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
Christopher
Laughman
Ankush
Chakrabarty
Daniel
Nikovski
Devesh
Jha
Arvind
Raghunathan
Diego
Romeres
Abraham
Goldsmith
Philip
Orlik
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.)
Kim
Yanting
Ma
Hongbo
Sun
Jeroen
van Baar
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 Ankush Chakrabarty gave an invited talk at University of Illinois at Chicago Date: April 9, 2021
MERL Contact: Ankush Chakrabarty
Research Areas: Control, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Ankush Chakrabarty, a Research Scientist at MERL's Multiphysical Systems (MS) Team, gave an invited talk on "Learning for Control and Estimation using Digital Twins" at the Department of Electrical and Computer Engineering Seminar Series organized at UIC. The talk proposed new learning-based control/estimation architectures that can utilize simulation data obtained from digital twins to add self-optimization and constraint-enforcement features to grey/black-box control systems.
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NEWS Karl Berntorp gave an invited lecture at the Department of Electrical Engineering at Linköping University Date: April 6, 2021
Where: Linköping University, Sweden
MERL Contact: Karl Berntorp
Research Areas: Control, Dynamical Systems, RoboticsBrief- MERL researcher Karl Berntorp was invited to give a lecture in the ELLIIT PhD course "Motion Planning and Control" at the Division of Vehicular Systems, Department of Electrical Engineering, Linköping University. The course is open for Ph.D. students as well as senior undergraduate students, and covers both fundamental algorithms and state-of-the-art methods for motion planning and control. The invited lecture described MERL research on the use of invariant sets for safe motion planning and control, with application to autonomous vehicles.
See All News & Events for Control -
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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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CA1530: Hybrid Control of Cyberphysical Systems
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of hybrid control algorithms for cyberphysical system. The potential subjects include formal methods for control synthesis, control barrier-functions, stabilizing control for hybrid dynamical systems, and optimal control of hybrid dynamics. The ideal candidate is expected to be working towards a PhD with strong emphasis in control theory, and to have interest and background in as many as possible among: predictive control, Lyapunov stability, formal methods for control, constrained control, optimization, and machine learning. 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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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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Recent Publications
- , "Online Bayesian Inference and Learning of Gaussian-Process State-SpaceModels", Automatica, DOI: https://doi.org/10.1016/j.automatica.2021.109613, Vol. 129, March 2021.BibTeX TR2021-026 PDF
- @article{Berntorp2021mar,
- author = {Berntorp, Karl},
- title = {Online Bayesian Inference and Learning of Gaussian-Process State-SpaceModels},
- journal = {Automatica},
- year = 2021,
- volume = 129,
- month = mar,
- doi = {https://doi.org/10.1016/j.automatica.2021.109613},
- issn = {0005-1098},
- url = {https://www.merl.com/publications/TR2021-026}
- }
- , "Robust Coordinated Hybrid Source Seeking with Obstacle Avoidance in Multi-Vehicle Autonomous Systems", IEEE Transactions on Automatic Control, DOI: 10.1109/TAC.2021.3056365, March 2021.BibTeX TR2021-022 PDF
- @article{Poveda2021mar,
- author = {Poveda, Jorge and Benosman, Mouhacine and Teel, Andrew R. and Sanfelice, Ricardo G.},
- title = {Robust Coordinated Hybrid Source Seeking with Obstacle Avoidance in Multi-Vehicle Autonomous Systems},
- journal = {IEEE Transactions on Automatic Control},
- year = 2021,
- month = mar,
- doi = {10.1109/TAC.2021.3056365},
- url = {https://www.merl.com/publications/TR2021-022}
- }
- , "Short Paper: Debt Representation in UTXO Blockchains", Financial Cryptography and Data Security, February 2021.BibTeX TR2021-014 PDF
- @inproceedings{Chiu2021feb,
- author = {Chiu, Michael and Kalabic, Uros},
- title = {Short Paper: Debt Representation in UTXO Blockchains},
- booktitle = {Financial Cryptography and Data Security},
- year = 2021,
- month = feb,
- url = {https://www.merl.com/publications/TR2021-014}
- }
- , "Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty", Advances in Neural Information Processing Systems (NeurIPS)-workshop, January 2021.BibTeX TR2021-009 PDF
- @inproceedings{Russel2021jan,
- author = {Russel, Reazul Hasan and Benosman, Mouhacine and van Baar, Jeroen},
- title = {Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)-workshop},
- year = 2021,
- month = jan,
- url = {https://www.merl.com/publications/TR2021-009}
- }
- , "Improving Passenger Comfort by Exploiting Hub Motors in Electric Vehicles: Suspension Modeling", ASME Dynamic Systems and Control Conference (DSCC), DOI: 10.1115/DSCC2020-3167, January 2021.BibTeX TR2021-019 PDF
- @inproceedings{Chen2021jan,
- author = {Chen, Di and Danielson, Claus and Masahiro, Iezawa},
- title = {Improving Passenger Comfort by Exploiting Hub Motors in Electric Vehicles: Suspension Modeling},
- booktitle = {ASME Dynamic Systems and Control Conference (DSCC)},
- year = 2021,
- month = jan,
- doi = {10.1115/DSCC2020-3167},
- url = {https://www.merl.com/publications/TR2021-019}
- }
- , "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}
- }
- , "Online Bayesian Inference and Learning of Gaussian-Process State-SpaceModels", Automatica, DOI: https://doi.org/10.1016/j.automatica.2021.109613, Vol. 129, March 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
