Control
If it moves, we control it.
Our expertise in this area covers multivariable, nonlinear, optimal and model-predictive control theory, nonlinear estimation, nonlinear dynamical systems, and mechanical design. We conduct both fundamental and applied research targeting a wide range of applications including autonomous driving, factory automation and HVAC systems.
Quick Links
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Researchers
Stefano
Di Cairano
Yebin
Wang
Karl
Berntorp
Scott
Bortoff
Mouhacine
Benosman
Avishai
Weiss
Uroš
Kalabić
Rien
Quirynen
Christopher
Laughman
Ankush
Chakrabarty
Daniel
Nikovski
Devesh
Jha
Arvind
Raghunathan
Abraham
Goldsmith
Philip
Orlik
Diego
Romeres
Jianlin
Guo
Matthew
Brand
Saleh
Nabi
Hongtao
Qiao
Koon Hoo
Teo
Toshiaki
Koike-Akino
Chungwei
Lin
William
Yerazunis
Marcel
Menner
Alan
Sullivan
Jinyun
Zhang
Petros
Boufounos
Kyeong Jin
(K.J.)
KimYanting
Ma
Hongbo
Sun
Abraham
P. Vinod
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Awards
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AWARD Best Student Paper Award at the IEEE Conference on Control Technology and Applications Date: August 26, 2020
Awarded to: Marcus Greiff, Anders Robertsson, Karl Berntorp
MERL Contact: Karl Berntorp
Research Areas: Control, Signal ProcessingBrief- Marcus Greiff, a former MERL intern from the Department of Automatic Control, Lund University, Sweden, won one of three 2020 CCTA Outstanding Student Paper Awards and the Best Student Paper Award at the 2020 IEEE Conference on Control Technology and Applications. The research leading up to the awarded paper titled 'MSE-Optimal Measurement Dimension Reduction in Gaussian Filtering', concerned how to select a reduced set of measurements in estimation applications while minimally degrading performance, was done in collaboration with Karl Berntorp at MERL.
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AWARD MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019 Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief- MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
See All Awards for MERL -
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News & Events
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EVENT MERL Virtual Open House 2020 Date & Time: Wednesday, December 9, 2020; 1:00-5:00PM EST
MERL Contacts: Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
Location: Virtual
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & AudioBrief- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
Registration: https://mailchi.mp/merl/merl-virtual-open-house-2020
Schedule: https://www.merl.com/events/voh20
Current internship and employment openings:
https://www.merl.com/internship/openings
https://www.merl.com/employment/employment
Information about working at MERL:
https://www.merl.com/employment
- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
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NEWS Karl Berntorp gave an invited lecture at the Department of Electrical Engineering at Linkoping University Date: October 8, 2020
Where: Linkoping University
MERL Contact: Karl Berntorp
Research Areas: Control, Dynamical Systems, Robotics, Signal ProcessingBrief- MERL researcher Karl Berntorp was invited to give a lecture in the class "Autonomous vehicles – planning, control, and learning systems" at the Division of Vehicular Systems, Department of Electrical Engineering, Linkoping University. The course is for the engineering-program students at Linkoping University and gives a basic understanding of the available models, methods, and software libraries to work on autonomous vehicles, with particular focus on motion-planning and control methods. The invited lecture described the different system components and design of motion planning and predictive control methods targeted to autonomous driving.
See All News & Events for Control -
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Internships
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MD1564: Data-driven fluid mechanics and control
The Muti-Physics and Dynamics (MD) group at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2021. The ideal candidate will be a senior Ph.D. student specializing in fluid mechanics, control, turbulence modeling, reduced-order modeling, and non-convex optimization. Research experience in computational fluid dynamics (CFD), data-assimilations, continuous and discreet adjoint methods is highly desirable. Familiarity with computational programming languages like Python, Fortran or C++ (openFOAM level) is expected. Publication of results obtained during the internship is expected. The starting date is flexible between April-June 2021, and the internship will last 3-4 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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CA1520: Autonomous Vehicles: Perception, Planning, and Control
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of algorithms for planning and control of autonomous vehicles. The potential subjects include high level decision making using formal methods and set-based control, coordination or perception and control strategies to improve environment knowledge while achieving a goal, and distributed control for multi-vehicle systems. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible among: motion planning, predictive control, perception and object detection optimization, machine learning for vehicle prediction, autonomous vehicles. Good programming skills in MATLAB, Python or C/C++ are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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CA1521: Coordinated Perception and Control for Autonomous Systems
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of algorithms for coordinating control and perception in autonomous systems. The overall objective is to determine the sensing strategy together with the motion/control strategy to effectively achieve a control goal while managing the risk due to the environment uncertainty. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible among: predictive control, stochastic tubes, scenario-based stochastic optimization, uncertainty and risk representation, machine learning and motion planning algorithms. Good programming skills in MATLAB and/or Python, are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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Recent Publications
- "Abort-Safe Spacecraft Rendezvous in case of Partial Thrust Failure", IEEE Conference on Decision and Control (CDC), December 2020.BibTeX TR2020-175 PDF
- @inproceedings{AguilarMarsillach2020dec,
- author = {Aguilar Marsillach, Daniel and Di Cairano, Stefano and Weiss, Avishai},
- title = {Abort-Safe Spacecraft Rendezvous in case of Partial Thrust Failure},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-175}
- }
, - "Model-based Policy Search for Partially Measurable Systems", Advances in Neural Information Processing Systems (NeurIPS), December 2020.BibTeX TR2020-174 PDF
- @inproceedings{Romeres2020dec2,
- author = {Romeres, Diego and Amadio, Fabio and Dalla Libera, Alberto and Nikovski, Daniel N. and Carli, Ruggero},
- title = {Model-based Policy Search for Partially Measurable Systems},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-174}
- }
, - "Feedback Linearization Robot Control based on Gaussian Process Inverse Dynamics Model", Conferenza Italiana di Robotica e Macchine Intelligenti, December 2020.BibTeX TR2020-173 PDF
- @inproceedings{Romeres2020dec,
- author = {Romeres, Diego and Dalla Libera, Alberto and Amadio, Fabio and Carli, Ruggero},
- title = {Feedback Linearization Robot Control based on Gaussian Process Inverse Dynamics Model},
- booktitle = {Conferenza Italiana di Robotica e Macchine Intelligenti},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-173}
- }
, - "Modelica-Based Control of A Delta Robot", ASME Dynamic Systems and Control Conference, December 2020.BibTeX TR2020-154 PDF
- @inproceedings{Bortoff2020dec,
- author = {Bortoff, Scott A. and Okasha, Ahmed},
- title = {Modelica-Based Control of A Delta Robot},
- booktitle = {ASME Dynamic Systems and Control Conference},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-154}
- }
, - "Exploiting linear substructure in linear regression Kalman filters", IEEE Annual Conference on Decision and Control (CDC), December 2020.BibTeX TR2020-171 PDF
- @inproceedings{Greiff2020dec,
- author = {Greiff, Marcus and Robertsson, Anders and Berntorp, Karl},
- title = {Exploiting linear substructure in linear regression Kalman filters},
- booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-171}
- }
, - "Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving", IEEE Conference on Decision and Control (CDC), December 2020.BibTeX TR2020-168 PDF
- @inproceedings{Ahn2020dec2,
- author = {Ahn, Heejin and Berntorp, Karl and Di Cairano, Stefano},
- title = {Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-168}
- }
, - "Reachability-based Decision Making for Autonomous Driving: Theory and Experiment", IEEE Transactions on Control Systems Technology, December 2020.BibTeX TR2020-165 PDF
- @article{Ahn2020dec,
- author = {Ahn, Heejin and Berntorp, Karl and Inani, Pranav and Ram, Arjun Jagdish and Di Cairano, Stefano},
- title = {Reachability-based Decision Making for Autonomous Driving: Theory and Experiment},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-165}
- }
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- "Abort-Safe Spacecraft Rendezvous in case of Partial Thrust Failure", IEEE Conference on Decision and Control (CDC), December 2020.
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Videos
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Co-simulation of HVAC Equipment and Airflow
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Modelica-Based Modeling and Control of a Delta Robot
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Electric Satellite Station Keeping, Attitude Control, and Momentum Management by MPC
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Towards Human-Level Learning of Complex Physical Puzzles
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Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving
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Experimental Validation of Reachability-based Decision Making for Autonomous Driving
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MPC control and particle filter-based planning demonstration using mini-cars
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Particle filter-based planning demonstration using mini-cars
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HVAC Lab & Controls
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Fly Cut
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MPC for Laser Cutting
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Car Path Planning
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MPC for Satellites
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MERL Research on Autonomous Vehicles
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Software Downloads