MERL - Mitsubishi Electric Research Laboratories

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Control

If it moves, we control it.

Our expertise in this area covers multivariable, nonlinear, optimal and model-predictive control theory, nonlinear estimation, nonlinear dynamical systems, and mechanical design. We conduct both fundamental and applied research targeting a wide range of applications including autonomous driving, factory automation and HVAC systems.

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  • Researchers

  • Awards

    •  AWARD   Best Student Paper Award at the IEEE Conference on Control Technology and Applications
      Date: August 26, 2020
      Awarded to: Marcus Greiff, Anders Robertsson, Karl Berntorp
      MERL Contact: Karl Berntorp
      Research Areas: Control, Signal Processing
      Brief
      • Marcus Greiff, a former MERL intern from the Department of Automatic Control, Lund University, Sweden, won one of three 2020 CCTA Outstanding Student Paper Awards and the Best Student Paper Award at the 2020 IEEE Conference on Control Technology and Applications. The research leading up to the awarded paper titled 'MSE-Optimal Measurement Dimension Reduction in Gaussian Filtering', concerned how to select a reduced set of measurements in estimation applications while minimally degrading performance, was done in collaboration with Karl Berntorp at MERL.
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    •  AWARD   MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019
      Date: October 10, 2019
      Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
      MERL Contact: Devesh K. Jha
      Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
      Brief
      • MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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  • News & Events

    •  EVENT   Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Melanie Zeilinger, ETH
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
        Prof. Melanie Zeilinger from ETH .

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction

        Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
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    •  EVENT   Prof. Ashok Veeraraghavan of Rice University to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Ashok Veeraraghavan, Rice University
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
        Prof. Ashok Veeraraghavan from Rice University.

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Veeraraghavan's talk is scheduled for 1:15pm - 1:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Computational Imaging: Beyond the limits imposed by lenses.

        Abstract: The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of the incident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging.

        First, we discuss our lab’s recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.
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  • Internships

    • CA1719: Spacecraft Guidance, Navigation, and Control

      MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates have experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, rendezvous, attitude control, orbit control, orbit determination, nonlinear estimation, and optimization-based control. PhD students in aerospace, mechanical, or electrical engineering are encouraged to apply. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • SP1711: Advanced Network Design

      MERL is seeking a highly motivated and qualified intern to join the Signal Processing Group for a three month internship program. The ideal candidate will be expected to carry out research on network design and optimization methods including AI assisted networking. The candidate is expected to develop innovative network configuration technologies to support emerging IoT applications. The candidates should have knowledge of network technologies such as network slicing, software defined networking and/or semantic networking. Knowledge of the communication technologies such as 3GPP-5G or IEEE 802 WLAN/WPAN standards is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • CA1727: Learning for Control

      MERL is looking for highly motivated interns to work with the Control for Autonomy team in the domain of data-based estimation for integration into control, with applications to, e.g., vehicle control. The ideal candidate is working towards a PhD with emphasis on control and has experience in as many as possible of the following topics: statistical signal processing, Bayesian inference, predictive control, stochastic constrained control, statistical learning. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months, and the start date is in 2022 but flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


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  • Openings


    See All Openings at MERL

  • Recent Publications

    •  Chen, D., Danielson, C., Di Cairano, S., "A Predictive Controller for Drivability and Comfort in Multi-Motor Electric Vehicles", IFAC Modeling, Estimation and Control Conference (MECC), October 2021.
      BibTeX TR2021-133 PDF
      • @inproceedings{Chen2021oct,
      • author = {Chen, Di and Danielson, Claus and Di Cairano, Stefano},
      • title = {A Predictive Controller for Drivability and Comfort in Multi-Motor Electric Vehicles},
      • booktitle = {IFAC Modeling, Estimation and Control Conference (MECC)},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-133}
      • }
    •  Srinivasan, M., Chakrabarty, A., Quirynen, R., yoshikawa, N., Mariyama, T., Di Cairano, S., "Fast Multi-Robot Motion Planning via Imitation Learning of Mixed-Integer Programs", IFAC Modeling, Estimation and Control Conference (MECC), October 2021.
      BibTeX TR2021-134 PDF
      • @inproceedings{Srinivasan2021oct,
      • author = {Srinivasan, Mohit and Chakrabarty, Ankush and Quirynen, Rien and yoshikawa, nobuyuki and Mariyama, Toshisada and Di Cairano, Stefano},
      • title = {Fast Multi-Robot Motion Planning via Imitation Learning of Mixed-Integer Programs},
      • booktitle = {IFAC Modeling, Estimation and Control Conference (MECC)},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-134}
      • }
    •  Menner, M., Di Cairano, S., "Kernel Regression for Energy-Optimal Control of Fully Electric Vehicles", IEEE Vehicle Power and Propulsion Conference, October 2021.
      BibTeX TR2021-132 PDF
      • @inproceedings{Menner2021oct,
      • author = {Menner, Marcel and Di Cairano, Stefano},
      • title = {Kernel Regression for Energy-Optimal Control of Fully Electric Vehicles},
      • booktitle = {IEEE Vehicle Power and Propulsion Conference},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-132}
      • }
    •  Chakrabarty, A., Quirynen, R., Romeres, D., Di Cairano, S., "Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC", IEEE International Conference on Systems, Man, and Cybernetics, October 2021.
      BibTeX TR2021-126 PDF
      • @inproceedings{Chakrabarty2021oct,
      • author = {Chakrabarty, Ankush and Quirynen, Rien and Romeres, Diego and Di Cairano, Stefano},
      • title = {Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC},
      • booktitle = {IEEE International Conference on Systems, Man, and Cybernetics},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-126}
      • }
    •  Amadio, F., Dalla Libera, A., Carli, R., Romeres, D., "Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation", Automatica.it, September 2021.
      BibTeX TR2021-108 PDF
      • @inproceedings{Amadio2021sep,
      • author = {Amadio, Fabio and Dalla Libera, Alberto and Carli, Ruggero and Romeres, Diego},
      • title = {Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation},
      • booktitle = {Automatica.it},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-108}
      • }
    •  Tanaka, R., Nabi, S., Nonaka, M., "Airflow Optimization for Room Air Conditioners", Building Simulation 2021 Conference, September 2021.
      BibTeX TR2021-106 PDF
      • @inproceedings{Tanaka2021sep,
      • author = {Tanaka, Ryuta and Nabi, Saleh and Nonaka, Mio},
      • title = {Airflow Optimization for Room Air Conditioners},
      • booktitle = {Building Simulation 2021 Conference},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-106}
      • }
    •  Chakrabarty, A., Benosman, M., "Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization", Automatica, August 2021.
      BibTeX TR2021-101 PDF
      • @article{Chakrabarty2021aug,
      • author = {Chakrabarty, Ankush and Benosman, Mouhacine},
      • title = {Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization},
      • journal = {Automatica},
      • year = 2021,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2021-101}
      • }
    •  Ravikumar, S., Quirynen, R., Bhagat, A., Zeino, E., Di Cairano, S., "Mixed-integer Programming for Centralized Coordination of Connected and Automated Vehicles in Dynamic Environment", IEEE Conference on Control Technology and Applications (CCTA), August 2021.
      BibTeX TR2021-089 PDF
      • @inproceedings{Ravikumar2021aug,
      • author = {Ravikumar, Shreejith and Quirynen, Rien and Bhagat, Akshay and Zeino, Eyad and Di Cairano, Stefano},
      • title = {Mixed-integer Programming for Centralized Coordination of Connected and Automated Vehicles in Dynamic Environment},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
      • year = 2021,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2021-089}
      • }
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