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
Arvind
Raghunathan
Devesh K.
Jha
Saleh
Nabi
Philip V.
Orlik
Abraham M.
Goldsmith
Jianlin
Guo
Chungwei
Lin
Matthew E.
Brand
Marcus
Greiff
Marcel
Menner
Hongtao
Qiao
Koon Hoo
Teo
Toshiaki
Koike-Akino
Yanting
Ma
Alan
Sullivan
William S.
Yerazunis
Kyeong Jin
(K.J.)
Kim
Abraham P.
Vinod
Jinyun
Zhang
Petros T.
Boufounos
Rui
Ma
Hongbo
Sun
Anantaram
Varatharajan
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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 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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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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TALK [MERL Seminar Series 2022] Prof. Michael Posa presents talk titled Hybrid robotics and implicit learning Date & Time: Tuesday, May 3, 2022; 1:00 PM
Speaker: Michael Posa, University of Pennsylvania
MERL Host: Devesh K. Jha
Research Areas: Control, Optimization, RoboticsAbstract
Machine learning has shown incredible promise in robotics, with some notable recent demonstrations in manipulation and sim2real transfer. These results, however, require either an accurate a priori model (for simulation) or a large amount of data. In contrast, my lab is focused on enabling robots to enter novel environments and then, with minimal time to gather information, accomplish complex tasks. In this talk, I will argue that the hybrid or contact-driven nature of real-world robotics, where a robot must safely and quickly interact with objects, drives this high data requirement. In particular, the inductive biases inherent in standard learning methods fundamentally clash with the non-differentiable physics of contact-rich robotics. Focusing on model learning, or system identification, I will show both empirical and theoretical results which demonstrate that contact stiffness leads to poor training and generalization, leading to some healthy skepticism of simulation experiments trained on artificially soft environments. Fortunately, implicit learning formulations, which embed convex optimization problems, can dramatically reshape the optimization landscape for these stiff problems. By carefully reasoning about the roles of stiffness and discontinuity, and integrating non-smooth structures, we demonstrate dramatically improved learning performance. Within this family of approaches, ContactNets accurately identifies the geometry and dynamics of a six-sided cube bouncing, sliding, and rolling across a surface from only a handful of sample trajectories. Similarly, a piecewise-affine hybrid system with thousands of modes can be identified purely from state transitions. Time permitting, I'll discuss how these learned models can be deployed for control via recent results in real-time, multi-contact MPC.
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TALK [MERL Seminar Series 2022] Prof. Sebastien Gros presents talk titled RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning? Date & Time: Tuesday, April 12, 2022; 11:00 AM EDT
Speaker: Sebastien Gros, NTNU
MERL Host: Rien Quirynen
Research Areas: Control, Dynamical Systems, OptimizationAbstract
Reinforcement Learning (RL), similarly to many AI-based techniques, is currently receiving a very high attention. RL is most commonly supported by classic Machine Learning techniques, i.e. typically Deep Neural Networks (DNNs). While there are good motivations for using DNNs in RL, there are also significant drawbacks. The lack of “explainability” of the resulting control policies, and the difficulty to provide guarantees on their closed-loop behavior (safety, stability) makes DNN-based policies problematic in many applications. In this talk, we will discuss an alternative approach to support RL, via formal optimal control tools based on Model Predictive Control (MPC). This approach alleviates the issues detailed above, but also presents some challenges. In this talk, we will discuss why MPC is a valid tool to support RL, and how MPC can be combined with RL (RLMPC). We will then discuss some recent results regarding this combination, the known challenges, and the kind of control applications where we believe that RLMPC will be a valuable approach.
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Internships
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CA1741: Learning for Connected Vehicles
MERL is seeking a highly motivated intern to collaborate with the Control for Autonomy team in the development of learning technologies for Connected Vehicles. The intern will conduct research in the development of methods for learning/optimization of Advanced Driver Assistance Systems (ADAS) using data-sharing between connected vehicles and/or infrastructure. The ideal candidate has knowledge of at least one of machine learning, estimation, connected vehicles, and vehicle control systems. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. Good programming skills in Matlab are required and knowledge in Python or C/C++ is a merit. PhD students in engineering, mathematics, or similar are encouraged to apply. The expected duration of the internship is 3-6 months. The start date is flexible.
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CA1726: Distributed Estimation for Autonomous Systems
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in developing estimation methods with applications to multi-vehicle positioning. The ideal candidate is a PhD candidate with strong emphasis in estimation and control, and as interest and background in several of: bayesian inference, machine learning, maximum-likelihood estimation, optimization, distributed systems, and vehicle modeling and control. Good programming skills in MATLAB, Python, or C/C++ are required. The expected start of of the internship is in 2022 and flexible for a duration of 3-6 months.
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CA1728: Safe data-driven control of dynamical systems under uncertainty
MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety and performance in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (robust and stochastic) model predictive control, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in submission of relevant results to peer-reviewed conference proceedings and journals, and development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is Summer 2022.
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Recent Publications
- , "Safe multi-agent motion planning via filtered reinforcement learning", IEEE International Conference on Robotics and Automation (ICRA) 2022, May 2022.BibTeX TR2022-053 PDF Video
- @inproceedings{Vinod2022may,
- author = {Vinod, Abraham P. and Safaoui, Sleiman and Chakrabarty, Ankush and Quirynen, Rien and yoshikawa, nobuyuki and Di Cairano, Stefano},
- title = {Safe multi-agent motion planning via filtered reinforcement learning},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA) 2022},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-053}
- }
- , "Application of Pontryagin’s Maximum Principle to Quantum Metrology in Dissipative Systems", Physical Reivew A, May 2022.BibTeX TR2022-048 PDF
- @article{Lin2022may,
- author = {Lin, Chungwei and Ma, Yanting and Sels, Dries},
- title = {Application of Pontryagin’s Maximum Principle to Quantum Metrology in Dissipative Systems},
- journal = {Physical Reivew A},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-048}
- }
- , "Optimal Dynamic Transmission Scheduling for Wireless Networked Control Systems", IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, May 2022.BibTeX TR2022-043 PDF
- @article{Ma2022may,
- author = {Ma, Yehan and Guo, Jianlin and Wang, Yebin and Chakrabarty, Ankush and Ahn, Heejin and Orlik, Philip V. and Guan, Xinping and Lu, Chenyang},
- title = {Optimal Dynamic Transmission Scheduling for Wireless Networked Control Systems},
- journal = {IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-043}
- }
- , "PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control", Learning for Dynamics and Control Conference (L4DC), April 2022.BibTeX TR2022-039 PDF
- @inproceedings{Cauligi2022apr,
- author = {Cauligi, Abhishek and Chakrabarty, Ankush and Di Cairano, Stefano and Quirynen, Rien},
- title = {PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control},
- booktitle = {Learning for Dynamics and Control Conference (L4DC)},
- year = 2022,
- month = apr,
- url = {https://www.merl.com/publications/TR2022-039}
- }
- , "H-Infinity Loop-Shaped Model Predictive Control with HVAC Application", IEEE Transactions on Control Systems Technology, March 2022.BibTeX TR2022-028 PDF
- @article{Bortoff2022mar,
- author = {Bortoff, Scott A. and Schwerdtner, Paul and Danielson, Claus and Di Cairano, Stefano and Burns, Daniel J.},
- title = {H-Infinity Loop-Shaped Model Predictive Control with HVAC Application},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2022,
- month = mar,
- url = {https://www.merl.com/publications/TR2022-028}
- }
- , "Accelerating self-optimization control of refrigerant cycles with Bayesian optimization and adaptive moment estimation", Applied Thermal Engineering, DOI: 10.1016/j.applthermaleng.2021.117335, Vol. 197, pp. 117335, February 2022.BibTeX TR2022-010 PDF
- @article{Chakrabarty2022feb,
- author = {Chakrabarty, Ankush and Danielson, Claus and Bortoff, Scott A. and Laughman, Christopher R.},
- title = {Accelerating self-optimization control of refrigerant cycles with Bayesian optimization and adaptive moment estimation},
- journal = {Applied Thermal Engineering},
- year = 2022,
- volume = 197,
- pages = 117335,
- month = feb,
- doi = {10.1016/j.applthermaleng.2021.117335},
- url = {https://www.merl.com/publications/TR2022-010}
- }
- , "Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data", IFAC Journal of Systems and Control, DOI: 10.1016/j.ifacsc.2021.100181, Vol. 19, pp. 100181, January 2022.BibTeX TR2022-009 PDF
- @article{Vijayshankar2022jan,
- author = {Vijayshankar, Sanjana and Chakrabarty, Ankush and Grover, Piyush and Nabi, Saleh},
- title = {Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data},
- journal = {IFAC Journal of Systems and Control},
- year = 2022,
- volume = 19,
- pages = 100181,
- month = jan,
- doi = {10.1016/j.ifacsc.2021.100181},
- url = {https://www.merl.com/publications/TR2022-009}
- }
- , "Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications", IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/TMECH.2021.3081035, Vol. 26, No. 4, pp. 1941-1950, January 2022.BibTeX TR2022-003 PDF
- @article{Jeon2022jan,
- author = {Jeon, Woongsun and Chakrabarty, Ankush and Zemouche, Ali and Rajamani, Rajesh},
- title = {Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications},
- journal = {IEEE/ASME Transactions on Mechatronics},
- year = 2022,
- volume = 26,
- number = 4,
- pages = {1941--1950},
- month = jan,
- doi = {10.1109/TMECH.2021.3081035},
- url = {https://www.merl.com/publications/TR2022-003}
- }
- , "Safe multi-agent motion planning via filtered reinforcement learning", IEEE International Conference on Robotics and Automation (ICRA) 2022, May 2022.
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Videos
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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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Particle filter-based planning demonstration using mini-cars
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MPC control and particle filter-based planning demonstration using mini-cars
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Fly Cut
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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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MERL Research on Autonomous Vehicles
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Software Downloads
