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
Avishai
Weiss
Ankush
Chakrabarty
Christopher R.
Laughman
Daniel N.
Nikovski
Diego
Romeres
Devesh K.
Jha
Philip V.
Orlik
Arvind
Raghunathan
Abraham P.
Vinod
Abraham
Goldsmith
Jianlin
Guo
William S.
Yerazunis
Vedang M.
Deshpande
Toshiaki
Koike-Akino
Chungwei
Lin
Hongtao
Qiao
Matthew
Brand
Koon Hoo
Teo
Yanting
Ma
Hassan
Mansour
Pedro
Miraldo
Jinyun
Zhang
Petros T.
Boufounos
Siddarth
Jain
Kieran
Parsons
James
Queeney
Hongbo
Sun
Gordon
Wichern
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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 Saviz Mowlavi gave an invited talk at North Carolina State University Date: April 12, 2024
MERL Contact: Saviz Mowlavi
Research Areas: Control, Dynamical Systems, Machine Learning, OptimizationBrief- Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.
The talk, entitled "Model-based and data-driven prediction and control of spatio-temporal systems", described the use of temporal smoothness to regularize the training of fast surrogate models for PDEs, user-friendly methods for PDE-constrained optimization, and efficient strategies for learning feedback controllers for PDEs.
- Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.
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TALK [MERL Seminar Series 2024] Na Li presents talk titled Close the Loop: From Data to Actions in Complex Systems Date & Time: Wednesday, April 10, 2024; 12:00 PM
Speaker: Na Li, Harvard University
MERL Host: Yebin Wang
Research Areas: Control, Dynamical Systems, Machine LearningAbstract- The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, translating these successes to the domain of dynamical, physical systems remains a significant challenge, hindered by the complex and often unpredictable nature of such environments. Closing the loop from data to actions in these systems faces many difficulties, stemming from the need for sample efficiency and computational feasibility amidst intricate dynamics, along with many other requirements such as verifiability, robustness, and safety. In this talk, we bridge this gap by introducing innovative approaches that harness representation-based methods, domain knowledge, and the physical structures of systems. We present a comprehensive framework that integrates these components to develop reinforcement learning and control strategies that are not only tailored for the complexities of physical systems but also achieve efficiency, safety, and robustness with provable performance.
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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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MS1958: Simulation, Control, and Optimization of Large-Scale Systems
MERL is seeking a motivated graduate student to research numerical methods pertaining to the simulation, control, and optimization of large-scale systems. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in numerical methods, control, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 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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Openings
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Recent Publications
- "Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees", Transactions on Machine Learning Research (TMLR), April 2024.BibTeX TR2024-037 PDF
- @article{Queeney2024apr,
- author = {Queeney, James and Ozcan, Erhan Can and Paschalidis, Ioannis Ch. and Cassandras, Christos G.},
- title = {Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees},
- journal = {Transactions on Machine Learning Research (TMLR)},
- year = 2024,
- month = apr,
- issn = {2835-8856},
- url = {https://www.merl.com/publications/TR2024-037}
- }
, - "LMI-Based Neural Observer for State and Nonlinear Function Estimation", International Journal of Robust and Nonlinear Control, DOI: 10.1002/rnc.7327, April 2024.BibTeX TR2024-036 PDF
- @article{Jeon2024apr,
- author = {Jeon, Woongsun and Chakrabarty, Ankush and Zemouche, Ali and Rajamani, Rajesh},
- title = {LMI-Based Neural Observer for State and Nonlinear Function Estimation},
- journal = {International Journal of Robust and Nonlinear Control},
- year = 2024,
- month = apr,
- doi = {10.1002/rnc.7327},
- url = {https://www.merl.com/publications/TR2024-036}
- }
, - "Hierarchical planning for autonomous parking in dynamic environments", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2024.3367468, March 2024.BibTeX TR2024-034 PDF
- @article{Wang2024mar2,
- author = {Wang, Yebin and Hansen, Emma and Ahn, Heejin},
- title = {Hierarchical planning for autonomous parking in dynamic environments},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2024,
- month = mar,
- doi = {10.1109/TCST.2024.3367468},
- issn = {1558-0865},
- url = {https://www.merl.com/publications/TR2024-034}
- }
, - "Control Challenges and Opportunities in Building Automation" in The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, February 2024.BibTeX TR2024-011 PDF
- @incollection{Bortoff2024feb,
- author = {Bortoff, Scott A. and Eisenhower, Bryan and Adetola, Veronica and O'Neil, Zheng},
- title = {Control Challenges and Opportunities in Building Automation},
- booktitle = {The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future},
- year = 2024,
- month = feb,
- url = {https://www.merl.com/publications/TR2024-011}
- }
, - "Lunar Landing with Feasible Divert using Controllable Sets", AIAA SciTech, DOI: 10.2514/6.2024-0324, January 2024, pp. AIAA 2024-0324.BibTeX TR2024-004 PDF
- @inproceedings{Srinivas2024jan,
- author = {Srinivas, Neeraj and Vinod, Abraham P. and Di Cairano, Stefano and Weiss, Avishai},
- title = {Lunar Landing with Feasible Divert using Controllable Sets},
- booktitle = {AIAA SCITECH 2024 Forum},
- year = 2024,
- pages = {AIAA 2024--0324},
- month = jan,
- doi = {10.2514/6.2024-0324},
- url = {https://www.merl.com/publications/TR2024-004}
- }
, - "Perception-Aware Model Predictive Control for Constrained Control in Unknown Environments", Automatica, DOI: 10.1016/j.automatica.2023.111418, December 2023.BibTeX TR2023-147 PDF
- @article{Bonzanini2023dec,
- author = {Bonzanini, Angelo Domenico and Mesbah, Ali and Di Cairano, Stefano},
- title = {Perception-Aware Model Predictive Control for Constrained Control in Unknown Environments},
- journal = {Automatica},
- year = 2023,
- month = dec,
- doi = {10.1016/j.automatica.2023.111418},
- url = {https://www.merl.com/publications/TR2023-147}
- }
, - "Preference-Guided Bayesian Optimization for Control Policy Learning: Application to Personalized Plasma Medicine", Advances in Neural Information Processing Systems (NeurIPS), December 2023.BibTeX TR2023-146 PDF
- @inproceedings{Shao2023dec,
- author = {Shao, Ketong and Romeres, Diego and Chakrabarty, Ankush and Mesbah, Ali},
- title = {Preference-Guided Bayesian Optimization for Control Policy Learning: Application to Personalized Plasma Medicine},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2023,
- month = dec,
- url = {https://www.merl.com/publications/TR2023-146}
- }
, - "Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States", IEEE Control Systems Letters, DOI: 10.1109/LCSYS.2023.3334959, November 2023.BibTeX TR2023-138 PDF
- @article{Deshpande2023nov,
- author = {Deshpande, Vedang M. and Chakrabarty, Ankush and Vinod, Abraham P. and Laughman, Christopher R.},
- title = {Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States},
- journal = {IEEE Control Systems Letters},
- year = 2023,
- month = nov,
- doi = {10.1109/LCSYS.2023.3334959},
- url = {https://www.merl.com/publications/TR2023-138}
- }
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- "Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees", Transactions on Machine Learning Research (TMLR), April 2024.
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