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

    • CA1695: Spacecraft Attitude Control

      MERL is seeking a highly motivated intern for a research position in spacecraft attitude control. The ideal candidate has experience in attitude kinematics and dynamics, computational fluid dynamics (CFD) using OpenFOAM, programming in C++, optimization, and control of rigid bodies and PDEs. Experience in multi-phase flow modeling and volume-of-fluid approach with an emphasis on liquid-gas systems is highly desirable. 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 internship is 3-6 months, and the 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.

    • MD1693: Aircraft electric propulsion system design

      MERL is seeking a motivated and qualified individual to conduct research in modeling, simulation and analysis of aircraft electric propulsion system. The ideal candidate should have solid backgrounds in multi-physics modeling and simulation of aircraft electrical propulsion system. Demonstrated experience in modeling and simulation software/language such as Modelica or Simscape is a necessity. Knowledge and experience of NPSS, aircraft dynamics, and aerodynamics is a definite plus. Senior Ph.D. students in aerospace and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • SP1542: Research in Computational Sensing

      The Computational Sensing team at MERL is seeking motivated and qualified individuals to assist in the development of computational methods for a variety of sensing applications. 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, learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/sonar imaging, sensing of dynamical systems, or wave-based inversion. Experience with experimentally measured data is desirable. 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.


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  • Openings


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  • Recent Publications

    •  Ravikumar, S., Quirynen, R., Bhagat, A., Zeino, E., Di Cairano, S., "Mixed-integer Programming for Centralized Coordination of Connected and Automated Vehicles in Dynamic Environment", IEEE Conference on Control Technology and Applications (CCTA), August 2021.
      BibTeX TR2021-089 PDF
      • @inproceedings{Ravikumar2021aug,
      • author = {Ravikumar, Shreejith and Quirynen, Rien and Bhagat, Akshay and Zeino, Eyad and Di Cairano, Stefano},
      • title = {Mixed-integer Programming for Centralized Coordination of Connected and Automated Vehicles in Dynamic Environment},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
      • year = 2021,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2021-089}
      • }
    •  Greiff, M., Berntorp, K., Di Cairano, S., Kim, K.J., "Mixed-Integer Linear Regression Kalman Filters for GNSS Positioning", IEEE Conference on Control Technology and Applications (CCTA), August 2021.
      BibTeX TR2021-090 PDF
      • @inproceedings{Greiff2021aug,
      • author = {Greiff, Marcus and Berntorp, Karl and Di Cairano, Stefano and Kim, Kyeong Jin},
      • title = {Mixed-Integer Linear Regression Kalman Filters for GNSS Positioning},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
      • year = 2021,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2021-090}
      • }
    •  Menner, M., Berntorp, K., Di Cairano, S., "A Kalman Filter for Online Calibration of Optimal Controllers", IEEE Conference on Control Technology and Applications (CCTA), August 2021.
      BibTeX TR2021-091 PDF
      • @inproceedings{Menner2021aug,
      • author = {Menner, Marcel and Berntorp, Karl and Di Cairano, Stefano},
      • title = {A Kalman Filter for Online Calibration of Optimal Controllers},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
      • year = 2021,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2021-091}
      • }
    •  Quirynen, R., Berntorp, K., "Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC", IFAC Conference on Nonlinear Model Predictive Control, July 2021.
      BibTeX TR2021-084 PDF
      • @inproceedings{Quirynen2021jul,
      • author = {Quirynen, Rien and Berntorp, Karl},
      • title = {Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC},
      • booktitle = {IFAC Conference on Nonlinear Model Predictive Control},
      • year = 2021,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2021-084}
      • }
    •  Aguilar Marsillach, D., Di Cairano, S., Kalabic, U., Weiss, A., "Fail-Safe Spacecraft Rendezvous on Near-Rectilinear Halo Orbits", American Control Conference (ACC), DOI: 10.23919/​ACC50511.2021.9483328, May 2021, pp. 2980-2985.
      BibTeX TR2021-054 PDF
      • @inproceedings{AguilarMarsillach2021may,
      • author = {Aguilar Marsillach, Daniel and Di Cairano, Stefano and Kalabic, Uros and Weiss, Avishai},
      • title = {Fail-Safe Spacecraft Rendezvous on Near-Rectilinear Halo Orbits},
      • booktitle = {American Control Conference (ACC)},
      • year = 2021,
      • pages = {2980--2985},
      • month = may,
      • publisher = {IEEE},
      • doi = {10.23919/ACC50511.2021.9483328},
      • issn = {2378-5861},
      • isbn = {978-1-6654-4197-1},
      • url = {https://www.merl.com/publications/TR2021-054}
      • }
    •  Berntorp, K., Chakrabarty, A., Di Cairano, S., "Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter", American Control Conference (ACC), May 2021.
      BibTeX TR2021-058 PDF
      • @inproceedings{Berntorp2021may,
      • author = {Berntorp, Karl and Chakrabarty, Ankush and Di Cairano, Stefano},
      • title = {Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter},
      • booktitle = {American Control Conference (ACC)},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-058}
      • }
    •  Berntorp, K., Quirynen, R., Vaskov, S., "Joint Tire-Stiffness and Vehicle-Inertial Parameter Estimation for Improved Predictive Control", American Control Conference, May 2021.
      BibTeX TR2021-060 PDF
      • @inproceedings{Berntorp2021may2,
      • author = {Berntorp, Karl and Quirynen, Rien and Vaskov, Sean},
      • title = {Joint Tire-Stiffness and Vehicle-Inertial Parameter Estimation for Improved Predictive Control},
      • booktitle = {American Control Conference},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-060}
      • }
    •  Kalur, A., Nabi, S., Benosman, M., "Robust Adaptive Dynamic Mode Decomposition for Reduce Order Modelling of Partial Differential Equations", American Control Conference (ACC), DOI: 10.23919/​ACC50511.2021.9483319, May 2021, pp. 4497-4502.
      BibTeX TR2021-059 PDF
      • @inproceedings{Kalur2021may,
      • author = {Kalur, Aniketh and Nabi, Saleh and Benosman, Mouhacine},
      • title = {Robust Adaptive Dynamic Mode Decomposition for Reduce Order Modelling of Partial Differential Equations},
      • booktitle = {American Control Conference (ACC)},
      • year = 2021,
      • pages = {4497--4502},
      • month = may,
      • publisher = {IEEE},
      • doi = {10.23919/ACC50511.2021.9483319},
      • issn = {2378-5861},
      • isbn = {978-1-7281-9704-3},
      • url = {https://www.merl.com/publications/TR2021-059}
      • }
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