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
Rien
Quirynen
Avishai
Weiss
Ankush
Chakrabarty
Christopher R.
Laughman
Daniel N.
Nikovski
Diego
Romeres
Marcus
Greiff
Devesh K.
Jha
Philip V.
Orlik
Arvind
Raghunathan
Abraham
Goldsmith
Abraham P.
Vinod
Jianlin
Guo
William S.
Yerazunis
Toshiaki
Koike-Akino
Chungwei
Lin
Hongtao
Qiao
Matthew
Brand
Vedang M.
Deshpande
Koon Hoo
Teo
Yanting
Ma
Hassan
Mansour
Jinyun
Zhang
Petros T.
Boufounos
Siddarth
Jain
Pedro
Miraldo
Kieran
Parsons
Hongbo
Sun
Gordon
Wichern
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 MERL presents 9 papers at 2023 IFAC World Congress Date: July 9, 2023 - July 14, 2023
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Diego Romeres; Abraham P. Vinod
Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers and organized 2 invited/workshop sessions at the 2023 IFAC World Congress held in Yokohama, JP.
MERL's contributions covered topics including decision-making for autonomous vehicles, statistical and learning-based estimation for GNSS and energy systems, impedance control for delta robots, learning for system identification of rigid body dynamics and time-varying systems, and meta-learning for deep state-space modeling using data from similar systems. The invited session (MERL co-organizer: Ankush Chakrabarty) was on the topic of “Estimation and observer design: theory and applications” and the workshop (MERL co-organizer: Karl Berntorp) was on “Gaussian Process Learning for Systems and Control”.
- MERL researchers presented 9 papers and organized 2 invited/workshop sessions at the 2023 IFAC World Congress held in Yokohama, JP.
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NEWS Abraham Vinod gave an invited talk at the University of California Santa Cruz Date: June 8, 2023
Where: Zoom
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Optimization, RoboticsBrief- Abraham Vinod gave an invited talk at the Electrical and Computer Engineering Department, the University of California Santa Cruz, titled "Motion Planning under Constraints and Uncertainty using Data and Reachability". His presentation covered recent work on fast and safe motion planners that can allow for coordination among agents, mitigate uncertainty arising from sensing limitations and simplified models, and tolerate the possibility of failures.
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Internships
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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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EA2045: Speed-sensorless Control of Electrical Machines
MERL is seeking a highly motivated and qualified individual to conduct research/development in speed-sensorless control of electrical machines.
The ideal candidate should have solid backgrounds in electrical machines, sensorless drives control, dynamical system analysis, signal processing, state estimation, and parameter identification. Demonstrated knowledge of the state-of-the-art sensorless drives control and experience on using dSPACE for real-time HIL experimentation is necessary. Proven record of publishing results in leading conferences/journals is a plus.
Senior Ph.D. students in electrical engineering, control, and related areas are encouraged to apply. Start date for this internship is as soon as possible and the duration is about 3-6 months. -
CA2054: Locomotion of Legged Robots
MERL is seeking a highly motivated candidate to collaborate with the Control for Autonomy team in research and experimentation on control and planning for legged robots. The ideal candidate is expected to be working towards an MS or PhD with emphasis on control or related area, and it is a merit to have interest and background in one or several of: experimentation and research on locomotion of legged robots, model predictive control, statistical estimation, machine learning, numerical optimization, motion planning, SLAM. Good programming skills in MATLAB, ROS, Python, are required and knowledge of C++ is a merit. The expected duration of the internship is 3 months with a start date of late fall 2022/early winter 2023.
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Openings
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EA2051: Research Scientist - Electric Systems Automation
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CA2053: Research Scientist - Control for Autonomy
See All Openings at MERL -
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Recent Publications
- , "Tailored Presolve Techniques in Branch-and-Bound Method for Fast Mixed-Integer Optimal Control Applications", Optimal Control Applications and Methods, August 2023.BibTeX TR2023-110 PDF
- @article{Quirynen2023aug2,
- author = {Quirynen, Rien and Di Cairano, Stefano},
- title = {Tailored Presolve Techniques in Branch-and-Bound Method for Fast Mixed-Integer Optimal Control Applications},
- journal = {Optimal Control Applications and Methods},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-110}
- }
- , "Trajectory Generation for Online Payload Estimation of Robot Manipulators: A Supervised Learning Based Approach", IEEE Conference on Automation and Science Engineering, August 2023.BibTeX TR2023-106 PDF
- @inproceedings{Duan2023aug,
- author = {Duan, Xiaoming and Wang, Yebin and Romeres, Diego and Koike-Akino, Toshiaki and Orlik, Philip V.},
- title = {Trajectory Generation for Online Payload Estimation of Robot Manipulators: A Supervised Learning Based Approach},
- booktitle = {IEEE Conference on Automation and Science Engineering},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-106}
- }
- , "Digital Twins for Vapor Compression Cycles: Challenges & Opportunities", International Congress of Refrigeration (ICR), August 2023.BibTeX TR2023-103 PDF
- @inproceedings{Laughman2023aug,
- author = {Laughman, Christopher R. and Deshpande, Vedang M. and Qiao, Hongtao and Bortoff, Scott A. and Chakrabarty, Ankush},
- title = {Digital Twins for Vapor Compression Cycles: Challenges & Opportunities},
- booktitle = {International Congress of Refrigeration (ICR)},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-103}
- }
- , "Motion Planning of Articulated Vehicles with Active Trailer Steering by Particle Filtering", Conference on Control Technology and Applications (CCTA), August 2023.BibTeX TR2023-101 PDF
- @inproceedings{Iqbal2023aug,
- author = {Iqbal, Hassan and Di Cairano, Stefano and Berntorp, Karl},
- title = {Motion Planning of Articulated Vehicles with Active Trailer Steering by Particle Filtering},
- booktitle = {Conference on Control Technology and Applications (CCTA)},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-101}
- }
- , "Gaussian Processes with State-Dependent Noise for Stochastic Control", IEEE Conference on Control Technology and Applications (CCTA), August 2023.BibTeX TR2023-100 PDF
- @inproceedings{Menner2023aug2,
- author = {Menner, Marcel and Berntorp, Karl},
- title = {Gaussian Processes with State-Dependent Noise for Stochastic Control},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-100}
- }
- , "MPC-based Pedestrian Routing for Congestion Balancing", IEEE Conference on Control Technology and Applications (CCTA), August 2023.BibTeX TR2023-099 PDF
- @inproceedings{Menner2023aug,
- author = {Menner, Marcel and Di Cairano, Stefano and Hamada, Masaki and Gushima, Kota},
- title = {MPC-based Pedestrian Routing for Congestion Balancing},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-099}
- }
- , "DEEP REINFORCEMENT LEARNING FOR STATION KEEPING ON NEAR RECTILINEAR HALO ORBITS", AIAA/AAS Astrodynamics Specialist Conference, August 2023.BibTeX TR2023-098 PDF
- @inproceedings{Suda2023aug,
- author = {Suda, Takumi and Shimane, Yuri and Elango, Purnanand and Weiss, Avishai},
- title = {DEEP REINFORCEMENT LEARNING FOR STATION KEEPING ON NEAR RECTILINEAR HALO ORBITS},
- booktitle = {AIAA/AAS Astrodynamics Specialist Conference},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-098}
- }
- , "Personalized Routing using Crowdsourced Connected Vehicle Data", IEEE Conference on Control Technology and Applications (CCTA), August 2023.BibTeX TR2023-095 PDF
- @inproceedings{Tiwari2023aug,
- author = {Tiwari, Anuj and Berntorp, Karl and Di Cairano, Stefano and Menner, Marcel},
- title = {Personalized Routing using Crowdsourced Connected Vehicle Data},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-095}
- }
- , "Tailored Presolve Techniques in Branch-and-Bound Method for Fast Mixed-Integer Optimal Control Applications", Optimal Control Applications and Methods, August 2023.
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Videos
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[MERL Seminar Series Spring 2023] Learning and Dynamical Systems
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[MERL Seminar Series Spring 2023] Investigating Multi-Agent Reinforcement Learning for Grid-Interactive Smart Communities using CityLearn
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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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MPC for Satellites
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Car Path Planning
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MPC for Laser Cutting
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MERL Research on Autonomous Vehicles
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Fly Cut
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HVAC Lab & Controls
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Downloads
