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

    •  NEWS   Karl Berntorp gave an invited lecture at the Department of Electrical Engineering at Linköping University
      Date: April 6, 2021
      Where: Linköping University, Sweden
      MERL Contact: Karl Berntorp
      Research Areas: Control, Dynamical Systems, Robotics
      Brief
      • MERL researcher Karl Berntorp was invited to give a lecture in the ELLIIT PhD course "Motion Planning and Control" at the Division of Vehicular Systems, Department of Electrical Engineering, Linköping University. The course is open for Ph.D. students as well as senior undergraduate students, and covers both fundamental algorithms and state-of-the-art methods for motion planning and control. The invited lecture described MERL research on the use of invariant sets for safe motion planning and control, with application to autonomous vehicles.
    •  
    •  NEWS   Stefano Di Cairano joins the Editorial Board of IEEE Transactions on Intelligent Vehicles
      Date: March 7, 2021
      MERL Contact: Stefano Di Cairano
      Research Areas: Control, Dynamical Systems, Robotics
      Brief
      • Stefano Di Cairano has joined the Editorial Board of the IEEE Transactions on Intelligent Vehicles (T-IV) as an Associate Editor. The IEEE T-IV publishes peer-reviewed articles in the area of intelligent vehicles in a roadway environment, and in particular in automated vehicles. While primarily led by the IEEE ITS Society, IEEE T-IV is an IEEE multi-society journal.
        As Associate Editor Stefano will be responsible for the review process of some of the papers submitted to T-IV and will work with the Editorial Board to monitor the status and continuously strengthen the journal.
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  • Internships

    • 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.

    • CA1529: Energy Management for Electric Vehicles

      MERL is looking for a highly motivated intern to conduct research on data-driven energy management strategies for (hybrid) electric vehicles. The candidate will develop methods that use data, e.g., of human drivers or traffic conditions, in order to improve the control of electric vehicles. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, machine learning, statistical learning, numerical optimization, and (inverse) optimal control. Prior experience with (hybrid) electric vehicles is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. PhD students in engineering or mathematics with a focus on control theory or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. The expected duration of the internship is 3-6 months. 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.

    • 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.


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  • Recent Publications

    •  Chiu, M., Kalabic, U., "Short Paper: Debt Representation in UTXO Blockchains", Financial Cryptography and Data Security, February 2021.
      BibTeX TR2021-014 PDF
      • @inproceedings{Chiu2021feb,
      • author = {Chiu, Michael and Kalabic, Uros},
      • title = {Short Paper: Debt Representation in UTXO Blockchains},
      • booktitle = {Financial Cryptography and Data Security},
      • year = 2021,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2021-014}
      • }
    •  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}
      • }
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  • Videos