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
Mouhacine
Benosman
Scott A.
Bortoff
Avishai
Weiss
Uroš
Kalabić
Rien
Quirynen
Ankush
Chakrabarty
Daniel N.
Nikovski
Christopher R.
Laughman
Diego
Romeres
Devesh K.
Jha
Arvind
Raghunathan
Abraham M.
Goldsmith
Saleh
Nabi
Philip V.
Orlik
Jianlin
Guo
Matthew E.
Brand
Chungwei
Lin
Hongtao
Qiao
Koon Hoo
Teo
Toshiaki
Koike-Akino
Marcel
Menner
Alan
Sullivan
William S.
Yerazunis
Kyeong Jin
(K.J.)
Kim
Yanting
Ma
Jinyun
Zhang
Petros T.
Boufounos
Rui
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 K. 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.
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News & Events
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NEWS Diego Romeres appointed as Associate Editor at ICRA 2022. Date: September 17, 2021 - October 31, 2021
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Optimization, RoboticsBrief- Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, is serving as an Associate Editor (AE) for the IEEE International Conference on Robotics and Automation (ICRA) 2022.
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NEWS Anthony Vetro delivers keynote on robotic manipulation at inaugural IEEE Conference on Autonomous Systems Date: August 12, 2021
MERL Contact: Anthony Vetro
Research Areas: Artificial Intelligence, Computer Vision, Control, Dynamical Systems, Machine Learning, Optimization, RoboticsBrief- Anthony Vetro gave a keynote at the inaugural IEEE Conference on Autonomous Systems (ICAS), which was held virtually from August 11-13, 2021. The talk focused on challenges and recent progress in the area of robotic manipulation. The conference is sponsored by IEEE Signal Processing Society (SPS) through the SPS Autonomous Systems Initiative.
Abstract: Human-level manipulation continues to be beyond the capabilities of today’s robotic systems. Not only do current industrial robots require significant time to program a specific task, but they lack the flexibility to generalize to other tasks and be robust to changes in the environment. While collaborative robots help to reduce programming effort and improve the user interface, they still fall short on generalization and robustness. This talk will highlight recent advances in a number of key areas to improve the manipulation capabilities of autonomous robots, including methods to accurately model the dynamics of the robot and contact forces, sensors and signal processing algorithms to provide improved perception, optimization-based decision-making and control techniques, as well as new methods of interactivity to accelerate and enhance robot learning.
- Anthony Vetro gave a keynote at the inaugural IEEE Conference on Autonomous Systems (ICAS), which was held virtually from August 11-13, 2021. The talk focused on challenges and recent progress in the area of robotic manipulation. The conference is sponsored by IEEE Signal Processing Society (SPS) through the SPS Autonomous Systems Initiative.
See All News & Events for Control -
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Internships
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MD1694: Path Planning for Articulated Vehicles
MERL is seeking a highly skilled and self-motivated intern to work on path/motion planning of articulated vehicles.
The ideal candidate should have solid backgrounds in path/motion planning, nonlinear geometric control theory, and machine learning. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, electrical engineering, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 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. -
CA1695: Spacecraft Attitude Control
MERL is seeking a highly motivated intern for a research position in spacecraft attitude control. The ideal candidate has experience in attitude kinematics and dynamics, computational fluid dynamics (CFD) using OpenFOAM, programming in C++, optimization, and control of rigid bodies and PDEs. Experience in multi-phase flow modeling and volume-of-fluid approach with an emphasis on liquid-gas systems is highly desirable. PhD students in aerospace, mechanical, or electrical engineering are encouraged to apply. Publication of results produced during the internship is expected. The duration of the internship is 3-6 months, and the start date is flexible. 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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MD1300: Compiler Optimizations for Linear Algebra Kernels
MERL is looking for a highly motivated individual to work on automatic, compiler based techniques for optimizing linear algebra kernels. The ideal candidate is a Ph.D. student in computer science with extensive experience in compiler design and source code optimization techniques. In particular, the successful candidate will have a strong working knowledge of polyhedral optimization techniques, the LLVM compiler, and Polly. Strong C/C++ skills and knowledge of LLVM at the source level are required. Publication of results in conference proceedings and journals is expected. The expected duration of the internship is 3 months and the start date is flexible.
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Openings
See All Openings at MERL -
Recent Publications
- , "Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation", Automatica.it, September 2021.BibTeX TR2021-108 PDF
- @inproceedings{Amadio2021sep,
- author = {Amadio, Fabio and Dalla Libera, Alberto and Carli, Ruggero and Romeres, Diego},
- title = {Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation},
- booktitle = {Automatica.it},
- year = 2021,
- month = sep,
- url = {https://www.merl.com/publications/TR2021-108}
- }
- , "Airflow Optimization for Room Air Conditioners", Building Simulation 2021 Conference, September 2021.BibTeX TR2021-106 PDF
- @inproceedings{Tanaka2021sep,
- author = {Tanaka, Ryuta and Nabi, Saleh and Nonaka, Mio},
- title = {Airflow Optimization for Room Air Conditioners},
- booktitle = {Building Simulation 2021 Conference},
- year = 2021,
- month = sep,
- url = {https://www.merl.com/publications/TR2021-106}
- }
- , "Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization", Automatica, August 2021.BibTeX TR2021-101 PDF
- @article{Chakrabarty2021aug,
- author = {Chakrabarty, Ankush and Benosman, Mouhacine},
- title = {Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization},
- journal = {Automatica},
- year = 2021,
- month = aug,
- url = {https://www.merl.com/publications/TR2021-101}
- }
- , "Mixed-integer Programming for Centralized Coordination of Connected and Automated Vehicles in Dynamic Environment", IEEE Conference on Control Technology and Applications (CCTA), August 2021.BibTeX TR2021-089 PDF
- @inproceedings{Ravikumar2021aug,
- author = {Ravikumar, Shreejith and Quirynen, Rien and Bhagat, Akshay and Zeino, Eyad and Di Cairano, Stefano},
- title = {Mixed-integer Programming for Centralized Coordination of Connected and Automated Vehicles in Dynamic Environment},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2021,
- month = aug,
- url = {https://www.merl.com/publications/TR2021-089}
- }
- , "Mixed-Integer Linear Regression Kalman Filters for GNSS Positioning", IEEE Conference on Control Technology and Applications (CCTA), August 2021.BibTeX TR2021-090 PDF
- @inproceedings{Greiff2021aug,
- author = {Greiff, Marcus and Berntorp, Karl and Di Cairano, Stefano and Kim, Kyeong Jin},
- title = {Mixed-Integer Linear Regression Kalman Filters for GNSS Positioning},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2021,
- month = aug,
- url = {https://www.merl.com/publications/TR2021-090}
- }
- , "A Kalman Filter for Online Calibration of Optimal Controllers", IEEE Conference on Control Technology and Applications (CCTA), August 2021.BibTeX TR2021-091 PDF
- @inproceedings{Menner2021aug,
- author = {Menner, Marcel and Berntorp, Karl and Di Cairano, Stefano},
- title = {A Kalman Filter for Online Calibration of Optimal Controllers},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2021,
- month = aug,
- url = {https://www.merl.com/publications/TR2021-091}
- }
- , "Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC", IFAC Conference on Nonlinear Model Predictive Control, July 2021.BibTeX TR2021-084 PDF
- @inproceedings{Quirynen2021jul,
- author = {Quirynen, Rien and Berntorp, Karl},
- title = {Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC},
- booktitle = {IFAC Conference on Nonlinear Model Predictive Control},
- year = 2021,
- month = jul,
- url = {https://www.merl.com/publications/TR2021-084}
- }
- , "Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models", European Control Conference (ECC), June 2021.BibTeX TR2021-068 PDF
- @inproceedings{DallaLibera2021jun,
- author = {Dalla Libera, Alberto and Amadio, Fabio and Nikovski, Daniel N. and Carli, Ruggero and Romeres, Diego},
- title = {Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models},
- booktitle = {European Control Conference (ECC)},
- year = 2021,
- month = jun,
- url = {https://www.merl.com/publications/TR2021-068}
- }
- , "Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation", Automatica.it, September 2021.
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Videos
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Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
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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
