PROJECTS IN THE SYSTEMS AND CONTROL ENGINEERING (SYSCON) DEPARTMENT
Robust optimization for machine learning
Professor: Debasish Chatterjee
UID: SC01
Novel techniques in model predictive control (MPC)
Professor: Debasish Chatterjee
UID: SC02
Introduction to quantum control
Professor: Debasish Chatterjee
UID: SC03
Shape sensors for flexible structures
Professor: Vivek Natarajan
UID: SC04
Modelling and simulation of slender aircraft wings
Professor: Vivek Natarajan
UID: SC05
SC01
Description: This project centers around an entirely
novel approach to robust optimization in the “convex” regime.
More specifically, we will explore specific applications of
convex semi-infinite programs, via a newly developed “targeted
sampling technique”, in machine learning, estimation and
statistics, portfolio optimization, etc. Students in their third
year of their UG studies and equipped with a deep sense of
curiosity are encouraged to apply. Efforts will be split equally
between learning new theory and developing numerical tools for
the aforementioned applications, and there is a strong
possibility of filing patents in each case.
Number of students: 2
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria:
Prerequisites: Solid background in optimization and/or
probability.
Duration: 3 months, extendable up to 3 years
Learning outcome: Patents; introduction to several
entirely novel techniques and applications of robust
optimization.
Weekly time commitment: At least 20 during vacation, 10
during the semester
Instructions for assignment: The key and novel idea is
contained in the indicated paper. Specific software/theoretical
developments will have to be made for each application.
SC02
Description:We have developed an entirely novel technique
for MPC in the robust linear, nonlinear, and stochastic regimes.
A large number of ideas from diverse disciplines are brought to
bear on the difficult and challenging problem of explicit MPC;
we need students to study and absorb them, develop the technique
for their own specific applications, write papers and/or apply
for patents. Applications range from chemical process control,
aerospace applications involving rigid bodies, control of
self-driving cars, control of flexible structures, control of
battery banks, control of gas turbines, dynamic portfolio
optimization, etc.
Number of students: 3
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 8.5
Prerequisites: Background in optimization.
Duration: 3 months, extendable up to 3 years.
Learning outcome: Patents in specific cases; introduction
to a novel technique in the important and applicable area of
model predictive control (MPC).
Weekly time commitment: At least 20 during the vacation,
at least 5 during the semester.
General expectations: Sincerety.
Assignment: Several patent applications have been made,
others are in the pipeline; there is very little documentation
about this technique in the public domain and it will remain
this way for some time. Interested students may contact my
doctoral student Siddhartha Ganguly
(https://sites.google.com/view/siddhartha-ganguly) to get a
brief idea about the technique involved here, and I'll talk to
you during the selection process.
Instructions for assignment:
SC03
Description: Several advanced applications such as MRI,
NMR, etc., are at their heart, dependent on the control of
quantum mechanical systems. This project is about learning the
basic ideas in the emerging and extremely interesting domain of
quantum control. It will involve learning differential geometry
and Lie groups, and also various ideas related to optimization
and optimal control on manifolds.
Number of students: 2
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 8.5
Prerequisites: Multivariable calculus and basics of
optimization.
Duration: 3 months through 3 years
Learning outcome: Introduction to the fascinating area of
quantum control.
Weekly time commitment: At least 20 during vacations and
at least 5 during the semester.
Instructions for assignment: The two chapters indicated
above are from my colleague Prof. Navin Khaneja's lecture notes.
We will try to cover them during the first 3 months and then
move to other advanced topics.
SC04
Description: Flexible structures are used in many
engineering applications. Sensors that can accurately measure
the shape of these structures are important both for feedback
control and for monitoring purposes. The goal of this project is
to survey the existing technologies in the literature for
constructing shape sensors and then to analyse the feasibility
of making such sensors in-house. Depending on the progress, we
may also start fabricating these sensors in the lab.
Number of students: 1
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 7
Prerequisites: None, but some basic understanding of
instrumentation will be useful.
Duration: The students should be able to complete the
main part of the project in summer. Depending on the progress
and their interest, they can continue working on it during the
semester.
Learning outcome: In addition to learning about some of
the existing sensor technologies, students will learn how to
perform literature survey and feasibility study.
Weekly time commitment:10 hours
General expectations:
Assignment:
Instructions for assignment:
SC05
Description: Suppression of flutter in wings is a problem
of interest in aircraft design. The goal of this project is to
understand existing models for the dynamics of slender aircraft
wings, improve these models by using ideas from beam theory and
perform numerical simulations.
Number of students: 1
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 7
Prerequisites: None, but familiarity with flexible
structures and/or FEM is desirable.
Duration: The students should be able to complete the
main part of the project in summer. Depending on the progress
and their interest, they can continue working on it during the
semester.
Learning outcome: In addition to learning about modelling
of slender structures, students will learn about numerical
evaluation and comparison of different models.