Dynamical Systems

Exploiting nonlinearity and shaping dynamics in creative and deeply mathematical ways.

We apply dynamical systems theory in applications ranging from space probe trajectory optimization to elevator suspensions. We also develop fundamental theory and computational methods in fluid dynamics.

  • Researchers

  • News & Events

    •  TALK   [MERL Seminar Series 2021] Harnessing machine learning to build better Earth system models for climate projection
      Date & Time: Tuesday, December 14, 2021; 1:00 PM EST
      Speaker: Prof. Chris Fletcher, University of Waterloo
      MERL Host: Ankush Chakrabarty
      Research Areas: Dynamical Systems, Machine Learning, Multi-Physical Modeling
      Brief
      • Decision-making and adaptation to climate change requires quantitative projections of the physical climate system and an accurate understanding of the uncertainty in those projections. Earth system models (ESMs), which solve the Navier-Stokes equations on the sphere, are the only tool that climate scientists have to make projections forward into climate states that have not been observed in the historical data record. Yet, ESMs are incredibly complex and expensive codes and contain many poorly constrained physical parameters—for processes such as clouds and convection—that must be calibrated against observations. In this talk, I will describe research from my group that uses ensembles of ESM simulations to train statistical models that learn the behavior and sensitivities of the ESM. Once trained and validated the statistical models are essentially free to run, which allows climate modelling centers to make more efficient use of precious compute cycles. The aim is to improve the quality of future climate projections, by producing better calibrated ESMs, and to improve the quantification of the uncertainties, by better sampling the equifinality of climate states.
    •  
    •  EVENT   Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Melanie Zeilinger, ETH
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
        Prof. Melanie Zeilinger from ETH .

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction

        Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
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  • Internships

    • DA1768: Contact Modeling and Optimization

      MERL is looking for a self-motivated and qualified candidate to work on modeling for contact phenomenon. Robotic manipulation is heavily affected by external contacts that can be modeled with physical and data driven models. We are interested in researching those models for analysis and control purposes. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas. Contact modeling and robotic manipulation. Physic Engines like Mujoco, Bullet, Drake and sim2real gap problems. Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. Experience in working with robotic systems. Knowledge in learning from demonstration algorithms and standard Reinforcement Learning algorithms is a plus. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms that will lead to a scientific publication. Typical internship length is 3-4 months.

    • CA1719: Spacecraft Guidance, Navigation, and Control

      MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates have experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, rendezvous, attitude control, orbit control, orbit determination, nonlinear estimation, and optimization-based control. PhD students in aerospace, mechanical, or electrical engineering are encouraged to apply. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible.

    • CA1707: Autonomous vehicles guidance and control

      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. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. 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, predictive control algorithms linear and nonlinear systems, motion planning, convex, non-convex, and mixed -integer optimization, statistical estimation, cooperative control. Good programming skills in MATLAB, Python or C/C++ are required, knowledge of rapid prototyping systems, automatic code generation or ROS is 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

    •  Berntorp, K., Chakrabarty, A., Di Cairano, S., "Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor", IEEE Annual Conference on Decision and Control (CDC), December 2021.
      BibTeX TR2021-151 PDF
      • @inproceedings{Berntorp2021dec,
      • author = {Berntorp, Karl and Chakrabarty, Ankush and Di Cairano, Stefano},
      • title = {Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2021,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2021-151}
      • }
    •  Quirynen, R., Di Cairano, S., "Sequential Quadratic Programming Algorithm for Real-Time Mixed-Integer Nonlinear MPC", IEEE Conference on Decision and Control (CDC), December 2021.
      BibTeX TR2021-147 PDF
      • @inproceedings{Quirynen2021dec,
      • author = {Quirynen, Rien and Di Cairano, Stefano},
      • title = {Sequential Quadratic Programming Algorithm for Real-Time Mixed-Integer Nonlinear MPC},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2021,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2021-147}
      • }
    •  Vinod, A.P., Weiss, A., Di Cairano, S., "Abort-safe spacecraft rendezvous under stochastic actuation and navigation uncertainty", IEEE Annual Conference on Decision and Control (CDC), December 2021.
      BibTeX TR2021-148 PDF
      • @inproceedings{Vinod2021dec,
      • author = {Vinod, Abraham P. and Weiss, Avishai and Di Cairano, Stefano},
      • title = {Abort-safe spacecraft rendezvous under stochastic actuation and navigation uncertainty},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2021,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2021-148}
      • }
    •  Bonzanini, A.D., Mesbah, A., Di Cairano, S., "On the Stability Properties of Perception-aware Chance-constrained MPC in Uncertain Environments", IEEE Conference on Decision and Control (CDC), December 2021.
      BibTeX TR2021-145 PDF
      • @inproceedings{Bonzanini2021dec,
      • author = {Bonzanini, Angelo Domenico and Mesbah, Ali and Di Cairano, Stefano},
      • title = {On the Stability Properties of Perception-aware Chance-constrained MPC in Uncertain Environments},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2021,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2021-145}
      • }
    •  Johnson, R.S., Di Cairano, S., Sanfelice, R., "Parameter Estimation using Hybrid Gradient Descent", IEEE Conference on Decision and Control (CDC), December 2021.
      BibTeX TR2021-146 PDF
      • @inproceedings{Johnson2021dec,
      • author = {Johnson, Ryan S. and Di Cairano, Stefano and Sanfelice, Ricardo},
      • title = {Parameter Estimation using Hybrid Gradient Descent},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2021,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2021-146}
      • }
    •  Wang, B., Zhou, L., Miyoshi, M., Inoue, H., Kanemaru, M., "Quantification of Induction Motor Bearing Fault Severity based on Modified Winding Function Theory", International Conference on Electrical Machines and Systems (ICEMS), DOI: 10.23919/​ICEMS52562.2021.9634328, November 2021, pp. 944-948.
      BibTeX TR2021-139 PDF
      • @inproceedings{Wang2021nov3,
      • author = {Wang, Bingnan and Zhou, Lei and Miyoshi, Masahito and Inoue, hiroshi and Kanemaru, Makoto},
      • title = {Quantification of Induction Motor Bearing Fault Severity based on Modified Winding Function Theory},
      • booktitle = {2021 24th International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2021,
      • pages = {944--948},
      • month = nov,
      • publisher = {IEEE},
      • doi = {10.23919/ICEMS52562.2021.9634328},
      • url = {https://www.merl.com/publications/TR2021-139}
      • }
    •  Yao, G., WANG, P., Berntorp, K., Mansour, H., Boufounos, P.T., Orlik, P.V., "Extended Object Tracking with Spatial Model Adaptation Using Automotive Radar", International Conference on Information Fusion (FUSION), November 2021, pp. 1-8.
      BibTeX TR2021-138 PDF
      • @inproceedings{Yao2021nov,
      • author = {Yao, Gang and WANG, PU and Berntorp, Karl and Mansour, Hassan and Boufounos, Petros T. and Orlik, Philip V.},
      • title = {Extended Object Tracking with Spatial Model Adaptation Using Automotive Radar},
      • booktitle = {International Conference on Information Fusion (FUSION)},
      • year = 2021,
      • pages = {1--8},
      • month = nov,
      • isbn = {IEEE Xplore},
      • url = {https://www.merl.com/publications/TR2021-138}
      • }
    •  Chen, D., Danielson, C., Di Cairano, S., "A Predictive Controller for Drivability and Comfort in Multi-Motor Electric Vehicles", IFAC Modeling, Estimation and Control Conference (MECC), October 2021.
      BibTeX TR2021-133 PDF
      • @inproceedings{Chen2021oct,
      • author = {Chen, Di and Danielson, Claus and Di Cairano, Stefano},
      • title = {A Predictive Controller for Drivability and Comfort in Multi-Motor Electric Vehicles},
      • booktitle = {IFAC Modeling, Estimation and Control Conference (MECC)},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-133}
      • }
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  • Videos