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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  • 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


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  • Internships

    • CA1707: Autonomous vehicles guidance and control

      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. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. 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, predictive control algorithms linear and nonlinear systems, motion planning, convex, non-convex, and mixed -integer optimization, statistical estimation, cooperative control. Good programming skills in MATLAB, Python or C/C++ are required, knowledge of rapid prototyping systems, automatic code generation or ROS is a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

    • CI1733: ML for GNSS-based Applications

      MERL is seeking a highly motivated, qualified intern to work on machine learning for Global Navigation Satellite System (GNSS) applications. The ideal candidate is working towards a PhD and is expected to develop innovative machine learning technologies to increase accuracy and integrity of GNSS-based positioning systems. Candidates should have strong knowledge about as many as possible of GNSS signal processing for multipath mitigation, handling RINEX data, neural network and learning techniques, such as feature extraction, deep machine learning, reinforcement learning, domain adaptation, and distributed learning. Proficient programming skills with PyTorch, Matlab, and C++, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • MS1866: Deep Unsupervised/Semi-Supervised Learning for Smart Buildings

      MERL is seeking a highly motivated and qualified intern to collaborate with the Multiphysical Systems (MS) team in research on unsupervised/semi-supervised learning using data from real building energy systems. The ideal candidate is expected to be working towards a Ph.D. in deep learning for time-series, with special interest in learning representations for deep clustering. Fluency in Python and either PyTorch/Tensorflow is required. Previous peer-reviewed publications in related research topics and/or experience with mining from real-world data is preferred. The minimum duration of the internship is 12 weeks; start time is flexible.


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  • Recent Publications

    •  Elango, P., Di Cairano, S., Berntorp, K., Weiss, A., "Sequential linearization-based station keeping with optical navigation for NRHO", AAS/AIAA Astrodynamics Specialist Conference, September 2022.
      BibTeX TR2022-114 PDF
      • @inproceedings{Elango2022sep,
      • author = {Elango, Purnanand and Di Cairano, Stefano and Berntorp, Karl and Weiss, Avishai},
      • title = {Sequential linearization-based station keeping with optical navigation for NRHO},
      • booktitle = {AAS/AIAA Astrodynamics Specialist Conference},
      • year = 2022,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2022-114}
      • }
    •  Wang, Z., Ahmad, A., Quirynen, R., Wang, Y., Bhagat, A., Zeino, E., Zushi, Y., Di Cairano, S., "Motion Planning and Model Predictive Control for Automated Tractor-Trailer Hitching Maneuver", IEEE Conference on Control Technology and Applications (CCTA), August 2022.
      BibTeX TR2022-109 PDF
      • @inproceedings{Wang2022aug,
      • author = {Wang, Zejiang and Ahmad, Ahmad and Quirynen, Rien and Wang, Yebin and Bhagat, Akshay and Zeino, Eyad and Zushi, Yuji and Di Cairano, Stefano},
      • title = {Motion Planning and Model Predictive Control for Automated Tractor-Trailer Hitching Maneuver},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
      • year = 2022,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2022-109}
      • }
    •  Menner, M., Chakrabarty, A., Berntorp, K., Di Cairano, S., "Learning Optimization-based Control Policies Directly from Digital Twin Simulations", IEEE Conference on Control Technology and Applications (CCTA), August 2022.
      BibTeX TR2022-108 PDF
      • @inproceedings{Menner2022aug,
      • author = {Menner, Marcel and Chakrabarty, Ankush and Berntorp, Karl and Di Cairano, Stefano},
      • title = {Learning Optimization-based Control Policies Directly from Digital Twin Simulations},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
      • year = 2022,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2022-108}
      • }
    •  Berntorp, K., Greiff, M., Di Cairano, S., "Bayesian Sensor Fusion of GNSS and Camera With Outlier Adaptation for Vehicle Positioning", International Conference on Information Fusion (FUSION), DOI: 10.23919/​FUSION49751.2022.9841302, July 2022, pp. 1-8.
      BibTeX TR2022-093 PDF
      • @inproceedings{Berntorp2022jul,
      • author = {Berntorp, Karl and Greiff, Marcus and Di Cairano, Stefano},
      • title = {Bayesian Sensor Fusion of GNSS and Camera With Outlier Adaptation for Vehicle Positioning},
      • booktitle = {International Conference on Information Fusion (FUSION)},
      • year = 2022,
      • pages = {1--8},
      • month = jul,
      • doi = {10.23919/FUSION49751.2022.9841302},
      • isbn = {978-1-7377497-2-1},
      • url = {https://www.merl.com/publications/TR2022-093}
      • }
    •  Greiff, M., Di Cairano, S., Berntorp, K., "Dynamic Clustering for GNSS Positioning with Multiple Receivers", International Conference on Information Fusion (FUSION), DOI: 10.23919/​FUSION49751.2022.9841289, July 2022, pp. 1-7.
      BibTeX TR2022-094 PDF
      • @inproceedings{Greiff2022jul,
      • author = {Greiff, Marcus and Di Cairano, Stefano and Berntorp, Karl},
      • title = {Dynamic Clustering for GNSS Positioning with Multiple Receivers},
      • booktitle = {International Conference on Information Fusion (FUSION)},
      • year = 2022,
      • pages = {1--7},
      • month = jul,
      • doi = {10.23919/FUSION49751.2022.9841289},
      • isbn = {978-1-6654-8941-6},
      • url = {https://www.merl.com/publications/TR2022-094}
      • }
    •  Schperberg, A., Di Cairano, S., Menner, M., "Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots", IEEE Robotics and Automation Letters, DOI: 10.1109/​LRA.2022.3185387, Vol. 7, No. 3, pp. 7802-7809, June 2022.
      BibTeX TR2022-085 PDF
      • @article{Schperberg2022jun,
      • author = {Schperberg, Alexander and Di Cairano, Stefano and Menner, Marcel},
      • title = {Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots},
      • journal = {IEEE Robotics and Automation Letters},
      • year = 2022,
      • volume = 7,
      • number = 3,
      • pages = {7802--7809},
      • month = jun,
      • doi = {10.1109/LRA.2022.3185387},
      • url = {https://www.merl.com/publications/TR2022-085}
      • }
    •  Berntorp, K., Menner, M., "Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models", American Control Conference (ACC), June 2022.
      BibTeX TR2022-066 PDF
      • @inproceedings{Berntorp2022jun,
      • author = {Berntorp, Karl and Menner, Marcel},
      • title = {Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models},
      • booktitle = {American Control Conference (ACC)},
      • year = 2022,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2022-066}
      • }
    •  Bonzanini, A.D., Mesbah, A., Di Cairano, S., "Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving", American Control Conference (ACC), June 2022.
      BibTeX TR2022-062 PDF
      • @inproceedings{Bonzanini2022jun,
      • author = {Bonzanini, Angelo Domenico and Mesbah, Ali and Di Cairano, Stefano},
      • title = {Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving},
      • booktitle = {American Control Conference (ACC)},
      • year = 2022,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2022-062}
      • }
    See All Publications for Dynamical Systems

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