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(Since August 15th,
The CDC 2017 will offer full-day and half-day pre-conference workshops on
Sunday, December 10, 2017, and on Monday, December 11, 2017,
addressing current and future topics in control systems from experts
from academia, research institutes, and industry. The location for the workshops is the campus of University of Melbourne,
which is also sponsoring the event.
All workshops will be held at:
University of Melbourne
Melbourne Law School
Level 6, Law Building
185 Pelham St, Carlton VIC 3053
In the following the list of Workshops is reported together with the names of the organizers, the name of the speakers, the abstract, the target audience, and a detailed agenda of the event.
|List of Half Day Workshops Offered at the 56th CDC (Sunday 10 December 2017, 1PM-5PM) |
|List of Full Day Workshops Offered at the 56th CDC (Monday 11 December 2017, 9AM-5PM) |
30 years of the Ramadge-Wonham Theory of Supervisory Control:
A Retrospective and Future Perspectives
Stephane Lafortune, Karen Rudie, Stavros Tripakis
Gain Scheduled Model Predictive Control System
Design and Implementation
Liuping Wang, John Anthony Rossiter
Model-Based and Model-Guided Approaches
for Automotive and Maritime
Powertrain Calibration and Control
Guoming Zhu, Xiang Chen, Chris Manzie, Ying Tan,
Noam Olshina, Gokul Siva Sankar, Gowri Sankar
Deterministic and Stochastic Hybrid Methods
for Robust Learning and Optimization
in Dynamical Systems
Jorge I. Poveda, Andrew R. Teel, Mouhacine Benosman,
Kyriakos G. Vamvoudakis, Chris Manzie, Martin Guay
What's the big deal with Deep Learning?
Girish Chowdhary, Alexander Schwing, Hassan Kingravi, Chinmay Soman
Economic Model Predictive Control: State of the Art and Open Problems
Over the last years, the development of tailored optimization methods and increased computational
power have led to a considerable speed-up of Nonlinear Model Predictive Control (NMPC) algorithms
such that new areas of application besides classical process control can be targeted. Alongside this
development, one can observe a trend to consider more flexible formulations in NMPC with respect
to the considered cost functions. In other words, a lot of recent research efforts have focused on
considering so-called economic objective functions in NMPC, which gave rise to the term Economic
MPC (EMPC) for such approaches [1-21]. In EMPC, compared to classical stabilizing (tracking) MPC, the
primary control objective is not stabilization of an a priori given setpoint (or trajectory), but rather
optimization of some general performance criterion. As a consequence, the resulting closed-loop
system might not converge to an equilibrium setpoint, since a different operating behavior (e.g.,
periodic) might result in a better performance.
In recent years, various results on different aspects of economic MPC have been presented in the
literature, and many of the crucial (theoretical) questions have been answered. Furthermore,
successful implementations of economic MPC have been reported in various fields of application such
as energy and process systems. In conclusion, the field of economic MPC is, by now, in a quite mature
state. On the other hand, there are still various open issues and problems. As detailed below, the
proposed workshop is intended to give an overview of the main developments in economic MPC
obtained in recent years, and will also point the participants to interesting open problems.
The objective of the proposed half-day workshop is to provide the participants a tutorial overview of
recent developments on economic MPC. In particular, the workshop will cover a wide range of
different EMPC approaches and topics, including closed-loop performance guarantees, the
classification of the optimal operating behavior, closed-loop convergence analysis with and without
dissipativity assumptions, economic MPC approaches with and without terminal constraints and
terminal penalties, time invariant and time-varying problem data. The intended audience includes
both PhD students as well as experienced researchers working in the areas of optimal and model
predictive control. Considering the relevance of economic MPC for many application areas – such as
energy, productions, and process systems – and the ongoing scientific interest in the topic, the
proposed workshop is likely to attract a large number of participants.
Organizers and speakers:
Timm Faulwasser, Lars Grüne, Matthias A. Müller
Distributed Parameter Systems: from theory to applications
Using a series of four interrelated lectures, this half-day
workshop will demonstrate the basic theoretical underpinnings
of DPS via a series of engineering examples and
subsequently will discuss the practical aspects of controller
design for DPS. The interplay between the sensor technology
limitations will be presented in the context of mobile sensor
guidance in distributed estimation of DPS and an overview
of data assimilation in the estimator design for DPS will be
At the conclusion of the half-day workshop, the participants
are expected to have an understanding of the modelling
and system theoretic properties (well posedness, controllability,
observability, stability) of representative examples of
infinite dimensional systems including the diffusion equation,
the wave and beam equations, delay differential equations
and platoon-type systems. Additionally, the participants are
expected to have an understanding for the practical aspects of
integrated actuator-sensor-controller design, an appreciation
of the sensor technology limitations and their effects on the
distributed estimation of DPS and to have a familiarity of
the data assimilation in the use of estimation of DPS.
The proposed half-day workshop is divided into four
lectures addressing various aspects for the control and estimation of DPS.
The starting point is the revised book An Introduction
to Infinite-Dimensional Linear Systems Theory, Springer-Verlag,
New York (1995), by Ruth Curtain and Hans Zwart.
The point of departure will be an introduction to Lyapunov
stability and colocated systems with boundary control. This
introductory talk will be given by Ruth Curtain.
Subsequently, Kirsten Morris will extensively discuss the
modeling of the actuators and sensors in control design and
how this affects system behaviour. Physical intuition does
not always lead to the best choice of locations.
The incorporation of the sensor technology limitations
into the design of distributed state estimation with mobile
sensor networks will be presented and discussed by Michael
A. Demetriou. A performance-based guidance and spatial
repositioning of mobile sensors that considers sensor technology
limitations, the computational model used and physical
properties of the process will be discussed.
The ultimate goal for simulation and control synthesis, is
the application of the distributed-parameter systems theory
in practice. Thus the final lesson, given by Sergiy Zhuk, will
focus on experimental studies which have been done by the
speaker in collaboration with T. Tchrakian, A. Akhriev, S.
Thirupati, S. McKenna (IBM Research-Ireland), S. Lu and
H. Hamann (IBM T.J.Watson Research Center) and S. Moore
Covering both theory and applications, the workshop is of interest for a broad audience.
Organizers: Michael A. Demetriou, Orest V. Iftime
Speakers: Ruth F. Curtain, Kirsten Morris, Michael A. Demetriou, Sergiy Zhuk
Scenario-based optimization for stochastic optimal power flow problems
This workshop aims to provide systems and control researchers with, 1) an
understanding of the challenges of power system operation in the presence of
generation uncertainty arising from renewable energy sources, and 2) methodologies for
achieving optimal and reliable operation. Generation uncertainty may significantly
increase operational costs due to the resulting stochastic formulation and the additional
reserve requirements. Existing optimal power flow (OPF) and reserve scheduling
algorithms need to be reformulated to provide solutions that are reliable with respect to
uncertainty. The workshop will initially focus on a specific class of stochastic optimization
tools that can provide solutions with probabilistic performance guarantees. Those tools
build on scenario-based optimization and may be applied to both convex and non-
convex problems. The workshop will then provide an overview of power flow models and
establish optimal power flow formulations. Finally, the workshop will present stochastic
formulations of the optimal power flow and reserve scheduling problems, and
demonstrate how scenario-based optimization techniques can be applied to achieve
reliable and cost-effective operation. Various forms of policy-based feedback control will
be presented for generator reserve deployment, and extensions to other forms of
controllable resources will be considered. A discussion of the effects of policies on
economic performance will be provided.
The workshop is intended for systems and control researchers who have interests in the
large-scale integration of renewable energy sources in power systems. The expected
audience includes engineers and scientists from industry and academia. The workshop
will be self-contained so that it is suitable for participants who may not have prior
familiarity with the topics.
Organizers and speakers:
Ian Hiskens, John Lygeros, Maria Vrakopoulou
Detectability for Time-Varying Systems
Similar to observability, detectability indicates the ability of a dynamic system to use its
output signals from sensors and the model to “observe” the behaviour of the state that can
fully capture the characteristic of the system. Thus it plays an important role in many
engineering applications in order to design a feedback control law using the measured output
signals. For a time-invariant system, the well-defined detectability also indicates its uniform
attractivity under mild assumptions. However, it is usually much harder to define such
detectability for complex time-varying dynamic systems such as switching systems and
hybrid systems. The main objective of this tutorial is to present a few different definitions of
well-defined detectability for different types of systems; to reveal the link among
detectability, the persistent excitation (PE) and uniform attractivity; and to demonstrate the
power of detectability in analysing stability properties for switched nonlinear time-varying
systems to researchers in the area of systems and control who may not be familiar with the
concept. This tutorial will
- revisit the concept of detectability and its link with uniform attractivity for linear-
- demonstrate the difficulty of the well-defined detectability for time-varying systems
and revisit various detectability definitions of different dynamic systems in literature;
- introduce the concept of Signal Set, which can serve as a unified framework to
characterize a large class of dynamic systems;
- introduce a well-defined detectability, weak detectability (WD) in the Signal Set
- reveal the link among WD, PE condition and uniform attractivity;
- provide sufficient conditions to check WD;
- demonstrate the power of WD in stability analysis of switched nonlinear time-varying
systems including arbitrarily switching cases and constrained switching cases with
new stability results;
- consider a few examples from the literature to illustrate the strength of the proposed
Target audience: the expected audience includes engineers, scientists,
postgraduate students, and academic researchers.
Organizers and speakers: Ying Tan, Ti-Chung Lee, Iven Mareels
30 years of the Ramadge-Wonham Theory of Supervisory Control:
A Retrospective and Future Perspectives
2017 marks the 30-year anniversary of the publication of the two seminal papers of Ramadge and Wonham
on supervisory control of discrete event systems, in the January and May issues of the SIAM Journal on
Control and Optimization (SICOPT) in 1987:
These two papers launched the area of supervisory control of discrete event systems within the control
engineering community. Their general framework based on regular languages and their finite automata
representations provided powerful foundations for the development of this supervisory control theory by a
worldwide community of researchers in control engineering, including continued contributions by Ramadge
and Wonham with their students and collaborators. Nowadays, supervisory control a la Ramadge-Wonham
is a broad theory that covers partially-observed systems, a variety of control architectures
that exploit horizontal and vertical modularity,
and extensions to timed systems. Indeed, as of this writing, [RW1] has
over 3,200 citations according to Google Scholar, with yearly citations
in the range of 121-171 since 2003.
Supervisory control a la Ramadge-Wonham remains an active area of research, and several real-world
applications have recently been demonstrated in domains ranging from electric
vehicles in theme parks to patient
support systems in MRI scanners, among others.
More recently, the area of formal methods in control has gained prominence in the control community,
primarily in the context of cyber-physical systems that are abstracted as discrete transition systems subject
to specifications expressed in temporal logic. The terminology "formal methods" comes from the computer
science literature and it encompasses a set of mathematically-formal techniques that have been developed
for the verification of hardware and software systems, and more recently for the synthesis of systems or
programs that interact with their environment, or reactive systems. Reactive synthesis, as this latter problem
is called, is fundamentally a problem of feedback control.
The Ramadge-Wonham theory of supervisory control is itself a formal method in control, for the case of
specifications that can be expressed as regular languages, the same modeling paradigm as the uncontrolled
system. In that sense, it is complementary to works on reactive synthesis that handle certain classes of
specifications expressed using various fragments of temporal logics.
The goal of the workshop will be to offer a retrospective of the Ramadge-Wonham theory of supervisory
control and to present some of its recent developments and applications. In addition, the workshop will
attempt to connect the Ramadge-Wonham theory with the emerging area of formal methods in control and
more generally with the work in reactive synthesis in computer science. To this end, the list of speakers will
comprise researchers associated primarily with the area of supervisory control of discrete event systems and
researchers associated primarily with the areas of formal methods in control and reactive synthesis.
We believe this workshop is very timely given the emergence of autonomous systems, especially in the
context of cyber-physical systems, where stringent requirements of correctness and safety are placed on
the higher-level supervisory control layer. At the same time, it will give our community an opportunity to
reflect on seminal papers published 30 years ago that defined a new sub-discipline in control science and
- [RW1] P.J. Ramadge and W.M. Wonham, "Supervisory Control of a Class of Discrete Event Pro-
cesses," SIAM J. Control Optim., 25(1), 206-230.
- [RW2] W.M. Wonham and P.J. Ramadge, "On the Supremal Controllable Sublanguage of a Given
Language," SIAM J. Control Optim., 25(3), 637-659.
The speakers will give talks that will be accessible to a wide audience of CDC attendees. The
objective will be to combine the "retrospective" theme with the "future perspectives" theme,
in presentations that will have tutorial value.
Given the emergence of cyber-physical systems as an important area of research
and given that there are typically multiple CDC sessions on discrete event systems,
formal methods in control, and hybrid systems, we expect that a large number of
CDC attendees, in particular students, will be
interested in this workshop.
Stéphane Lafortune, Karen Rudie, Stavros Tripakis
Peter Ramadge, Calin Belta, Kai Cai, José Cury, Martin Fabian, Alessandro Giua,
Hervé Marchand, Richard Murray, Necmiye Ozay, George Pappas, Sanjit Seshia
Gain Scheduled Model Predictive Control System Design and Implementation
Model Predictive Control (MPC) has a long history in control engineering. It is one of the
few areas that has received on-going interest from researchers in both the industrial and
academic communities. Three major aspects of model predictive control make the design
methodology attractive. The first is the design formulation, which uses a completely
multivariable system framework where the performance parameters of the multivariable
control system are related to the engineering aspects of the system; hence, they can be
understood and "tuned" by engineers. The second aspect is the ability to handle "soft"
constraints and hard constraints in a multivariable control framework. This is particularly
attractive to industry where tight profit margins and limits on the process operation are
present. The third aspect is the ability to perform process on-line optimization.
MPC systems are designed using linear models unless a nonlinear model is explicitly
stated. Nonlinear MPC is conceptually similar to its linear counterpart except that
nonlinear models are deployed for the prediction and optimization. However, because of
its computational intensity and complexity, nonlinear MPC is not widely applied. Instead,
gain scheduled control system techniques have found success in the area of predictive
control of nonlinear plants. This one-day short-course will begin by introducing and
reinforcing the basic concepts in the design of an effective linear predictive controller.
This is followed by the design of a gain scheduled predictive controller: (i) linearization of
a nonlinear plant model according to operating conditions; (ii) design of linear predictive
controllers using the family of linear models; (iii) gain scheduled predictive control law
that will optimize a multiple model objective function with constraints and ensure smooth
transitions (i.e. bumpless transfer); (iv) simulation and experimental validation of the gain
scheduled MPC with constraints using MATLAB and Simulink as a platform.
The course is suitable for engineers, students and researchers who wish to gain basic
knowledge about linear and gain scheduled MPC of nonlinear plant, as well as understand
how to perform real time simulation and implementation using MATLAB and
Simulink tools. It is partially based on the speakers books' entitled "Model Predictive
Control System Design and Implementation Using MATLAB" (Wang, 2009), "Model
based predictive control: a practical approach" (Rossiter, 2003) [New edition in
preparation], and "PID and Predictive Control of Electrical Drives and Power Converters"
(Wang, et.al, 2015).
Organizers and speakers: Liuping Wang, J.A Rossiter,
Model-Based and Model-Guided Approaches for Automotive
and Maritime Powertrain Calibration and Control
Efficiency and emission associated with internal combustion powertrain systems have
been the focus of research efforts in both academic and industrial stakeholders due to more and
more stringent governmental regulations and public concerns about the environment and the
energy resources. Yet the complexities of internal combustion with both fossil and alternative fuels
pose significant challenges to the development of effective and efficient with satisfied performance.
In recent years, both model-based and data-driven optimization approaches have been explored to
address automotive and maritime powertrain combustion control and calibration in order to
squeeze the potential over the entire operation ranges. These are in addition to the traditionally
empirical calibration based method and also motivate the newly developed model-guided datadriven
optimization approach which essentially emerges as a process learning oriented design.
This full-day workshop intends to introduce the motivation and application of model-based and
model-guided data-driven optimization with sample studies in both automotive powertrain and
maritime platform calibration and control. In particular, discussion will be presented in detail on
the control-oriented modeling approach for automotive powertrain with initialization and
validation in real-time hardware-in-loop simulations, the concept of model-guided data-driven
optimization, as well as the model-based and model-guided calibration and control examples for
automotive powertrain and maritime platform, including control of mechatronic actuation
subsystem and internal combustion engine systems.
This workshop is designed to facilitate both control researchers from
academic communities and engineers from relevant industries to conduct model-based and modelguided
innovative design for powertrain systems. It is intended to share with audiences the
motivation, rationale, challenges, and achievements in the model-based and model-guided
powertrain (internal combustion) control approach. Those who are interested in the model-based
and model-data integrated approaches for control and optimization would also find the workshop
beneficial in terms of both concepts and applications.
Organizers: Guoming Zhu, Xiang Chen, Chris Manzie
Speakers: Guoming Zhu, Xiang Chen, Noam Olshina, Gokul Siva Sankar, Ying Tan
Deterministic and Stochastic Hybrid Methods for
Robust Learning and Optimization in Dynamical
The goal of this workshop is to present novel results in the area of deterministic and stochastic hybrid systems
applied to robust optimization and learning in dynamical systems. These include, but are not limited to, hybrid
extremum seeking control, event-triggered sampled-data optimization, stochastic learning in asynchronous sampleddata
games, robust set-based estimation, and event-triggered Q-learning optimal control. The workshop will present
constructive approaches for the design of novel robust hybrid algorithms for black-box and grey-box optimization,
as well as some engineering applications that motivate the study of hybrid optimization and learning.
After finishing the workshop attendees will be familiar with a broad class of hybrid dynamics in the area of
black-box and grey-box optimization, control, and estimation, as well as a basic understanding on how to design
and analyze some of these hybrid algorithms. The merits of working with hybrid dynamics will be clearly illustrated
throughout the workshop.
The workshop is intended to be a brief course on recent analysis and design tools for robust algorithms for modelfree
optimization and learning using deterministic and stochastic hybrid dynamics. It targets a broad audience in
academia and industry, including graduate students looking for an introduction to a new and active area of research;
control practitioners interested in novel design techniques and applications; and researchers in dynamical systems,
optimization, and control. The workshop audience is not expected to have any advanced background in hybrid
systems or optimization. A basic knowledge of linear/nonlinear system theory and probability theory is useful.
Organizers: Jorge I. Poveda, Andrew R. Teel, Mouhacine Benosman
Speakers: Jorge I. Poveda, Andrew R. Teel, Mouhacine Benosman,
Kyriakos Vamvoudakis, Chris Manzie, Martin Guay
What’s the big deal with Deep Learning?
Bringing together data-driven learning and feedback control
The last few years have seen multiple major successes of deep learning. From beating
professionals at games like Go, to fast detection of cancer, classification of complex images, and
generation of captions for images with incomplete information. In classification and
reinforcement learning, deep learning indeed has outperformed all existing machine learning
and model-based methods. Fueled by rapid advances in parallel computing and GPUs, new
impressive results are achieved almost on a daily basis by deep learning methods in a vast array
of applications from finance to self-driving cars. In the midst of this excitement however, there
are fundamental questions that remain unanswered: For example, recent results have also
shown that deep learning can be highly susceptible to noise and can misclassify images with
very high confidence that are very easy for humans to process. If deep learning is going to make
its way into self-driving cars and other autonomous systems, it is justifiable to ask just how
stable and robust can deep learning based control and decision making algorithms be? Indeed,
one could ask a even deeper question: What is the role of feedback in an increasingly
data-driven world? This we believe is a question that the controls community can significantly
contribute to answering. In the view of this vision, the objective of this workshop is to:
- Introduce the theory, algorithms, and implementation of Deep Learning to the
audience, including conducting tutorials on using deep learning software (Google Tensor
Flow). Embedded in this introduction, is a compact introduction to machine learning in
- Have a candid conversation about the connection of deep learning with the Neural
Network theory that has been developed in the controls community in the late 90s-early
2000s, and the larger controls efforts in system identification, adaptive control, and
- Bring together experts from deep learning and machine learning with control theory
- Hold a discussions on what critical roles feedback control and estimation play in a world
dominated by data-driven learning
The target audience for this workshop are control theorists and practicing controls engineers.
We will assume only familiarity with MATLAB and some familiarity with Python programming
language (we will circulate material beforehand to the registrants to prime them in Python).
Our presentations will be self-contained and leverage material that instructors have developed
in teaching courses on related topics UIUC or their organizations. In addition, we will provide a
repository of relevant links that help the audience navigate the wide variety of literature and
resources on the internet on deep learning.
Organizers and speakers:
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Key dates (2017)
|Submission Site Open:||January 4 |
|Initial Paper |
Submissions to L-CSS with CDC Option Due:
|Initial Paper |
|Workshop Proposals Due:||May 1|
|Paper and Workshop|
|Best Student Paper|
|Final Submission Open:||August 1|
|Registration Opens:||August 1|
|Best Student Paper|
|Accepted Papers Due:||September 20|
|Early Bird Closes:||October 1|
|Online Registration Closes:||December 5|
|Conference opens:||December 12|
|Conference closes:||December 15|
Click here to see the complete list of sponsors and exhibitors