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
Daniel
Nikovski
Ankush
Chakrabarty
Christopher
Laughman
Devesh
Jha
Diego
Romeres
Arvind
Raghunathan
Abraham
Goldsmith
Philip
Orlik
Jianlin
Guo
Saleh
Nabi
Matthew
Brand
Chungwei
Lin
Hongtao
Qiao
Koon Hoo
Teo
Toshiaki
Koike-Akino
Alan
Sullivan
William
Yerazunis
Yanting
Ma
Marcel
Menner
Jeroen
van Baar
Jinyun
Zhang
Petros
Boufounos
Kyeong Jin
(K.J.)
Kim
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 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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NEWS Karl Berntorp gave an invited lecture at University of Houston Date: April 22, 2021
Where: Houston, Texas
MERL Contact: Karl Berntorp
Research Areas: Control, Dynamical Systems, Robotics, Signal ProcessingBrief- The invited seminar "System Design, Planning, and Control for Autonomous Driving" was part of the Distinguished Seminar series at the Department of Mechanical Engineering at the University of Houston, Houston, Tx. The invited lecture described MERL research related to the different system components involved in autonomous driving, with particular focus on motion-planning and predictive-control methods.
See All News & Events for Control -
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Internships
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CA1654: Learning for Connected Vehicles
MERL is seeking a highly motivated intern to collaborate with the Control for Autonomy team in the development of learning technologies for Connected Vehicles. The intern will conduct research in the development of methods for learning/optimization of Advanced Driver Assistance Systems (ADAS) using data-sharing between connected vehicles and/or infrastructure. The ideal candidate has knowledge of at least one of machine learning, estimation, connected vehicles, and vehicle control systems. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. Good programming skills in Matlab are required and knowledge in Python or C/C++ is a merit. PhD students in engineering or mathematics are encouraged to apply. The expected duration of the internship is 3-6 months. The start date is flexible (ideally in Fall 2021). This internship is preferred to be onsite at MERL, but may be done remotely if preferred by the candidate.
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CA1565: Connected Vehicle Driver Assistance Systems
MERL is seeking a highly motivated qualified intern to collaborate with the Control for Autonomy team and the Signal Processing group in the development of Advanced Driver Assistance Systems (ADAS) for Connected Vehicles. The intern will collaborate in the development of methods for distributed learning and optimization of ADAS using data-sharing between connected vehicles and infrastructure. The ideal candidate has knowledge of machine learning, optimization and connected vehicles. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. The expected duration of the internship is 3-6 months, starting in Spring or Summer 2021, but later starting periods may also be considered. 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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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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Recent Publications
- , "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}
- }
- , "Robust Extended Kalman Filtering for Systems with Measurement Outliers", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2021.3077535, May 2021.BibTeX TR2021-078 PDF
- @article{Fang2021may,
- author = {Fang, Huazhen and Haile, Mulugeta and Wang, Yebin},
- title = {Robust Extended Kalman Filtering for Systems with Measurement Outliers},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2021,
- month = may,
- doi = {10.1109/TCST.2021.3077535},
- url = {https://www.merl.com/publications/TR2021-078}
- }
- , "Long-Horizon Motion Planning for Autonomous Vehicle Parking Incorporating Incomplete Map Information", IEEE International Conference on Robotics and Automation (ICRA), May 2021.BibTeX TR2021-062 PDF
- @inproceedings{Dai2021may,
- author = {Dai, Siyu and Wang, Yebin},
- title = {Long-Horizon Motion Planning for Autonomous Vehicle Parking Incorporating Incomplete Map Information},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2021,
- month = may,
- url = {https://www.merl.com/publications/TR2021-062}
- }
- , "Fail-Safe Spacecraft Rendezvous on Near-Rectilinear Halo Orbits", American Control Conference (ACC), May 2021.BibTeX TR2021-054 PDF
- @inproceedings{AguilarMarsillach2021may,
- author = {Aguilar Marsillach, Daniel and Di Cairano, Stefano and Kalabic, Uros and Weiss, Avishai},
- title = {Fail-Safe Spacecraft Rendezvous on Near-Rectilinear Halo Orbits},
- booktitle = {American Control Conference (ACC)},
- year = 2021,
- month = may,
- url = {https://www.merl.com/publications/TR2021-054}
- }
- , "Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter", American Control Conference (ACC), May 2021.BibTeX TR2021-058 PDF
- @inproceedings{Berntorp2021may,
- author = {Berntorp, Karl and Chakrabarty, Ankush and Di Cairano, Stefano},
- title = {Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter},
- booktitle = {American Control Conference (ACC)},
- year = 2021,
- month = may,
- url = {https://www.merl.com/publications/TR2021-058}
- }
- , "Joint Tire-Stiffness and Vehicle-Inertial Parameter Estimation for Improved Predictive Control", American Control Conference, May 2021.BibTeX TR2021-060 PDF
- @inproceedings{Berntorp2021may2,
- author = {Berntorp, Karl and Quirynen, Rien and Vaskov, Sean},
- title = {Joint Tire-Stiffness and Vehicle-Inertial Parameter Estimation for Improved Predictive Control},
- booktitle = {American Control Conference},
- year = 2021,
- month = may,
- url = {https://www.merl.com/publications/TR2021-060}
- }
- , "Perception-Aware Chance-Constrained Model Predictive Control for Uncertain Environments", American Control Conference (ACC), May 2021.BibTeX TR2021-055 PDF
- @inproceedings{Bonzanini2021may,
- author = {Bonzanini, Angelo Domenico and Mesbah, Ali and Di Cairano, Stefano},
- title = {Perception-Aware Chance-Constrained Model Predictive Control for Uncertain Environments},
- booktitle = {American Control Conference (ACC)},
- year = 2021,
- month = may,
- url = {https://www.merl.com/publications/TR2021-055}
- }
- , "Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC", IFAC Conference on Nonlinear Model Predictive Control, July 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
