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   Stefano Di Cairano Appointed Inaugural Chair of IEEE CSS Technology Conference Editorial Board
      Date: December 12, 2019
      MERL Contact: Stefano Di Cairano
      Research Areas: Control, Dynamical Systems, Robotics
      Brief
      • Stefano Di Cairano has been appointed inaugural chair of the IEEE CSS Technology Conference Editorial Board. In this role Stefano will coordinate the creation and maintenance of the Editorial Board, and will coordinate the editorial board activities supporting the IEEE CCTA conference series, including manuscript assignment to associate editors, monitoring of the manuscript assessment, and program finalization with the conference program chairs. Stefano will also work with the other IEEE CSS Editorial Board Chairs and IEEE CSS Leadership to ensure the quality and improve the processes of IEEE CSS publications.
    •  
    •  NEWS   Ankush Chakrabarty gave an invited talk on machine learning for constrained control at AI for Engineering in Toronto
      Date: August 19, 2019 - August 23, 2019
      Where: AI for Engineering Summer School 2019
      MERL Contact: Ankush Chakrabarty
      Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning
      Brief
      • Ankush Chakrabarty, a Visiting Research Scientist in MERL's Control and Dynamical Systems group, gave an invited talk at the AI for Engineering Summer School 2019 hosted by Autodesk. The talk briefly described MERL's research areas, and focused on Dr. Chakrabarty's work at MERL (with collaborators from the CD and DA group) on the use of supervised learning for verification of control systems with simulators/neural nets in the loop, and on constraint-enforcing reinforcement learning. Other speakers at the event included researchers from various academic and industrial research facilities including U Toronto, UW-Seattle, Carnegie Mellon U, the Vector Institute, and the Montreal Institute for Learning Algorithms.
    •  

    See All News & Events for Dynamical Systems
  • Internships

    • CD1383: Collaborative Estimation for Robotic Manipulators

      MERL is seeking a highly skilled and self-motivated intern to conduct research on condition monitoring for robotic manipulators. The ideal candidate should have solid backgrounds in robotic manipulators, stochastic estimation methods for dynamical systems, and collaborative strategies over multi-agents. Experience of applying machine learning to dynamical systems is a strong plus. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.

    • SP1430: WiFi Sensing

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in wireless sensing using communication signals such as 5G, WiFi, and Bluetooth. Previous experience on occupancy sensing, people counting, localization, device-free pose/gesture recognition with machine learning approaches is highly preferred. Familiarity with IEEE 802.11 (ac/ad/ay)standards is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, collect real-world channel measurements, and prepare results for publication. Senior Ph.D. students with research focuses on wireless communications, machine learning, signal processing, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.

    • MP1406: Numerical Analysis of Electric Machines

      MERL is seeking a motivated and qualified intern to conduct research in the design, modeling and optimization of electrical machines. The ideal candidate should have solid backgrounds in electromagnetic theory, electric machine design, and numerical modeling techniques (including model reduction), research experiences in electric, magnetic, and thermal modeling and analysis of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and optimization techniques is a strong plus. Senior Ph.D. students in electrical or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.


    See All Internships for Dynamical Systems
  • Openings


    See All Openings at MERL
  • Recent Publications

    •  Muralidharan, V., Weiss, A., Kalabic, U., "Control Strategy for Long-Term Station-Keeping on Near-Rectilinear Halo Orbits", AAS/AIAA Space Flight Mechanics Meeting, DOI: 10.2514/6.2020-1459, January 2020.
      BibTeX Download PDFAbout TR2020-006
      • @inproceedings{Muralidharan2020jan,
      • author = {Muralidharan, Vivek and Weiss, Avishai and Kalabic, Uros},
      • title = {Control Strategy for Long-Term Station-Keeping on Near-Rectilinear Halo Orbits},
      • booktitle = {AAS/AIAA Space Flight Mechanics Meeting},
      • year = 2020,
      • month = jan,
      • doi = {10.2514/6.2020-1459},
      • url = {https://www.merl.com/publications/TR2020-006}
      • }
    •  Berntorp, K., Quirynen, R., Uno, T., Di Cairano, S., "Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control", Journal of Vehicle Systems Dynamics, January 2020.
      BibTeX Download PDFAbout TR2020-005
      • @article{Berntorp2020jan,
      • author = {Berntorp, Karl and Quirynen, Rien and Uno, Tomoki and Di Cairano, Stefano},
      • title = {Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control},
      • journal = {Journal of Vehicle Systems Dynamics},
      • year = 2020,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2020-005}
      • }
    •  Romero, O., Benosman, M., "Finite-Time Convergence of Continuous-Time Optimization Algorithms via Differential Inclusions", Advances in Neural Information Processing Systems (NIPS)- Workshop, January 2020.
    •  Chu, T., Kalabić, U., "Model-based deep reinforcement learning for CACC in mixed-autonomy vehicle platoons", IEEE Conference on Decision and Control (CDC), December 2019.
      BibTeX Download PDFAbout TR2019-142
      • @inproceedings{Chu2019dec,
      • author = {Chu, Tianshu and Kalabić, Uroš},
      • title = {Model-based deep reinforcement learning for CACC in mixed-autonomy vehicle platoons},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2019,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2019-142}
      • }
    •  Nabi, S., Grover, P., Caulfield, C., "Nonlinear Optimal Control Strategies for Buoyancy-Driven Flows in the Built Environment", Journal of Computers and Fluids, DOI: 10.1016/j.compfluid.2019.104313, Vol. 194, No. 104313, December 2019.
      BibTeX Download PDFAbout TR2019-151
      • @article{Nabi2019dec,
      • author = {Nabi, Saleh and Grover, Piyush and Caulfield, Colm},
      • title = {Nonlinear Optimal Control Strategies for Buoyancy-Driven Flows in the Built Environment},
      • journal = {Journal of Computers and Fluids},
      • year = 2019,
      • volume = 194,
      • number = 104313,
      • month = dec,
      • doi = {10.1016/j.compfluid.2019.104313},
      • url = {https://www.merl.com/publications/TR2019-151}
      • }
    •  Nabi, S., Nishio, N., Grover, P., Matai, R., Kajiyama, Y., Kotake, N., Kameyama, S., Yoshiki, W., Iida, M., "Improving LiDAR performance on complex terrain using CFD-based correction and direct-adjoint-loop optimization", Journal of physics, November 2019.
      BibTeX Download PDFAbout TR2019-152
      • @article{Nabi2019nov,
      • author = {Nabi, S. and Nishio, N. and Grover, P. and Matai, R. and Kajiyama, Y. and Kotake, N. and Kameyama, S. and Yoshiki, W. and Iida, M.},
      • title = {Improving LiDAR performance on complex terrain using CFD-based correction and direct-adjoint-loop optimization},
      • journal = {Journal of physics},
      • year = 2019,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2019-152}
      • }
    •  Nabi, S., Grover, P., "Improving LiDAR performance on a complex terrain using CFD-based correction and direct-adjoint-loop optimization", NAWEA/WindTech Conference, October 2019.
      BibTeX Download PDFAbout TR2019-130
      • @inproceedings{Nabi2019oct,
      • author = {Nabi, Saleh and Grover, Piyush},
      • title = {Improving LiDAR performance on a complex terrain using CFD-based correction and direct-adjoint-loop optimization},
      • booktitle = {NAWEA/WindTech Conference},
      • year = 2019,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2019-130}
      • }
    •  Benosman, M., Borggaard, J., "Robust Nonlinear State Estimation for a Class of Infinite-Dimensional Systems Using Reduced-Order Models", Automatica, September 2019.
      BibTeX Download PDFAbout TR2019-111
      • @article{Benosman2019sep,
      • author = {Benosman, Mouhacine and Borggaard, Jeff},
      • title = {Robust Nonlinear State Estimation for a Class of Infinite-Dimensional Systems Using Reduced-Order Models},
      • journal = {Automatica},
      • year = 2019,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2019-111}
      • }
    See All Publications for Dynamical Systems
  • Videos