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.
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Researchers
Stefano
Di Cairano
Yebin
Wang
Karl
Berntorp
Scott A.
Bortoff
Mouhacine
Benosman
Ankush
Chakrabarty
Avishai
Weiss
Christopher R.
Laughman
Daniel N.
Nikovski
Diego
Romeres
Abraham P.
Vinod
Devesh K.
Jha
Arvind
Raghunathan
Philip V.
Orlik
Abraham
Goldsmith
William S.
Yerazunis
Jianlin
Guo
Hongtao
Qiao
Vedang M.
Deshpande
Chungwei
Lin
Toshiaki
Koike-Akino
Matthew
Brand
Yanting
Ma
Dehong
Liu
Hassan
Mansour
Pedro
Miraldo
Ye
Wang
Jinyun
Zhang
Petros T.
Boufounos
Siddarth
Jain
Kieran
Parsons
James
Queeney
Hongbo
Sun
Bingnan
Wang
Gordon
Wichern
Na
Li
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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 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 K. 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 MERL researchers present 9 papers at ACC 2024 Date: July 10, 2024 - July 12, 2024
Where: Toronto, Canada
MERL Contacts: Karl Berntorp; Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
In addition, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
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TALK [MERL Seminar Series 2024] Chuchu Fan presents talk titled Neural Certificates and LLMs in Large-Scale Autonomy Design Date & Time: Wednesday, May 29, 2024; 12:00 PM
Speaker: Chuchu Fan, MIT
MERL Host: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Machine LearningAbstract
Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics. However, this performance often arrives with the trade-off of diminished transparency and the absence of guarantees regarding the safety and stability of the learned controllers. In recent years, new techniques have emerged to provide these guarantees by learning certificates alongside control policies — these certificates provide concise, data-driven proofs that guarantee the safety and stability of the learned control system. These methods not only allow the user to verify the safety of a learned controller but also provide supervision during training, allowing safety and stability requirements to influence the training process itself. In this talk, we present two exciting updates on neural certificates. In the first work, we explore the use of graph neural networks to learn collision-avoidance certificates that can generalize to unseen and very crowded environments. The second work presents a novel reinforcement learning approach that can produce certificate functions with the policies while addressing the instability issues in the optimization process. Finally, if time permits, I will also talk about my group's recent work using LLM and domain-specific task and motion planners to allow natural language as input for robot planning.
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Internships
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CA2132: Optimization Algorithms for Motion Planning and Predictive Control
MERL is looking for a highly motivated and qualified individual to work on tailored computational algorithms for optimization-based motion planning and predictive control applications in autonomous systems (vehicles, mobile robots). The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, stochastic predictive control (e.g., scenario trees), interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.
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CA2182: Motion Planning and Control for Articulated Vehicles
MERL is seeking a highly skilled and self-motivated intern to work on motion planning of articulated vehicles. The ideal candidate should have solid backgrounds in established path/motion planning algorithms (A*, D*, graph-search) and optimization-based control for ground and articulated vehicles. Excellent coding skills in MATLAB/Simulink and publication records are necessary. Experience with CasADi and dSPACE is a plus. Ph.D. students in robotics, computer science, control, electrical engineering, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 4-6 months.
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CA2213: Mobile robotics: Sensing, Planning, and Control
MERL is seeking a highly motivated intern to collaborate in the development and experimental validation of sensing, planning, and control methods in various robotic testbeds (quadrotors, turtlebots, and mini-cars) at MERL. The ideal candidate is enrolled in a Masters/PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in some or all of the following: motion planning, control, optimization, learning, computer vision, and their application in mobile robots, including experimental validation. The successful candidate is proficient in ROS2, C/C++, and Python, and at least familiar with MATLAB. The expected duration of the internship is 4-6 months with a flexible start date in the late Fall/Winter 2024.
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Openings
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Recent Publications
- , "Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning", IEEE Transactions on Control Systems Technology, September 2024.BibTeX TR2024-123 PDF
- @article{Quirynen2024sep,
- author = {Quirynen, Rien and Safaoui, Sleiman and Di Cairano, Stefano}},
- title = {Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-123}
- }
- , "MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models", International Conference on Automation Science and Engineering (CASE), August 2024.BibTeX TR2024-115 PDF
- @inproceedings{Yan2024aug,
- author = {Yan, Jiaqi and Chakrabarty, Ankush and Rupenyan, Alisa and Lygeros, John}},
- title = {MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models},
- booktitle = {International Conference on Automation Science and Engineering (CASE)},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-115}
- }
- , "Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks", IEEE Conference on Control Technology and Applications (CCTA) 2024, August 2024.BibTeX TR2024-113 PDF
- @inproceedings{Chakrabarty2024aug,
- author = {Chakrabarty, Ankush and Vanfretti, Luigi and Bortoff, Scott A. and Deshpande, Vedang M. and Wang, Ye and Paulson, Joel A. and Zhan, Sicheng and Laughman, Christopher R.}},
- title = {Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-113}
- }
- , "Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics", IEEE Conference on Control Technology and Applications (CCTA) 2024, August 2024.BibTeX TR2024-114 PDF
- @inproceedings{Park2024aug,
- author = {Park, Seho and Wang, Yebin and Qiao, Hongtao and Sakamoto, Yusuke and Wang, Bingnan and Liu, Dehong}},
- title = {Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-114}
- }
- , "Modeling and Control of a Multi-Mode Heat Pump", IEEE Conference on Control Technology and Applications (CCTA) 2024, August 2024.BibTeX TR2024-111 PDF
- @inproceedings{Bortoff2024aug,
- author = {Bortoff, Scott A. and Qiao, Hongtao and Laughman, Christopher R.}},
- title = {Modeling and Control of a Multi-Mode Heat Pump},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-111}
- }
- , "Power System Modeling for Identification and Control Applications using Modelica and OpenIPSL", Conference on Control Technology and Applications (CCTA), August 2024.BibTeX TR2024-112 PDF
- @inproceedings{Vanfretti2024aug,
- author = {Vanfretti, Luigi and Laughman, Christopher R.}},
- title = {Power System Modeling for Identification and Control Applications using Modelica and OpenIPSL},
- booktitle = {Conference on Control Technology and Applications (CCTA)},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-112}
- }
- , "Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems", IEEE Conference on Control Technology and Applications (CCTA), August 2024.BibTeX TR2024-110 PDF
- @inproceedings{Sorouifar2024aug,
- author = {Sorouifar, Farshud and Paulson, Joel A. and Wang, Ye and Quirynen, Rien and Laughman, Christopher R. and Chakrabarty, Ankush}},
- title = {Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-110}
- }
- , "Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning", IEEE Transactions on Control Systems Technology, September 2024.
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Videos
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