MERL - Mitsubishi Electric Research Laboratories

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.

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  • Researchers

  • Awards

    •  AWARD    Marcus Greiff receives Outstanding Student Paper Award at CCTA 2022
      Date: August 25, 2022
      Awarded to: Marcus Greiff
      MERL Contact: Marcus Greiff
      Research Areas: Control, Dynamical Systems, Robotics
      Brief
      • Marcus Greiff, a Visiting Research Scientist at MERL, was awarded one of three outstanding student paper awards at the IEEE CCTA 2022 conference for his paper titled "Quadrotor Control on SU(2)xR3 with SLAM Integration". The award was given for originality, clarity, and potential impact on practical applications of control. The work presents a complete UAV control system design, facilitating autonomous supermarket inventorying without the need for external motion capture systems. A video of the experiments is on YouTube, including both simulations and real-time examples.
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  • News & Events

    •  NEWS    Ankush Chakrabarty co-organized three sessions at the ACC2023, and was nominated for Best Energy Systems Paper.
      Date: June 30, 2023 - June 2, 2023
      Where: San Diego, CA
      MERL Contact: Ankush Chakrabarty
      Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
      Brief
      • Ankush Chakrabarty (researcher, Multiphysical Systems Team) co-organized and spoke at 3 sessions at the 2023 American Control Conference in San Diego, CA. These include: (1) A tutorial session (w/ Stefano Di Cairano) on "Physics Informed Machine Learning for Modeling and Control": an effort with contributions from multiple academic institutes and US research labs; (2) An invited session on "Energy Efficiency in Smart Buildings and Cities" in which his paper (w/ Chris Laughman) on "Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems" was nominated for Best Energy Systems Paper Award; and, (3) A special session on Diversity, Equity, and Inclusion to improve recruitment and retention of underrepresented groups in STEM research.
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    •  TALK    [MERL Seminar Series 2023] Dr. Michael Muehlebach presents talk titled Learning and Dynamical Systems
      Date & Time: Tuesday, April 11, 2023; 11:00 AM
      Speaker: Michael Muehlebach, Max Planck Institute for Intelligent Systems
      MERL Host: Marcel Menner
      Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
      Abstract
      • The talk will be divided into two parts. The first part of the talk introduces a class of first-order methods for constrained optimization that are based on an analogy to non-smooth dynamical systems. The key underlying idea is to express constraints in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. This results is a simplified suite of algorithms and an expanded range of possible applications in machine learning. In the second part of my talk, I will present a robot learning algorithm for trajectory tracking. The method incorporates prior knowledge about the system dynamics and by optimizing over feedforward actions, the risk of instability during deployment is mitigated. The algorithm will be evaluated on a ping-pong playing robot that is actuated by soft pneumatic muscles.
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  • Internships

    • CA1940: Autonomous vehicle planning and contro in uncertain environments

      MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

    • CA2028: Mobile robots sensing, planning, and control

      MERL is seeking a highly motivated intern to collaborate in the development and experimental validation of sensing, planning, and control methods in various robotic testbeds (quadrotors, turtlebots, and mini-cars) at MERL. The ideal candidate is enrolled in a Masters/PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in motion planning, control, optimization, computer vision, and their application in mobile robots, including experimental validation. The successful candidate is proficient in ROS, C/C++, and Python, and at least familiar with MATLAB. The expected duration of the internship is 6 months with a flexible start date in the late Fall/Winter 2023.

    • 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.


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  • Recent Publications

    •  Vaskov, S., Quirynen, R., Menner, M., Berntorp, K., "Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles", Vehicle System Dynamics, May 2023.
      BibTeX TR2023-064 PDF
      • @article{Vaskov2023may2,
      • author = {Vaskov, Sean and Quirynen, Rien and Menner, Marcel and Berntorp, Karl},
      • title = {Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles},
      • journal = {Vehicle System Dynamics},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-064}
      • }
    •  Greiff, M., Vinod, A.P., Nabi, S., Cairano, S., "Quadrotor Motion Planning in Stochastic Wind Fields", American Control Conference (ACC), May 2023.
      BibTeX TR2023-056 PDF
      • @inproceedings{Greiff2023may,
      • author = {Greiff, Marcus and Vinod, Abraham P. and Nabi, Saleh and Cairano, Stefano},
      • title = {Quadrotor Motion Planning in Stochastic Wind Fields},
      • booktitle = {American Control Conference (ACC)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-056}
      • }
    •  Ngheim, T.X., Drgona, J., Jones, C., Nagy, Z., Schwan, R., Dey, B., Chakrabarty, A., Di Cairano, S., Paulson, J.A., Carron, A., Zeilinger, M., Cortez, W.S., Vrabie, D.L., "Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems", American Control Conference (ACC), May 2023.
      BibTeX TR2023-052 PDF
      • @inproceedings{Ngheim2023may,
      • author = {Ngheim, Truong X. and Drgona, Jan and Jones, Colin and Nagy, Zoltan and Schwan, Roland and Dey, Biswadip and Chakrabarty, Ankush and Di Cairano, Stefano and Paulson, Joel A. and Carron, Andrea and Zeilinger, Melanie and Cortez, Wenceslaw S. and Vrabie, Draguna L.},
      • title = {Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems},
      • booktitle = {American Control Conference (ACC)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-052}
      • }
    •  Skibik, T., Vinod, A.P., Weiss, A., Di Cairano, S., "MPC with Integrated Evasive Maneuvers for Failure-safe Automated Driving", American Control Conference (ACC), May 2023.
      BibTeX TR2023-055 PDF
      • @inproceedings{Skibik2023may,
      • author = {Skibik, Terrence and Vinod, Abraham P. and Weiss, Avishai and Di Cairano, Stefano},
      • title = {MPC with Integrated Evasive Maneuvers for Failure-safe Automated Driving},
      • booktitle = {American Control Conference (ACC)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-055}
      • }
    •  Suriyarachchi, N., Quirynen, R., Baras, J.S., Di Cairano, S., ,, "Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Environments", American Control Conference (ACC), May 2023.
      BibTeX TR2023-059 PDF
      • @inproceedings{Suriyarachchi2023may,
      • author = {Suriyarachchi, Nilesh and Quirynen, Rien and Baras, John S. and Di Cairano, Stefano and},
      • title = {Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Environments},
      • booktitle = {American Control Conference (ACC)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-059}
      • }
    •  Vinod, A.P., Di Cairano, S., "Sample quantile-based programming for non-convex separable chance constraints", American Control Conference (ACC), May 2023.
      BibTeX TR2023-062 PDF
      • @inproceedings{Vinod2023may,
      • author = {Vinod, Abraham P. and Di Cairano, Stefano},
      • title = {Sample quantile-based programming for non-convex separable chance constraints},
      • booktitle = {American Control Conference (ACC)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-062}
      • }
    •  You, S., Greiff, M., Quirynen, R., Ran, S., Wang, Y., Berntorp, K., Dai, R., Di Cairano, S., "Integral Action NMPC for Tight Maneuvers of Articulated Vehicles", American Control Conference (ACC), May 2023.
      BibTeX TR2023-058 PDF
      • @inproceedings{You2023may,
      • author = {You, Sixiong and Greiff, Marcus and Quirynen, Rien and Ran, Shuangxuan and Wang, Yebin and Berntorp, Karl and Dai, Ran and Di Cairano, Stefano},
      • title = {Integral Action NMPC for Tight Maneuvers of Articulated Vehicles},
      • booktitle = {American Control Conference (ACC)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-058}
      • }
    •  Mowlavi, S., Benosman, M., Nabi, S., "Reinforcement Learning-based Estimation for Partial Differential Equations", SIAM Conference on Applications of Dynamical Systems, May 2023.
      BibTeX TR2023-066 PDF
      • @inproceedings{Mowlavi2023may,
      • author = {Mowlavi, Saviz and Benosman, Mouhacine and Nabi, Saleh},
      • title = {Reinforcement Learning-based Estimation for Partial Differential Equations},
      • booktitle = {SIAM Conference on Applications of Dynamical Systems},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-066}
      • }
    See All Publications for Dynamical Systems

  • Videos