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.

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  • News & Events


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  • Internships

    • SP1510: Learning for inverse problems and dynamical systems

      The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging, and Koopman theory/dynamic mode decomposition. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • MD1479: Electrical Power System Modeling Simulation

      MERL is seeking a motivated and qualified individual to conduct research in modeling, simulation and control of aircraft electrical power system. The ideal candidate should have solid backgrounds in dynamic modeling and simulation of power electronics and electrical machine, and transient analysis of overall electrical power system. Demonstrated experience in physical modeling and simulation software/language such as Modelica or Simscape is a necessity. Knowledge of aircraft dynamics and aerodynamics is a big plus. Senior Ph.D. students in aerospace, electrical engineering, control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • SP1507: Extended Object Tracking with Automotive Radar

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in extended object tracking (EOT) using automotive radar sensors. Previous experience in multiple (point and extended) object tracking, data association, and motion/measurement model learning on open automotive datasets is highly preferred. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, data analysis (Python & MATLAB), and prepare results for patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


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  • Recent Publications

    •  Caverly, R., Di Cairano, S., Weiss, A., "Electric Satellite Station Keeping, Attitude Control, and Momentum Management by MPC", IEEE Transactions on Control Systems Technology, December 2020.
      BibTeX TR2020-153 PDF
      • @article{Caverly2020dec,
      • author = {Caverly, Ryan and Di Cairano, Stefano and Weiss, Avishai},
      • title = {Electric Satellite Station Keeping, Attitude Control, and Momentum Management by MPC},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-153}
      • }
    •  Muralidharan, V., Weiss, A., Kalabic, U., "Tracking neighboring quasi-satellite orbits around Phobos", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-102 PDF
      • @inproceedings{Muralidharan2020jul,
      • author = {Muralidharan, Vivek and Weiss, Avishai and Kalabic, Uros},
      • title = {Tracking neighboring quasi-satellite orbits around Phobos},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-102}
      • }
    •  Maske, H., Chu, T., Kalabic, U., "Control of traffic light timing using decentralized deep reinforcement learning", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-101 PDF
      • @inproceedings{Maske2020jul,
      • author = {Maske, Harshal and Chu, Tianshu and Kalabic, Uros},
      • title = {Control of traffic light timing using decentralized deep reinforcement learning},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-101}
      • }
    •  Aguilar Marsillach, D., Di Cairano, S., Weiss, A., "Fail-safe Rendezvous Control on Elliptic Orbits using Reachable Sets", American Control Conference (ACC), DOI: 10.23919/ACC45564.2020.9147957, July 2020, pp. 4920-4925.
      BibTeX TR2020-098 PDF
      • @inproceedings{AguilarMarsillach2020jul,
      • author = {Aguilar Marsillach, Daniel and Di Cairano, Stefano and Weiss, Avishai},
      • title = {Fail-safe Rendezvous Control on Elliptic Orbits using Reachable Sets},
      • booktitle = {American Control Conference (ACC)},
      • year = 2020,
      • pages = {4920--4925},
      • month = jul,
      • publisher = {IEEE},
      • doi = {10.23919/ACC45564.2020.9147957},
      • issn = {2378-5861},
      • isbn = {978-1-5386-8266-1},
      • url = {https://www.merl.com/publications/TR2020-098}
      • }
    •  Vijayshankar, S., Nabi, S., Chakrabarty, A., Grover, P., Benosman, M., "Dynamic Mode Decomposition and Robust Estimation: Case Study of a 2D Turbulent Boussinesq Flow", American Control Conference (ACC), DOI: 10.23919/ACC45564.2020.9147823, July 2020, pp. 2351-2356.
      BibTeX TR2020-091 PDF
      • @inproceedings{Vijayshankar2020jul,
      • author = {Vijayshankar, Sanjana and Nabi, Saleh and Chakrabarty, Ankush and Grover, Piyush and Benosman, Mouhacine},
      • title = {Dynamic Mode Decomposition and Robust Estimation: Case Study of a 2D Turbulent Boussinesq Flow},
      • booktitle = {American Control Conference (ACC)},
      • year = 2020,
      • pages = {2351--2356},
      • month = jul,
      • publisher = {IEEE},
      • doi = {10.23919/ACC45564.2020.9147823},
      • issn = {2378-5861},
      • isbn = {978-1-5386-8266-1},
      • url = {https://www.merl.com/publications/TR2020-091}
      • }
    •  Tian, N., Fang, H., Wang, Y., "Real-Time Optimal Lithium-Ion Battery Charging Based on Explicit Model Predictive Control", IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2020.2983176, June 2020.
      BibTeX TR2020-090 PDF
      • @article{Tian2020jun,
      • author = {Tian, Ning and Fang, Huazhen and Wang, Yebin},
      • title = {Real-Time Optimal Lithium-Ion Battery Charging Based on Explicit Model Predictive Control},
      • journal = {IEEE Transactions on Industrial Informatics},
      • year = 2020,
      • month = jun,
      • doi = {10.1109/TII.2020.2983176},
      • url = {https://www.merl.com/publications/TR2020-090}
      • }
    •  Kalabic, U., Grover, P., Aeron, S., "Optimization-based incentivization and control scheme for autonomous traffic", IEEE Intelligent Vehicle Symposium, June 2020.
      BibTeX TR2020-079 PDF
      • @inproceedings{Kalabic2020jun,
      • author = {Kalabic, Uros and Grover, Piyush and Aeron, Shuchin},
      • title = {Optimization-based incentivization and control scheme for autonomous traffic},
      • booktitle = {IEEE Intelligent Vehicle Symposium},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-079}
      • }
    •  Tian, N., Fang, H., Chen, J., Wang, Y., "Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2020.2976036, pp. 1-15, April 2020.
      BibTeX TR2020-035 PDF
      • @article{Tian2020apr,
      • author = {Tian, Ning and Fang, Huazhen and Chen, Jian and Wang, Yebin},
      • title = {Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2020,
      • pages = {1--15},
      • month = apr,
      • doi = {10.1109/TCST.2020.2976036},
      • url = {https://www.merl.com/publications/TR2020-035}
      • }
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