand is tedious and one would expect appropriate use of computer
packages to support visualization of behaviors across a broad range of
scenarios and parameters. Indeed, this also allows easy extension to
higher order systems and potentially more interesting examples which
the lecturer can provide pre-packaged for the students to use. Such pre-
packaged scenarios can also include a variety of important and realistic
issues such as uncertainty, faults and disturbances. One of the authors
has a number of these coded on MATLAB (e.g. Koch et al., 2020;
Rossiter, 2017) which students seem to appreciate and other authors
have found Python and Julia popular (Julia, 2022; Python, 2022), the
last two presenting the advantage of offering a free and open-source
solution.
3.4. Modularizingtheteachingexperience
As discussed in Section 2.4, a clear benefit of an effective repository
of teaching aids is that high-quality resources will be readily available
to the global community, both staff and students, and in a plug-and-
play form. This vastly increases the flexibility to develop new courses
with a reduced workload, or indeed, to produce micro-courses which
cover only 2–4 lectures of content. Naturally, this type of concept has
long been popular on YouTube and similar platforms (e.g. Khan, 2022;
Rossiter, 2022), but it is something we, as a community, should take
the opportunity to exploit better.
The COVID-19 pandemic, forced many lectures to be held online,
has given a great boost to teaching modalities that differ from the
traditional frontal teaching. In fact, students have perceived some clear
advantages of the increased availability of video lectures and web
resources at a very large scale. They can facilitate a new learning
paradigm where the students select by themselves courses based on
their own needs and preferences (for example, a teacher who explains
a topic in a clear way with the required level of details). On the one
hand, this personalized education might be beneficial for a person
who can select their best path for their learning process, but, on the
other hand, there is the clear risk that the student becomes lost in
the vast ocean of available resources. The real challenge is, therefore,
to organize the resources in a proper way, for example by providing
selected ‘‘journeys’’ (Douglas, 2022) and by classifying them according
to the required background, their goal, the system requirements in
case of software tools, etc. There are also big issues that need to be
addressed, such as the option to give comments/feedback from the user
and the option to modify/add some material (in a wiki-style fashion).
At this point, instructors should also possess the new ability to provide
the right suggestions to a specific student in order to complement their
teaching activity as well as possible.
There are two obvious scenarios we may wish to prioritise:
1. A lecturer wishes to redesign their course from scratch, but has
limited time.
2. Continuing professional development (CPD) where a practicing
engineer wishes to upskill in a focussed area.
For the former, we can support teaching staff by providing suitable
resources in plug-and-play form so that minimal editing is needed to
modify for the local context. Some good examples of this will be given
in Sections 4.1, 4.2, 4.3. Effective laboratories, either hardware or
software-based, take significant time to develop so it is even better if
we can pick up and use resources developed elsewhere which cover in a
high-quality manner the learning outcomes we are interested in. Better
still if these resources are free or very low cost. For the latter scenario,
Section 4.5 gives an excellent exemplar of how we can support CPD.
Naturally, teaching resources could also extend to Powerpoint
slides, short videos, code snippets, jokes and more. A core point here is
that if those resources are modularized effectively into small chunks so
that they can be adopted standalone, then they are much more useful
for sharing. Moreover, they are also much more useful to the end user
who may need to upskill in a narrow topic area and wish to do this
efficiently without having to engage with a whole course worth’s of
material first. In the authors’ view, this modularization of learning is
likely to become far more prevalent as the 21st century progresses and
we should prepare for this.
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Recommendation:
The essential thrust of the arguments in this
section imply that the role of historical textbooks needs to change.
Staff and students will not want to purchase a 400-page tome covering
everything; rather, they will want to be able to select from a range
of 10–20 page resources with far greater focus. The change to mainly
online access of textbooks has probably already occurred, so this slight
adaption should be straightforward.
3.5. Whatresourcesdowewantandwhy?
Having argued that modularization into relatively small chunks
is the future, the control community will need to decide how they
support and facilitate this, and indeed how material is clustered and
stored effectively. The discussion on this is ongoing in the Technical
Committees of the IEEE CSS and IFAC (Rossiter et al., 2022) and so
now is timely for colleagues to support and direct this initiative.
It is likely that the modules will need to be separated. It is important
to have some motivational, and perhaps more superficial, content such
as: (i) the power of feedback and (ii) simple AI techniques. There
will also be a need for modules with more in depth technical and
mathematical content. This will need to be separated into themes such
as (this list is not exhaustive but rather illustrative):
1. Modeling: (i) linear system modeling; (ii) non-linear system
modeling; (iii) an introduction to hybrid systems (time-driven
and event-driven dynamics); (iv) computational models; (v)
data-based models; (vi) adaptation and learning; etc.
2. Behaviors: (i) how do linear systems behave; (ii) managing
behavior; (iii) non-linear behaviors; etc.
3. Feedback: (i) Why is feedback important?; (ii) Practical feedback
implementations; (iii) Common feedback design; etc.
4. Applications: (i) Automotive; (ii) Aerospace; (iii) Biomedical;
(iv) Energy; etc.
5. Emerging topics such as system of systems, security, etc.
6. Control modeling and design tools: (i) PID; (ii) MPC; (iii) Super-
visory control; etc.
7. Hardware: (i) sensors; (ii) actuators
It is implicit that within each theme or topic, resources should take a
variety of forms such as:
1. Notes, handouts and slides.
2. Tutorial sheets, exams, assignments and online self-assessment
tools if possible.
3. Simulation tools, scenarios and code to support independent
investigation.
4. Laboratories (virtual, hardware and take home) with relevant
staff/student information packs.
5. Videos, audios and other learning resources.
3.6. Summary: Adapting education delivery to the 21st century and its
students
This section has made a number of recommendations which are
summarised here for clarity.
1. A first course would be better to begin with motivating applica-
tions and with computer tools to solve diverse problems from
a wide range of modern challenges and only then gradually
introduce the mathematical tools and analysis.
2. Modern students are often not interested in mathematically ele-
gant approaches and perhaps teaching staff should adapt to the
students rather than the other way.
3. Institutions should reflect on their choice of course titles and use
these to communicate relevance and excitement.
4. Learning and teaching resources should be modularized into
small chunks for ease of sharing, use and adoption.
5. Future courses are more likely to be arranged around a diverse
range of online resources rather than a single textbook. These
could be wrapped into ‘‘journeys’’ (Douglas, 2022) for ease of
use.
6. Modern technology has opened the door to cheaper and eas-
ier access to hardware/laboratory experiences which should be
exploited.
7. While not discussed in detail here, the community needs to con-
sider far more carefully the likely context and skills requirements
of the future engineers and redesign our courses accordingly.
We can expect that while mathematics and rigor will remain
important, the focus and timing of the methods is likely to
change.
4. Casestudies
Some readers may view this section as the most important. The aim
is to give concrete examples of how our curriculum can be brought into
the 21st century in terms of:
•
The topics we teach and the emphasis different aspects are given.
•
How delivery and student engagement is managed.
•
How we use laboratory exercises to support student development.
The section provides exemplars of good practice from several different
sources, and many of these resources are already open access, hence
easy for staff to adopt. The first focuses on the holistic design of
a
large
first course, student motivation and engagement, alongside
modernizing the curriculum. The second focuses on the expanding
area of
take home laboratories
(Oliveira et al., 2020; Rossiter et al.,
2019; Taylor et al., 2013) and how these give students opportunities
to develop independent learning skills and a deeper understanding of
core principles. The next two exemplars continue on the theme of
laboratory-like exercises and look at the role of virtual and remote
laboratories in supporting holistic student learning and also the key
factor of student engagement and enthusiasm. The final case study
considers postgraduate education and the researchers of the future and
how we, as a community, can better support them.
4.1. Exemplar large first control course from University of California,
Berkeley
EECS16AB ‘‘Designing Information Devices and Systems’’ is a two-
semester course sequence designed to be taken by first-year students
at Berkeley, and offered by faculty from the Electrical Engineering and
Computer Sciences department. As will be clear, this is a substantial
commitment, but that provides opportunities to achieve much more
holistic learning and development. Students typically take two or three
technical courses a semester at Berkeley so this course would comprise
a third or more of their year’s studies. A typical schedule for a student in
their first semester would include EECS 16A along with an introductory
programming course (CS 61A). EECS 16B would be taken in their
second semester along with a more advanced data-structures/software
class (CS 61B).
The course requires students to have taken Advanced Placement
(AP) calculus (AP, 2020) (or an introductory college calculus class) and
has no physics prerequisites. It is designed to be taken concurrently
with an introductory computation/programming class (such as CS 61A
at Berkeley). EECS 16AB provide a strong foundation in linear algebra,
as well as an introduction to machine learning, circuit design, control
and signal processing. A series of hands-on labs showcases how the
mathematics has concrete impact through engineering applications. By
the end of the two course sequence, i.e. the end of 16B, the students
complete a final project (in groups of 2–4 students) where they build
their own robot car that responds to voice commands (see Fig. 1 for
a student showing off her final project). This is an integrative project
that combines ideas from all the areas mentioned earlier. It showcases
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Fig. 1.
A proud student showing off her final project, the voice-controlled robot car
in EECS16B at UC Berkeley at the end of her first year. The project uses principal-
components-analysis to learn the voice commands and pole-placement for control of
the car.
the full stack of modern data-driven design to students — from doing
experiments and collecting the data, to learning a model based on it,
to designing a decision-rule/control-law based on this model, imple-
menting it, and observing how it behaves in the physical world. For
all of this, they have built circuits, programmed a microcontroller and
used Python to identify system dynamics (learn the model) and build
a classifier.
The course sequence is built around six modules as below:
•
(16A) Introduction to systems (tomography/medical imaging,
matrices, inverse problems, PageRank)
•
(16A) Introduction to circuit design (touchscreen design, circuit
components, basic feedback using op-amps)
•
(16A) Introduction to machine learning (GPS design, least
squares, sparsity, Orthogonal Matching Pursuit)
•
(16B) Introduction to differential equations (Transistors, CMOS,
RLC, time constants, filters)
•
(16B) Introduction to robotics and control (Stability, controllabil-
ity, feedback)
•
(16B) Introduction to unsupervised machine learning and clas-
sification (singular value decomposition, principal components
analysis, linearization, classification)
4.1.1. Coursephilosophy
Here we try to explain some of the objectives behind the design of
these courses.
•
Inclusive
: 16AB students have different backgrounds, personal-
ities, and a-priori interests. It is often unclear to students what
practical problems can be solved by an engineer. Our goal is for
each student to find
something
in the course that resonates with
them. While some students may have access to family in STEM
fields, many have not. Many students come to us without fully
knowing what the words signal processing, machine learning,
or design even mean. Our goal is to demystify this jargon, all
while providing tangible examples of what engineers can do with
mathematics (and allow students to immediately achieve these in
the lab.) We try to reflect the diversity of intellectual work in
Fig.2.
Illustration of the Imaging Lab in EECS16A. The projector illuminates the object
to image, and the reflected light is collected by the photodiode, which is connected
to the Arduino. By using different illumination masks, the we collect different linear
combinations of pixels from the image, and can use matrix inversion to generate the
image.
an engineer’s life: students are exposed to modeling and mathe-
matical work, debugging in hands-on labs and writings software.
The diversity of perspectives allows students to find their niche —
what is the application that motivates them or the skill they excel
at? In a STEM environment where many students are exposed
to negative stereotypes such as ‘brogrammers’, this diversity can
allow students to find an intellectual home and feel they belong.
•
Supportive:
The course provides high-touch instructional re-
sources to help students be appropriately supported with the
challenging material so they can all succeed as individuals. We
use a non-curved grading policy to this end, and provide many
different, non-traditional avenues for students to engage with the
material, ranging from social dimensions, diverse applications,
diverse role models, teaching opportunities. Students can earn
extra credit through art and media projects related to the course
(we have had students writing songs or making videos about the
course).
•
Motivating, Empowering, and Inspiring
: Since we want to
reach all students, even those who are not necessarily committed
to engineering, we try to have every concept be motivated by a
concrete application. A student may not be a-priori motivated to
memorize the formula for a matrix inverse. However, using the
concept of inversion to build a camera and understand medical
tomography provides a concrete motivation to study mathematics
that might otherwise seem esoteric (see
Fig. 2 to for the hardware
setup of the tomography lab). As a concept is developed, we
aim to empower students to do something that they could not
do (or even conceive of) before. With this in mind, homeworks
connect to a wide range of applications even beyond the lab
(ranging from biomedical applications to home appliance design).
While programming is required for the course, all assignments are
Jupyter notebook-based and require minimal coding with skele-
tons largely provided, and the students only do the mathematical
parts of the programming.
•
FoundationalandRigorous
: The goal of this course is to prepare
students at the level of a single-semester lower-division linear
algebra course in the Mathematics department, and maybe a little
beyond. Based on a backbone of linear algebra, we build out
the foundations of linear circuit analysis, control of dynamical
systems and learning from data. Definitions are motivated and
then results are derived, albeit often in simplified models. The
idea behind modeling and how to use this idea is explicitly dis-
cussed. We try to get students to question assumptions, explicitly
introduce proofs and talk about design and debugging. The course
goes significantly deep and is not intended to be a survey class.
Admittedly, the goals for the course here are a bit ambitious, and
achieving them comes with some trade-offs. First, in order to be able
to discuss applications in detail, we spread out the content of a one
semester linear-algebra course over two semesters. We also rely on
Annual Reviews in Control 55 (2023) 1–17
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students having taken calculus in their high-school or community col-
lege.
1
We also require that students take a Python-based introductory
CS course as a co-requisite to be adequately prepared. The course
workload is quite high, with a total of eight minimum contact hours
per week (three lecture
+
two discussion section
+
three lab) and
additional time spent on homework. In order to be able to get to some
of the more advanced topics that may traditionally have been taught in
upper-division courses, we must follow a very careful path through the
course, with less time to explore tangential paths that might otherwise
be interesting. The circuits material is taught with limited exposure
to the physics aspects, given the short time. UC Berkeley has been
teaching this course at scale with approximately 1500 students taking
the course sequence every year. One faculty member works with about
30 teaching assistants (largely undergraduates working 8 h/week) and
60 tutors/graders (all undergraduates working 4–6 h/week).
4.1.2. Furtherdetails
Each of the applications from the course is discussed in more
detail below. Even though we organize modules around applications,
the course emphasizes interconnections across areas and integrative
mathematical thinking.
•
Tomography and Imaging (Modeling): The course starts with an
introduction to linear algebra and inverse problems, motivated by
the application of medical imaging. The lab consists of building
and programming an imaging system using structured illumi-
nation from a projector and measurements using a single pixel
sensor to reconstruct an image.
•
PageRank (Systems): We introduce the idea of state and use the
example of interconnected webpages and their state (in terms of
the number of people visiting the webpage) to understand the
idea of linear transformation. The motivation here is figuring out
the most important web-pages in a graph of interconnected sites.
This brings in concepts of eigenvalues, eigenvectors, as well as
connecting to recurrence relations.
•
Touchscreens (Design/Circuits): This module introduces basic lin-
ear circuit theory motivated by the problem of sensing touch in
the real world. The analysis is grounded in a linear-algebraic per-
spective where both circuit elements and the topology introduce
linear equations relating basic current and voltage variables. In
addition to analysis, the module shows how having interpretable
building blocks and understanding their interconnections is useful
for design. This especially comes out when discussing op-amps
and basic feedback. The lab consists of building resistive and
capacitive touchscreens.
•
Positioning (Machine Learning): This module introduces opti-
mization as a way to extract information, motivated by the prob-
lem of figuring out where you are in the world, i.e. the problem
of GPS. This brings in linear-algebraic concepts of inner-products,
norms, orthogonality, projections, and linear regression, as well
as the signal-processing idea of correlations. The lab consists of
programming a receiver for an indoor localization system based
on listening to acoustic beacons being transmitted by speakers at
known locations.
•
(16B) CMOS (Differential equations, system modeling): The first
module in the second semester introduces basic CMOS and an
understanding of differential equations, motivated by the prob-
lem of understanding why digital computations are limited in
speed. The approach to ordinary differential equations connects
to the eigenstructure of the matrices involved. The lab consists of
making an analog-to-digital converter and vice-versa.
1
The two-course structure allows students to take calculus in parallel with
the first-semester 16A, though we prefer students have completed one semester
of calculus before.
•
(16B) RLC circuits (Differential equations): RC circuits are intro-
duced as a way to model the CMOS earlier, and we build on
this to discuss the application of filtering (using RLC circuits).
We motivate this by the practical problem of rejecting ambient
signals, like 60 Hz noise, while trying to sense neural signals. By
thinking about what happens to linear constant coefficient matrix
differential equations driven by complex exponential inputs, we
arrive at transfer functions. The lab consists of building filters for
audio input for the robot car.
•
(16B) Robotics (Control): This module introduces basic state-
space linear control theory in the context of following a de-
sired trajectory for a robot (linear plant) while rejecting dis-
turbances and model errors. We introduce the ideas of stability
(connected to eigenvalues and differential equations) and con-
trollability (connections to subspaces and rank). We introduce
the key idea of feedback and how this can change the eigen-
values of a closed-loop system. Linearization is taught to get
an approximate model in the neighborhood of a trajectory and
system-identification from data is connected to the least-squares
approaches they learned in 16A. The lab consists of making a
robot car drive straight.
•
(16B) Spike-sorting/Classification (Unsupervised Machine Learn-
ing): This module introduces the ideas of principal component
analysis and clustering in the context of classifying signals from
distinct neurons. Here we introduce singular value decomposition
(via the spectral theorem for symmetric matrices). Singular value
decomposition (SVD) allows us to identify the principal compo-
nents and classify data. We also use the application of image
compression to introduce low-rank approximations to data. The
lab consists of doing simple speech recognition for the robot car.
Throughout these modules we try to highlight the similarity in the
modeling and mathematical approaches that span the areas of control,
signal-processing, machine learning and circuits. Our goal is to build
versatile students who cross traditional disciplinary boundaries, and
who are aware that each of these application areas are not intellectual
silos.
4.2. Exemplaruseofhardwaretoengagestudents
With the recent shift to remote learning during COVID, many in-
structors had to find alternatives to large campus-based experimental
activities. This has probably accelerated the adoption of home-based
learning modules/equipment and assessments of learning outcomes for
these miniature labs (de Moura Oliveira, Hedengren and Boaventura-
Cunha, 2020; Oliveira & Hedengren, 2019; Oliveira et al., 2020). Such
low-cost and compact hardware enables students to have substantial
hands-on learning at home and plan individually motivated experi-
ments to reinforce key concepts; here we focus on their usage for a
process dynamics and control course.
4.2.1. Conceptsandlearningoutcomesofabasiccontrolcourse
A typical process dynamics and control theory course has elements
of transient system analysis and regulatory control design as shown
in Fig. 3. Control design includes selection of an actuator and mea-
surement. If a physical system is available for data collection, the
actuator is moved with a step or other type of move plan to excite
the measurement for model identification. If a physical system does
not exist, a simulated system as a physics-based digital twin can be
substituted to either generate data or linearized to produce a standard
model for controller development. Some of the standard models include
first-order, second-order, and state space forms.
Controller development begins with an assessment of any additional
measured disturbances that can be included as a feedforward or cascade
controller. The selection of P-only, PI, or PID control is based on the
system characteristics as integrating (P-only) or non-integrating (PI
Annual Reviews in Control 55 (2023) 1–17
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J.A. Rossiter et al.
Fig. 3.
Course overview and flow of concepts for dynamic modeling (top half) and controller development (bottom half).
or PID). The tuning of the controller begins with a stability analysis
to determine the range of acceptable controller gain (
퐾
푐
) values. In-
ternal Model Control (IMC) tuning correlations are used to convert
the standard model forms into candidate values for PID parameter
implementation. The controller is tuned to achieve acceptable metrics
such as rise time, overshoot ratio, and settling time over a range of
setpoint changes.
Many control courses include a laboratory element to reinforce the-
ory and expose students to issues with real data such as measurement
noise. An example of the stark contrast between theory and practice is
with model mismatch of a physics-based simulation and measurements.
Students discover uncertain parameters in physics-based equations or
limitations of assumptions such as input constraints and computing
delays that lead to the mismatch. Another example of the value of real
data is that each physical system is slightly different. The differences
are created from variations in manufacturing and ambient conditions at
the time of the test. This lack of exactly reproducible results discourages
plagiarism and encourages adaptation of the methods for each device.
Moreover, students have reported that the hands-on and long-term
nature of a laboratory which can be accessed regularly and easily
throughout the course helps to solidify key theoretical concepts.
4.2.2. DescriptionandusageoftheTCLabtakehomeequipment
The temperature control lab (TCLab) is a very cheap (circa 40
dollars) take-home micro-controller with two temperature sensors, two
heaters, and an LED indicator (see
Fig. 4 ). The device connects to a local
computer via a USB and uses a simple domestic power source. Critically
therefore, each student can borrow for the entire semester a TCLab
device and thus use these safely at home to reinforce each concept in
a dynamics and control course. An open source website (
Temperature
,
2023) provides 23 lab modules that reinforce major topics of a typical
dynamics and control course with source code in Python (interactive
Jupyter notebooks) and M
atlab
(interactive live scripts and Simulink).
Instructors at 70 universities have adopted the TCLab as a take-home
lab experience with several adaptations of the content based on expe-
riential learning time (typically 1 week to 12 weeks). The TCLab is
also a benchmark for research in control algorithm development (
de
Moura Oliveira, Hedengren and Solteiro Pires
, 2020
; Mejía, Salazar,
& Camacho
, 2022
; Park, Martin, Kelly, & Hedengren
, 2020
; Sharma &
Padhy
, 2022
; Yerolla & Besta
, 2021
).
As noted in Section
4.1 , the provision of transparent and simple
template code in popular software to perform many different standard
tests (open-loop step, PID, etc.) means that students (and staff) can
focus on learning and applying the core concepts rather than being
bogged down by writing code to interact with the hardware.
At Brigham Young University, students complete a TCLab exercise
with each assignment throughout a 14 week course. Homework is due 3
times per week as micro-modules that include theory, simulation, and
the lab exercise. Each assignment is designed to be completed in 1–
2 h. In addition to the learning modules, a course project extends their
Annual Reviews in Control 55 (2023) 1–17
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Fig. 4.
The Temperature Control Lab (TCLab) connects to a computer for Python or M
atlab
hands-on exercises.
understanding to interacting control. Students create a 2 heater x 2 tem-
perature model with two interacting PID controllers with feedforward
elements. The TCLab project is a precursor to small team projects where
they design, build, and tune a physical system that includes an actuator,
sensor, and controller. This progression from homework exercises, to
TCLab project, and finally to an open-ended project is motivating for
students. Students gain hands-on experience that reinforces dynamics
and control theory.
As a complementary example, at the University of Sheffield the
equipment is used as the base for an open-ended assignment in lieu of
an exam; students are asked to demonstrate the importance of feedback,
the impact of different practical issues (e.g. delay, uncertainty, con-
straints, sampling) and how to tune PI effectively. It is notable from this
how much more effectively and confidently the students understand
the course content as compared to when they had an end of year exam
instead.
4.3. Exemplar use of virtual and remote laboratories to support learning
andunderstandingfromSpain
While the approach presented in the last section presents several ad-
vantages, there are many cases where the hardware, or lab equipment,
cannot be taken home by the students, either due to its scarcity or to
its size and weight. A solution to also provide off-campus lab activities
are the so-called virtual and remote labs (
Dormido
, 2004
). While virtual
labs work with simulations based on mathematical models, remote labs
use physical devices to perform real experiments. Other related solu-
tions are hybrid laboratories (which usually use a virtual model but a
real controller) (
Zapata-Rivera, Larrondo-Petrie, & Weinthal
, 2019
) and
digitized laboratories (which store real measurements and serve them
on-demand through an interactive app) (
de la Torre et al.
, 2020
). All
these approaches present their own advantages and limitations, but it is
generally accepted that using a combination of virtual and remote labs
is a good approach when delivering a complete lab experience (
Heradio
et al. , 2016
). As such, while this section applies primarily to a first
course, it also crosses over to more advanced courses.
This section presents a course overview, run in UNED as part of one
of its Master Programs, that uses the above lab resources to facilitate
experimental work to their students in a distance education context.
However, similar approaches also exist in traditional universities, such
as Lei, Zhou, Hu, and Liu
( 2022), offering virtual and remote labs as a
complement to their hands-on lab activities.
4.3.1. Briefoverviewofthevirtualcourse
The online course consists of ten practice units
2
; each of them
focused on a particular system or process (
de la Torre, Chacon, Chaos,
Dormido, & Sánchez
, 2019
). Every practice offers a virtual lab that
models and represents a simulated version of the system, as well
as its corresponding real remote laboratory. These units also contain
documents to introduce the students to: (1) the theoretical background
and the model of the plant; (2) the user interface for the virtual and
remote lab applications and (3) the experimental tasks they are asked
to perform.
2
https://unilabs.dia.uned.es/blog/index.php?entryid=3
.
Fig. 5 shows a screenshot of one of these practice units in the course.
In particular, students must follow these steps to complete each practice
unit:
1. Read the document with the introduction to the theory.
2. Read the manual about how to use the user interfaces for the
virtual and remote labs.
3. Read the document with the list of tasks to be performed during
the practice.
4. Access the virtual lab and do the required experimental tasks.
5. Prepare and present a lab report using the measurements and
knowledge obtained with the virtual lab.
6. Access the remote lab and do the required experimental tasks.
7. Prepare and present a lab report using the measurements and
knowledge obtained with the remote lab.
Table
1 lists a selection of the virtual and remote labs in the online
course and offers a brief description of the systems themselves.
When working with the virtual and remote labs, students can usu-
ally: (1) tune the parameters of already pre-built controllers to meet
some specifications in the system response, and/or (2) code their own
controllers in Javascript, among many other things.
4.3.2. Avirtualandremotelabexample
The Furuta pendulum (
Fig. 6 ) is a device consisting of an inverted
pendulum pivoting on a rotating base. The turn of this base allows
control of the position of the pivot and thus, indirectly, the angle of the
pendulum. This is a challenging device because it is unstable (when the
pendulum is in the upwards position) and it also exhibits non-minimum
phase behavior.
Figs. 7 and 8 show the virtual and remote versions of
this lab.
With this laboratory students can perform, among others, the fol-
lowing tasks and activities:
1. Develop a control law that keeps the pendulum in an upwards
position while the pivot of the pendulum follows a reference
signal. This control law is based on linearization around the
unstable equilibrium point.
2. Implement a swing up control that is able to swing-up the
pendulum from its stable equilibrium position (downwards) to
its unstable equilibrium point (upwards) in order to apply the
control developed on the previous step.
Both the virtual (
Fig. 7 ) and the remote (
Fig. 8 ) labs, present a visual
programming editor that allows students to:
1. Define their own charts. The charts that appear in both figures
are not present when the lab is loaded. They only appear when
the user defines the chart. Thus, students need to think and
decide what data they consider relevant to visualize and plot for
each experiment they perform.
2. Code their own controllers. Students are asked to replace the
built-in controller and test their own. This is done using the
replace function
blocks, which involves writing Javascript code
to code the controller.
Annual Reviews in Control 55 (2023) 1–17
11
J.A. Rossiter et al.
Fig. 5.
One of the ten practice units in the online course, containing documentation and the virtual and remote labs.
Table 1
Description of a subset of the available laboratories for remote access at UNED.
Title
Description
Tension and velocity control of a belt on
a two coupled electric drives system
Two electric drives coupled with a flexible belt that passes through a pulley with a system that allows measuring its
velocity and tension. The main control problem is to change the torque in the motors in order to regulate the tension
and the velocity of the belt. This can be done either individually or simultaneously.
Level control in a four tanks system
At the bottom of each tank, there is an outlet of known section and another one with an unknown section, regulated
by means of a valve that enables or disables the corresponding perturbation. The system also has two three-way valves
that allow regulating the flow, coming from two pumps, that enters in each of the tanks.
Control of the ball and hoop system
An electromechanical device consisting of a ball rolling on the rim of a hoop. The hoop is mounted on the shaft of a
servomotor and can rotate about its axis. The rotation of the hoop causes an oscillatory movement of the ball around
its equilibrium point. The behavior of the ball is similar to the dynamic of a liquid inside a cylindrical container,
being the main objective to control these oscillations.
Control of the ball and plate system
This system consists of a ball rolling on a rigid square plate. The main purpose of this system is to control the position
of the ball by manipulating the slope of the plate in two perpendicular directions. This system is employed in the
aeronautical industry for the development of vehicles simulators.
3. Control the execution flow of the lab. More options, such as run-
ning, stopping or resetting the experiment are available through
the blocks, as well as reading and or writing variables from
the lab. This, in combination with the previous features, offers
students an open sandbox to work with the lab.
4.4. ControlsystemscourseatImperialCollegeLondon:On-siteandremote
quadrotortest-bedexperiment
Continuing the theme of laboratory activities in engineering edu-
cation, here, we discuss a hybrid implementation which mixes remote
and face to face access. The section discusses the integration into the
second-year basic Control Systems course at Imperial College London
of a laboratory test-bed developed to replicate the dynamic behavior
and the control design challenges of an under-actuated multi-rotor
Unmanned Aerial Vehicle (UAV) (
Fig. 9 ). The test-bed ANT-X 2DoF
Drone (
ANT, 2020
) is based on a quadrotor UAV fixed to a structure
that allows only the longitudinal motion. Using specific open-source
firmware developed in Simulink, the drone can be directly operated
from MATLAB, relieving students from coding implementation issues.
Moreover, the equipment can be operated remotely.
In the following, we summarise some of the available experiments
and how the equipment was integrated into module delivery to op-
timise student learning and engagement, alongside an evaluation of
student experience (
Panza, Invernizzi, Giurato, Yang et al.
, 2021
).
Comment:
The reader will note that this case study, as indeed does
Section
4.1 goes slightly beyond a simple and short 20 h course in
control in terms of its content and specifically also includes use of some
elementary frequency response methods. We feel the good practice here
is nevertheless interesting and useful, indeed more so for indicating
how longer courses can go beyond earlier generic recommendations.
4.4.1. Thetest-bed
This section gives a concise test-bed description with more details
found in
Panza, Invernizzi, Giurato and Lovera
( 2021). The ANT-X
2DoF Drone has been designed to replicate the longitudinal motion
in near-hovering flight conditions when the multi-rotor dynamics can
be approximated by four decoupled sets of differential equations de-
scribing, respectively, the yaw, the altitude, the pitch/
푥
-translation and
roll/
푦
-translation dynamics (
Ghignoni, Buratti, Invernizzi, & Lovera
,
2021). The linearized longitudinal dynamics are described by:
̇
휃
=
푞
퐽
휃
̇푞
=
푀
푐
+
푀
푒
(1)