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

    • CA1531: Learning-based multi-agent motion planning

      MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • SP1512: Mutual Interference Mitigation

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in mutual interference mitigation for automotive radar. Previous experience in waveform design, radar detection under interference, joint communication and sensing, interference mitigation, and deep learning for radar is highly preferred. Knowledge about automotive radar schemes (MIMO and waveform modulation, e.g., FMCW, PMCW, and OFDM) is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, 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.

    • 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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  • Recent Publications

    •  Hayashi, N., Weiss, A., Di Cairano, S., "Model Predictive Control Approach for Autonomous Sun-Synchronous Sub-Recurrent Orbit Control", AIAA SciTech, January 2021.
      BibTeX TR2021-005 PDF
      • @inproceedings{Hayashi2021jan,
      • author = {Hayashi, Naohiro and Weiss, Avishai and Di Cairano, Stefano},
      • title = {Model Predictive Control Approach for Autonomous Sun-Synchronous Sub-Recurrent Orbit Control},
      • booktitle = {AIAA SciTech},
      • year = 2021,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2021-005}
      • }
    •  Poveda, J., Benosman, M., Vamvoudakis, K., "Data-Enabled Extremum Seeking: A Cooperative Concurrent Learning-Based Approach", International journal of adaptive control and signal processing, December 2020.
      BibTeX TR2020-180 PDF
      • @article{Poveda2020dec,
      • author = {Poveda, Jorge and Benosman, Mouhacine and Vamvoudakis, Kyriakos},
      • title = {Data-Enabled Extremum Seeking: A Cooperative Concurrent Learning-Based Approach},
      • journal = {International journal of adaptive control and signal processing},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-180}
      • }
    •  Aguilar Marsillach, D., Di Cairano, S., Weiss, A., "Abort-Safe Spacecraft Rendezvous in case of Partial Thrust Failure", IEEE Conference on Decision and Control (CDC), December 2020.
      BibTeX TR2020-175 PDF
      • @inproceedings{AguilarMarsillach2020dec,
      • author = {Aguilar Marsillach, Daniel and Di Cairano, Stefano and Weiss, Avishai},
      • title = {Abort-Safe Spacecraft Rendezvous in case of Partial Thrust Failure},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-175}
      • }
    •  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}
      • }
    •  Lin, C., Sels, D., Ma, Y., Wang, Y., "Stochastic optimal control formalism for an open quantum system", Physical Review, DOI: 10.1103/PhysRevA.102.052605, Vol. 102, pp. 052605, December 2020.
      BibTeX TR2020-163 PDF
      • @article{Lin2020dec,
      • author = {Lin, Chungwei and Sels, Dries and Ma, Yanting and Wang, Yebin},
      • title = {Stochastic optimal control formalism for an open quantum system},
      • journal = {Physical Review},
      • year = 2020,
      • volume = 102,
      • pages = 052605,
      • month = dec,
      • doi = {10.1103/PhysRevA.102.052605},
      • url = {https://www.merl.com/publications/TR2020-163}
      • }
    •  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}
      • }
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