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 University of Houston
      Date: April 22, 2021
      Where: Houston, Texas
      MERL Contact: Karl Berntorp
      Research Areas: Control, Dynamical Systems, Robotics, Signal Processing
      Brief
      • The invited seminar "System Design, Planning, and Control for Autonomous Driving" was part of the Distinguished Seminar series at the Department of Mechanical Engineering at the University of Houston, Houston, Tx. The invited lecture described MERL research related to the different system components involved in autonomous driving, with particular focus on motion-planning and predictive-control methods.
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    •  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.
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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.

    • CA1530: Hybrid Control of Cyberphysical Systems

      MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of hybrid control algorithms for cyberphysical system. The potential subjects include formal methods for control synthesis, control barrier-functions, stabilizing control for hybrid dynamical systems, and optimal control of hybrid dynamics. The ideal candidate is expected to be working towards a PhD with strong emphasis in control theory, and to have interest and background in as many as possible among: predictive control, Lyapunov stability, formal methods for control, constrained control, optimization, and machine learning. Good programming skills in MATLAB, and/or Python are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 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.

    • CA1646: Path Planning and Model Predictive Control for Autonomous Vehicles

      MERL is seeking highly motivated and qualified interns to collaborate on the implementation and experimental validation of algorithms for path/motion planning and optimization-based tracking control in autonomous vehicles. An ideal candidate should have experience in path planning and/or model predictive control (MPC) for autonomous vehicles, and the candidate should be familiar with Matlab and Simulink. Any experience with dSPACE (e.g., MicroAutoBox) or C/C++ code generation is a plus. Both MS and PhD students are welcome to apply. Start date for this internship is as soon as possible, and the expected 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.


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  • Recent Publications

    •  Aguilar Marsillach, D., Di Cairano, S., Kalabic, U., Weiss, A., "Fail-Safe Spacecraft Rendezvous on Near-Rectilinear Halo Orbits", American Control Conference (ACC), May 2021.
      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,
      • month = may,
      • 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), May 2021.
      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,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-059}
      • }
    •  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, DOI: https:/​/​doi.org/​10.2514/​6.2021-1953, 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,
      • publisher = {AIAA},
      • doi = {https://doi.org/10.2514/6.2021-1953},
      • 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), DOI: 10.1109/​CDC42340.2020.9303782, December 2020, pp. 1490-1495.
      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,
      • pages = {1490--1495},
      • month = dec,
      • publisher = {IEEE},
      • doi = {10.1109/CDC42340.2020.9303782},
      • url = {https://www.merl.com/publications/TR2020-175}
      • }
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  • Videos