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

  • Awards

    •  AWARD    MERL’s Paper on Wi-Fi Sensing Earns Top 3% Paper Recognition at ICASSP 2023, Selected as a Best Student Paper Award Finalist
      Date: June 9, 2023
      Awarded to: Cristian J. Vaca-Rubio, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Petros Boufounos and Petar Popovski
      MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
      Brief
      • A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.

        Performed during Cristian’s stay at MERL first as a visiting Marie Skłodowska-Curie Fellow and then as a full-time intern in 2022, this work capitalizes on standards-compliant Wi-Fi signals to perform indoor localization and sensing. The paper uses a neural dynamic learning framework to address technical issues such as low sampling rate and irregular sampling intervals.

        ICASSP, a flagship conference of the IEEE Signal Processing Society (SPS), was hosted on the Greek island of Rhodes from June 04 to June 10, 2023. ICASSP 2023 marked the largest ICASSP in history, boasting over 4000 participants and 6128 submitted papers, out of which 2709 were accepted.
    •  

    See All Awards for MERL
  • News & Events

    •  NEWS    Karl Berntorp joins the Editorial Board of IEEE Transactions on Control Systems Technology
      Date: December 7, 2023
      MERL Contact: Karl Berntorp
      Research Areas: Control, Dynamical Systems
      Brief
      • Karl Berntorp has joined the Editorial Board of the IEEE Transactions on Control Systems Technology (T-CST) as an Associate Editor. The IEEE T-CST publishes peer-reviewed papers on technological advances in the design, realization, and operation of control systems, and bridges the gap between the theory and practice of control engineering.
    •  
    •  TALK    [MERL Seminar Series 2023] Gioele Zardini presents talk titled Co-Design of Complex Systems: From Autonomy to Future Mobility
      Date & Time: Tuesday, November 21, 2023; 11:00 AM
      Speaker: Gioele Zardini, ETH Zürich and MIT
      MERL Host: Karl Berntorp
      Research Areas: Control, Dynamical Systems
      Abstract
      • When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management policies. Optimally co-designing sociotechnical systems is a complex task for at least two reasons. On one hand, the co-design of interconnected systems (e.g., large networks of cyber-physical systems) involves the simultaneous choice of components arising from heterogeneous natures (e.g., hardware vs. software parts) and fields, while satisfying systemic constraints and accounting for multiple objectives. On the other hand, components are connected via collaborative and conflicting interactions between different stakeholders (e.g., within an intermodal mobility system). In this talk, I will present a framework to co-design complex systems, leveraging a monotone theory of co-design and tools from game theory. The framework will be instantiated in the task of designing future mobility systems, all the way from the policies that a city can design, to the autonomy of vehicles part of an autonomous mobility-on-demand service. Through various case studies, I will show how the proposed approaches allow one to efficiently answer heterogeneous questions, unifying different modeling techniques and promoting interdisciplinarity, modularity, and compositionality. I will then discuss open challenges for compositional systems design optimization, and present my agenda to tackle them.
    •  

    See All News & Events for Dynamical Systems
  • Internships

    • ST2082: Integrated Sensing and Communication (ISAC)

      The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in integrated sensing and communication (ISAC) with a focus on signal processing, model-based learning, and optimization. Expertise in joint waveform/sequence optimization, integrated ISAC precoder/combiner design, model-based learning for ISAC, and downlink/uplink/active sensing under timing and frequency offsets is highly desired. Familiarity with IEEE 802.11 (ac/ax/ad/ay) standards is a plus but not required. 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 publication. The expected duration of the internship is 3 months with a flexible start date.

    • ST2025: Background Oriented Schlieren Tomography

      The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented Schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements. 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, large-scale optimization, learning-based modeling for imaging, Schlieren tomography, physics informed neural networks. 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.

    • CA2131: Collaborative Legged Robots

      MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on control and planning algorithms for legged robots for support activities of and collaboration with humans. The ideal candidate is expected to be working towards a PhD with strong emphasis in robotics control and planning and to have interest and background in as many as possible of: motion planning algorithms, control for legged robot locomotions, legged robots, perception and sensing with multiple sensors, SLAM, vision-based control. Good programming skills in Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.


    See All Internships for Dynamical Systems
  • Openings


    See All Openings at MERL
  • Recent Publications

    •  Srinivas, N., Vinod, A.P., Di Cairano, S., Weiss, A., "Lunar Landing with Feasible Divert using Controllable Sets", AIAA SciTech, January 2024.
      BibTeX TR2024-004 PDF
      • @inproceedings{Srinivas2024jan,
      • author = {Srinivas, Neeraj and Vinod, Abraham P. and Di Cairano, Stefano and Weiss, Avishai},
      • title = {Lunar Landing with Feasible Divert using Controllable Sets},
      • booktitle = {AIAA SciTech},
      • year = 2024,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2024-004}
      • }
    •  Bonzanini, A.D., Mesbah, A., Di Cairano, S., "Perception-Aware Model Predictive Control for Constrained Control in Unknown Environments", Automatica, December 2023.
      BibTeX TR2023-147 PDF
      • @article{Bonzanini2023dec,
      • author = {Bonzanini, Angelo Domenico and Mesbah, Ali and Di Cairano, Stefano},
      • title = {Perception-Aware Model Predictive Control for Constrained Control in Unknown Environments},
      • journal = {Automatica},
      • year = 2023,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2023-147}
      • }
    •  Mowlavi, S., Benosman, M., "Dual Parametric and State Estimation for Partial Differential Equations", IEEE Conference on Decision and Control, DOI: 10.1109/​CDC49753.2023.10384246, December 2023, pp. 8156-8161.
      BibTeX TR2023-145 PDF
      • @inproceedings{Mowlavi2023dec,
      • author = {Mowlavi, Saviz and Benosman, Mouhacine},
      • title = {Dual Parametric and State Estimation for Partial Differential Equations},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2023,
      • pages = {8156--8161},
      • month = dec,
      • publisher = {IEEE},
      • doi = {10.1109/CDC49753.2023.10384246},
      • issn = {2576-2370},
      • isbn = {979-8-3503-0125-0},
      • url = {https://www.merl.com/publications/TR2023-145}
      • }
    •  Queeney, J., Benosman, M., "Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning", Advances in Neural Information Processing Systems (NeurIPS), December 2023.
      BibTeX TR2023-143 PDF
      • @inproceedings{Queeney2023dec,
      • author = {Queeney, James and Benosman, Mouhacine},
      • title = {Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2023,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2023-143}
      • }
    •  Greiff, M., Di Cairano, S., Kim, K.J., Berntorp, K., "A System-Level Cooperative Multi-Agent GNSS Positioning Solution", IEEE Transactions on Control Systems Technology, DOI: 10.1109/​TCST.2023.3307339, Vol. 32, No. 1, pp. 158-173, October 2023.
      BibTeX TR2023-135 PDF
      • @article{Greiff2023oct,
      • author = {Greiff, Marcus and Di Cairano, Stefano and Kim, Kyeong Jin and Berntorp, Karl},
      • title = {A System-Level Cooperative Multi-Agent GNSS Positioning Solution},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2023,
      • volume = 32,
      • number = 1,
      • pages = {158--173},
      • month = oct,
      • doi = {10.1109/TCST.2023.3307339},
      • url = {https://www.merl.com/publications/TR2023-135}
      • }
    •  Sholokhov, A., Liu, Y., Mansour, H., Nabi, S., "Physics-Informed Neural ODE (PINODE): Embedding Physics into Models using Collocation Points", Nature Scientific Reports, October 2023.
      BibTeX TR2023-136 PDF
      • @article{Sholokhov2023oct,
      • author = {Sholokhov, Aleksei and Liu, Yuying and Mansour, Hassan and Nabi, Saleh},
      • title = {Physics-Informed Neural ODE (PINODE): Embedding Physics into Models using Collocation Points},
      • journal = {Nature Scientific Reports},
      • year = 2023,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2023-136}
      • }
    •  Shimane, Y., Miraldo, P., Berntorp, K., Greiff, M., Elango, P., Weiss, A., "High-Fidelity Simulation of Horizon-Based Optical Navigation with Open-Source Software", International Astronautical Congress (IAC), October 2023.
      BibTeX TR2023-128 PDF
      • @inproceedings{Shimane2023oct,
      • author = {Shimane, Yuri and Miraldo, Pedro and Berntorp, Karl and Greiff, Marcus and Elango, Purnanand and Weiss, Avishai},
      • title = {High-Fidelity Simulation of Horizon-Based Optical Navigation with Open-Source Software},
      • booktitle = {International Astronautical Congress (IAC)},
      • year = 2023,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2023-128}
      • }
    •  Quirynen, R., Di Cairano, S., "Tailored Presolve Techniques in Branch-and-Bound Method for Fast Mixed-Integer Optimal Control Applications", Optimal Control Applications and Methods, DOI: 10.1002/​oca.3030, Vol. 44, No. 6, pp. 3139-3167, August 2023.
      BibTeX TR2023-110 PDF
      • @article{Quirynen2023aug2,
      • author = {Quirynen, Rien and Di Cairano, Stefano},
      • title = {Tailored Presolve Techniques in Branch-and-Bound Method for Fast Mixed-Integer Optimal Control Applications},
      • journal = {Optimal Control Applications and Methods},
      • year = 2023,
      • volume = 44,
      • number = 6,
      • pages = {3139--3167},
      • month = aug,
      • doi = {10.1002/oca.3030},
      • url = {https://www.merl.com/publications/TR2023-110}
      • }
    See All Publications for Dynamical Systems
  • Videos