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
Norihiro
Nishiuma
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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CA1519: Estimation for High-Precision Positioning
MERL is seeking a highly motivated candidate for development of next-generation high-precision positioning methods for autonomous systems applications, e.g., autonomous driving. The candidate will work with the Control for Autonomy team and the Signal Processing group in developing satellite-based positioning methods using information from multiple sources. Previous experience with at least some of the Bayesian inference, distributed estimation, satellite navigation systems, is highly desirable. Solid knowledge in MATLAB is required, working experience in C/C++ is desired, and previous experience with satellite navigation packages such as RTKLib is a merit. PhD candidates meeting the above requirements are encouraged to apply. The expected duration of the internship is 3-6 months with flexible start date. 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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DA1602: Reinforcement Learning for HVAC systems
MERL is looking for a self-motivated and qualified candidate to work on air flow control of Heating, Ventilation and Air Conditioning (HVAC) systems. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas: fluid dynamics, control theory, reinforcement learning, familiarity with partial differential equations. Proficiency in Python and Matlab is required. The successful candidate will be expected to develop, in collaboration with MERL employees, a state of the art algorithms for air flow control that will lead to a scientific publication. Typical internship length is 3 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.
See All Internships for Control -
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Recent Publications
- "Model Predictive Control Approach for Autonomous Sun-Synchronous Sub-Recurrent Orbit Control", AIAA SciTech, January 2021.BibTeX TR2021-005 PDF
- @inproceedings{Hayashi2021jan,
- author = {Hayashi, Naohiro and Weiss, Avishai and Di Cairano, Stefano},
- title = {Model Predictive Control Approach for Autonomous Sun-Synchronous Sub-Recurrent Orbit Control},
- booktitle = {AIAA SciTech},
- year = 2021,
- month = jan,
- url = {https://www.merl.com/publications/TR2021-005}
- }
, - "Data-Driven Robust State Estimation for Reduced-Order Models of 2D Boussinesq Equations with Parametric Uncertainties", Journal of Computers and Fluids, December 2020.BibTeX TR2020-177 PDF
- @article{Benosman2020dec,
- author = {Benosman, Mouhacine and Borggaard, Jeff},
- title = {Data-Driven Robust State Estimation for Reduced-Order Models of 2D Boussinesq Equations with Parametric Uncertainties},
- journal = {Journal of Computers and Fluids},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-177}
- }
, - "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}
- }
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- "Model Predictive Control Approach for Autonomous Sun-Synchronous Sub-Recurrent Orbit Control", AIAA SciTech, January 2021.
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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