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   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.
    •  
    •  NEWS   MERL researcher Stefano Di Cairano taught short course for European Embedded Control Institute.
      Date: June 10, 2019 - June 14, 2019
      Where: Paris
      MERL Contact: Stefano Di Cairano
      Research Areas: Control, Dynamical Systems, Optimization
      Brief
      • MERL researcher Stefano Di Cairano and Prof. Ilya Kolmanovsky, Dept. Aerospace Engineering, the University of Michigan, were invited to teach a class on "Predictive and Optimization Based Control for Automotive and Aerospace Application" at the 2019 International Graduate School in Control, of the European Embedded Control Institute (EECI). Every year EECI invites world renown experts to teach 21-hours class modules, mostly for PhD students but also for professionals, on selected control subjects. Stefano and Ilya's class was attended by 30 "students" from both academia and industry, from all around the world, interested in automotive and aerospace control. The module described the fundamentals of modeling and control design in automotive and aerospace through lectures, real world examples and exercises, and placed particular emphasis on techniques such as MPC, reference governors, and optimal control.
    •  

    See All News & Events for Dynamical Systems
  • Internships

    • CD1388: Mixed-Integer Optimal Control Algorithms

      MERL is looking for highly motivated individuals to work on efficient numerical algorithms and applications of mixed-integer optimal control methods. The research will involve some among the following: the study and development of mixed-integer optimization techniques for optimal control, the implementation and validation of algorithms for relevant control applications. The ideal candidate should have experience in branch-and-bound methods and presolve techniques for mixed-integer optimization and/or model predictive control. PhD students in engineering or mathematics with a focus on mixed-integer optimization or numerical optimal control are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; coding parts of the algorithms in C/C++ is a big plus. The expected duration of the internship is 3-6 months and the start date is flexible.

    • 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.

    • SP1371: Object Tracking and Perception for Autonomous Driving

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in automotive radar-based object tracking and perception for autonomous driving. Previous experience on multiple (point and extended) object tracking, data association, and data-driven object detection/tracking is highly preferred. Knowledge about automotive radar schemes (MIMO array and waveform modulation (FMCW, PMCW, and OFDM)) and hands-on experience on open automotive datasets are a plus. Knowledge on vehicle dynamics is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, conduct field measurements, data analysis (Python & MATLAB), 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.


    See All Internships for Dynamical Systems
  • Openings


    See All Openings at MERL
  • Recent Publications

    •  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}
      • }
    •  Poveda, J., Vamvoudakis, K., Benosman, M., "Real-Time Optimization: A Memory-based Concurrent Extremum Seeking Approach", IFAC Nonlinear Control Systems (NOLCOS), September 2019.
      BibTeX Download PDFAbout TR2019-095
      • @inproceedings{Poveda2019sep,
      • author = {Poveda, Jorge and Vamvoudakis, Kyriakos and Benosman, Mouhacine},
      • title = {Real-Time Optimization: A Memory-based Concurrent Extremum Seeking Approach},
      • booktitle = {IFAC Nonlinear Control Systems (NOLCOS)},
      • year = 2019,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2019-095}
      • }
    •  Berntorp, K., Danielson, C., Weiss, A., Di Cairano, S., Erliksson, K., Bai, R., "Positive Invariant Sets for Safe Integrated Vehicle Motion Planning and Control", Transactions on intelligent vehicles, August 2019.
      BibTeX Download PDFAbout TR2019-086
      • @article{Berntorp2019aug,
      • author = {Berntorp, Karl and Danielson, Claus and Weiss, Avishai and Di Cairano, Stefano and Erliksson, Karl and Bai, Richard},
      • title = {Positive Invariant Sets for Safe Integrated Vehicle Motion Planning and Control},
      • journal = {Transactions on intelligent vehicles},
      • year = 2019,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2019-086}
      • }
    •  Wei, C., Benosman, M., Kim, T., "Online Parameter Identification for State of Power Prediction of Lithiumion Batteries in Electric Vehicles Using Extremum Seeking", International Journal of Control, Automation and Systems, August 2019.
      BibTeX Download PDFAbout TR2019-085
      • @article{Wei2019aug,
      • author = {Wei, Chun and Benosman, Mouhacine},
      • title = {Online Parameter Identification for State of Power Prediction of Lithiumion Batteries in Electric Vehicles Using Extremum Seeking},
      • journal = {International Journal of Control, Automation and Systems},
      • year = 2019,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2019-085}
      • }
    •  Tian, N., Fang, H., Wang, Y., "Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective", American Control Conference (ACC), July 2019.
      BibTeX Download PDFAbout TR2019-065
      • @inproceedings{Tian2019jul,
      • author = {Tian, Ning and Fang, Huazhen and Wang, Yebin},
      • title = {Parameter Identification of the Nonlinear Double-Capacitor Model for Lithium-Ion Batteries: From the Wiener Perspective},
      • booktitle = {American Control Conference (ACC)},
      • year = 2019,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2019-065}
      • }
    •  Romeres, D., Jha, D., Dalla Libera, A., Yerazunis, W.S., Nikovski, D.N., "Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze", IEEE International Conference on Robotics and Automation (ICRA), May 2019.
      BibTeX Download PDFAbout TR2019-028
      • @inproceedings{Romeres2019may,
      • author = {Romeres, Diego and Jha, Devesh and Dalla Libera, Alberto and Yerazunis, William S. and Nikovski, Daniel N.},
      • title = {Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze},
      • booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
      • year = 2019,
      • month = may,
      • url = {https://www.merl.com/publications/TR2019-028}
      • }
    •  Bakshi, K., Grover, P., Theodorou, E., "On Mean Field Games for Agents with Langevin Dynamics", IEEE Transactions on Control of Network Systems, DOI: 10.1109/TCNS.2019.2896975, March 2019.
      BibTeX Download PDFAbout TR2018-200
      • @article{Bakshi2019mar,
      • author = {Bakshi, Kaivalya and Grover, Piyush and Theodorou, Evangelos},
      • title = {On Mean Field Games for Agents with Langevin Dynamics},
      • journal = {IEEE Transactions on Control of Network Systems},
      • year = 2019,
      • month = mar,
      • doi = {10.1109/TCNS.2019.2896975},
      • url = {https://www.merl.com/publications/TR2018-200}
      • }
    See All Publications for Dynamical Systems
  • Videos