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
Bortoff
Avishai
Weiss
Uroš
Kalabić
Rien
Quirynen
Ankush
Chakrabarty
Daniel
Nikovski
Christopher
Laughman
Diego
Romeres
Devesh
Jha
Arvind
Raghunathan
Abraham
Goldsmith
Saleh
Nabi
Philip
Orlik
Jianlin
Guo
Matthew
Brand
Chungwei
Lin
Hongtao
Qiao
Koon Hoo
Teo
Toshiaki
Koike-Akino
Marcel
Menner
Alan
Sullivan
William
Yerazunis
Kyeong Jin
(K.J.)
Kim
Yanting
Ma
Jeroen
van Baar
Jinyun
Zhang
Petros
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 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 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.
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NEWS Rien Quirynen and Karl Berntorp to deliver keynote at IFAC NMPC 2021 Conference Date: July 12, 2021
MERL Contacts: Karl Berntorp; Rien Quirynen
Research Areas: Control, Dynamical Systems, OptimizationBrief- MERL researcher Rien Quirynen will present work in collaboration with Karl Berntorp on "Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic Nonlinear MPC" as a keynote speaker at the 7th IFAC Conference on Nonlinear Model Predictive Control 2021 on July, 12th. The paper is 1 out of 5 keynote presentations chosen among more than 50 accepted papers at the conference. An abstract of the talk can be found in the link below.
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Internships
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MD1377: Adaptive Optimal Control of Electrical Machines
MERL is seeking a motivated and qualified individual to conduct research in control of electrical machines. The ideal candidate should have solid backgrounds in adaptive dynamic programming and state/parameter estimation for electrical machines, demonstrated capability to publish results in leading conferences/journals, and experience with real-time control experiments involving high power devices. Senior Ph.D. students are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.
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CA1531: Learning-based multi-agent motion planning
MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. 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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CA1646: Path Planning and Model Predictive Control for Autonomous Vehicles
MERL is seeking highly motivated and qualified interns to collaborate on the implementation and experimental validation of algorithms for path/motion planning and optimization-based tracking control in autonomous vehicles. An ideal candidate should have experience in path planning and/or model predictive control (MPC) for autonomous vehicles, and the candidate should be familiar with Matlab and Simulink. Any experience with dSPACE (e.g., MicroAutoBox) or C/C++ code generation is a plus. Both MS and PhD students are welcome to apply. Start date for this internship is as soon as possible, 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.
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Openings
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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
