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
Rien
Quirynen
Ankush
Chakrabarty
Christopher R.
Laughman
Daniel N.
Nikovski
Diego
Romeres
Devesh K.
Jha
Arvind
Raghunathan
Marcel
Menner
Philip V.
Orlik
Marcus
Greiff
Jianlin
Guo
Abraham
Goldsmith
Chungwei
Lin
Matthew
Brand
Toshiaki
Koike-Akino
Hongtao
Qiao
Koon Hoo
Teo
William S.
Yerazunis
Yanting
Ma
Abraham P.
Vinod
Jinyun
Zhang
Petros T.
Boufounos
Vedang M.
Deshpande
Kei
Ota
Kieran
Parsons
Hongbo
Sun
Anantaram
Varatharajan
Jing
Zhang
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Awards
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AWARD Arvind Raghunathan receives Roberto Tempo Best CDC Paper Award at 2022 IEEE Conference on Decision & Control (CDC) Date: December 8, 2022
Awarded to: Arvind Raghunathan
MERL Contact: Arvind Raghunathan
Research Areas: Control, OptimizationBrief- Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).
The award is given annually in honor of Roberto Tempo, the 44th President of the IEEE Control Systems Society (CSS). The Tempo Award Committee selects the best paper from the previous year's CDC based on originality, potential impact on any aspect of control theory, technology, or implementation, and for the clarity of writing. This year's award committee was headed by Prof. Patrizio Colaneri, Politecnico di Milano. Arvind's paper was nominated for the award by Prof. Lorenz Biegler, Carnegie Mellon University, with supporting letters from Prof. Andreas Waechter, Northwestern University, and Prof. Victor Zavala, University of Wisconsin-Madison.
- Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).
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AWARD Marcus Greiff receives Outstanding Student Paper Award at CCTA 2022 Date: August 25, 2022
Awarded to: Marcus Greiff
MERL Contact: Marcus Greiff
Research Areas: Control, Dynamical Systems, RoboticsBrief- Marcus Greiff, a Visiting Research Scientist at MERL, was awarded one of three outstanding student paper awards at the IEEE CCTA 2022 conference for his paper titled "Quadrotor Control on SU(2)xR3 with SLAM Integration". The award was given for originality, clarity, and potential impact on practical applications of control. The work presents a complete UAV control system design, facilitating autonomous supermarket inventorying without the need for external motion capture systems. A video of the experiments is on YouTube, including both simulations and real-time examples.
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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 Contacts: Karl Berntorp; Marcus Greiff
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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News & Events
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NEWS Rien Quirynen Appointed IPC Vice-Chair for the 8th IFAC Conference on NMPC 2024 Date: August 27, 2024 - August 30, 2024
Where: Kyoto, Japan
MERL Contact: Rien Quirynen
Research Areas: Control, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researcher Rien Quirynen has been appointed as Vice-Chair from Industry of the International Program Committee of the 8th IFAC Conference on Nonlinear Model Predictive Control, which will be held in Kyoto, Japan, in August 2024.
IFAC NMPC is the main symposium focused on model predictive control, theory, methods and applications, includes contributions on control, optimization, and machine learning research, and is held every 3 years.
- MERL researcher Rien Quirynen has been appointed as Vice-Chair from Industry of the International Program Committee of the 8th IFAC Conference on Nonlinear Model Predictive Control, which will be held in Kyoto, Japan, in August 2024.
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NEWS Chris Laughman delivered two seminar talks for at the School of Engineering at Penn State Date: February 16, 2023 - February 17, 2023
Where: Pennsylvania State University
MERL Contact: Christopher R. Laughman
Research Areas: Control, Machine Learning, Multi-Physical ModelingBrief- On February 16 and 17, Chris Laughman, Senior Team Leader of the Multiphysical Systems Team, presented lectures for the Systems, Robotics, and Controls Seminar Series in the School of Engineering, and for the Distinguished Speaker Series in Architectural Engineering. His talk was titled "Architectural Thermofluid Systems: Next-Generation Challenges and Opportunities," and described characteristics of these systems that require specific attention in model-based system engineering processes, as well as MERL research to address these challenges.
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Internships
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MS2012: Residual Model Learning for Building Energy Systems
MERL is looking for a highly motivated and qualified candidate to work on learning residual dynamics to augment ODE/DAE-based models of building energy systems. The ideal candidate will have a strong understanding of system identification, optimization, machine learning and/or function approximation; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred; experience with Modelica/FMUs is a plus. PhD students are strongly preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible.
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MD1887: Optimization and control of xEV and electric aircraft
MERL is seeking a motivated and qualified individual to conduct research in modeling, control, simulation and analysis of electric system involved in xEV and electric aircraft. The ideal candidate should have solid backgrounds in some of the following areas: modeling, control, and simulation of electrical systems (including generators, motors, power electronics and batteries), aerodynamics, mission analysis, flight dynamics, and multi-disciplinary system design optimization. Demonstrated experience in software/language such as Modelica or Matlab/Simulink/Simscape is a necessity. Knowledge and experience of CarSim, NPSS, SUAVE, and FLOPS is a definite plus. Senior Ph.D. students in automotive, aerospace, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.
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CA1940: Autonomous vehicle planning and contro in uncertain environments
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.
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Recent Publications
- "Parameter-Adaptive Reference Governors with Learned Robust Constraint-Admissible Sets", Control Engineering Practice, DOI: 10.1016/j.conengprac.2023.105450, Vol. 133, February 2023.BibTeX TR2023-005 PDF
- @article{Chakrabarty2023feb,
- author = {Chakrabarty, Ankush and Berntorp, Karl and Di Cairano, Stefano},
- title = {Parameter-Adaptive Reference Governors with Learned Robust Constraint-Admissible Sets},
- journal = {Control Engineering Practice},
- year = 2023,
- volume = 133,
- month = feb,
- doi = {10.1016/j.conengprac.2023.105450},
- url = {https://www.merl.com/publications/TR2023-005}
- }
, - "Fixed-Time Stable Proximal Dynamical System for Solving MVIPs", IEEE Transactions on Automatic Control, December 2022.BibTeX TR2022-171 PDF
- @article{Garg2022dec,
- author = {Garg, Kunal and Baranwal, Mayank and Gupta, Rohit and Benosman, Mouhacine},
- title = {Fixed-Time Stable Proximal Dynamical System for Solving MVIPs},
- journal = {IEEE Transactions on Automatic Control},
- year = 2022,
- month = dec,
- url = {https://www.merl.com/publications/TR2022-171}
- }
, - "Sensorless Control of Synchronous Machines with DC-Link Voltage Immunity and Adaptation", IEEE Power Electronics, Drives and Energy Systems, December 2022.BibTeX TR2022-165 PDF
- @inproceedings{Anantaram2022dec,
- author = {Anantaram, Varatharajan and Wang, Yebin},
- title = {Sensorless Control of Synchronous Machines with DC-Link Voltage Immunity and Adaptation},
- booktitle = {IEEE Power Electronics, Drives and Energy Systems},
- year = 2022,
- month = dec,
- url = {https://www.merl.com/publications/TR2022-165}
- }
, - "Few-Shot Closed-Loop Performance Optimization with Bayesian Meta-Learning", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC51059.2022.9992404, December 2022.BibTeX TR2022-160 PDF
- @inproceedings{Chakrabarty2022dec,
- author = {Chakrabarty, Ankush},
- title = {Few-Shot Closed-Loop Performance Optimization with Bayesian Meta-Learning},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2022,
- month = dec,
- doi = {10.1109/CDC51059.2022.9992404},
- url = {https://www.merl.com/publications/TR2022-160}
- }
, - "Distributed Kalman Filtering: When to Share Measurements", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC51059.2022.9993404, December 2022, pp. 5399-5404.BibTeX TR2022-158 PDF
- @inproceedings{Greiff2022dec,
- author = {Greiff, Marcus and Berntorp, Karl},
- title = {Distributed Kalman Filtering: When to Share Measurements},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2022,
- pages = {5399--5404},
- month = dec,
- publisher = {IEEE},
- doi = {10.1109/CDC51059.2022.9993404},
- issn = {2576-2370},
- isbn = {978-1-6654-6761-2},
- url = {https://www.merl.com/publications/TR2022-158}
- }
, - "Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC51059.2022.9992943, December 2022.BibTeX TR2022-161 PDF
- @inproceedings{Paulson2022dec,
- author = {Paulson, Joel A. and Sorouifar, Farshud and Chakrabarty, Ankush},
- title = {Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2022,
- month = dec,
- doi = {10.1109/CDC51059.2022.9992943},
- url = {https://www.merl.com/publications/TR2022-161}
- }
, - "Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization", Journal of Process Control, DOI: 10.1016/j.jprocont.2022.11.006, Vol. 120, pp. 86-96, November 2022.BibTeX TR2022-144 PDF
- @article{Chakrabarty2022nov2,
- author = {Chakrabarty, Ankush and Burns, Daniel J. and Guay, Martin and Laughman, Christopher R.},
- title = {Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization},
- journal = {Journal of Process Control},
- year = 2022,
- volume = 120,
- pages = {86--96},
- month = nov,
- doi = {10.1016/j.jprocont.2022.11.006},
- url = {https://www.merl.com/publications/TR2022-144}
- }
, - "Abort-Safe Spacecraft Rendezvous on Elliptic Orbits", IEEE Transactions on Control Systems Technology, November 2022.BibTeX TR2022-142 PDF
- @article{AguilarMarsillach2022nov,
- author = {Aguilar Marsillach, Daniel and Di Cairano, Stefano and Weiss, Avishai},
- title = {Abort-Safe Spacecraft Rendezvous on Elliptic Orbits},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2022,
- month = nov,
- url = {https://www.merl.com/publications/TR2022-142}
- }
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- "Parameter-Adaptive Reference Governors with Learned Robust Constraint-Admissible Sets", Control Engineering Practice, DOI: 10.1016/j.conengprac.2023.105450, Vol. 133, February 2023.
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Videos
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Robot Locomotion by Automated Controller Tuning
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Real-time Mixed-integer Programming for Vehicle Decision Making and Motion Planning
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Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Networks
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[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?
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[MERL Seminar Series Spring 2022] Exact Structural Analysis of Multimode Modelica Models
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[MERL Seminar Series 2021] Use the [Magnetic] Force for Good: Sustainability Through Magnetic Levitation
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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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MPC for Satellites
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Car Path Planning
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MPC for Laser Cutting
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Fly Cut
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MERL Research on Autonomous Vehicles
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Software Downloads