MERL - Mitsubishi Electric Research Laboratories

Share:

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.

  • Quick Links

  • 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.
    •  
    •  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 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.
    •  

    See All Awards for MERL

  • News & Events

    •  NEWS   MERL Researcher Ankush Chakrabarty organized a special session on data-driven control at IEEE CCTA 2020
      Date: August 25, 2020
      MERL Contact: Ankush Chakrabarty
      Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
      Brief
      • Ankush Chakrabarty co-organized an invited session on “Data-Driven Control For Industrial Applications” at the IEEE Conference on Control Technology and Applications with Shahin Shahrampour (Asst. Prof., Texas A&M). Talks covered topics including reinforcement learning for aerospace systems, constrained reinforcement learning for motors, deep Q learning for traffic systems and participants included speakers from Stanford University, North Carolina State University, Texas A&M, Oklahoma State University, University of Science and Technology at Beijing, and TU Delft.

        MERL presented research (Chakrabarty, Danielson, Wang) on constraint-enforcing output-tracking with approximate dynamic programming for servomotor systems.
    •  
    •  NEWS   Dr. Abraham P. Vinod joins the Research Staff of Mitsubishi Electric Research Laboratories
      Date: August 3, 2020
      Where: Cambridge, MA
      MERL Contact: Abraham P. Vinod
      Research Areas: Artificial Intelligence, Control, Optimization, Robotics
      Brief
      • Mitsubishi Electric Research Laboratories is excited to welcome Abraham P. Vinod as the newest member of its research staff, in the Control for Autonomy Team. Abraham joins MERL from the University of Texas, Austin, where he was a Postdoctoral Research Fellow. He obtained his Ph.D. from the University of New Mexico. His PhD research produced scalable algorithms for providing safety guarantees for stochastic, control-constrained, dynamical systems, with applications to motion planning. In his postdoctoral research, Abraham studied theory and algorithms for on-the-fly, data-driven control of unknown systems under severely limited data. His current research interests lie in the intersection of optimization, control, and learning. Abraham won the Best Student Paper Award at the 2017 ACM Hybrid Systems: Computation and Control Conference, was a finalist for the Best Paper Award in the 2018 ACM Hybrid Systems: Computation and Control Conference, and won the best undergraduate student research project award at the Indian Institute of Technology, Madras.
    •  

    See All News & Events for Control

  • Internships

    • SP1460: Advanced Vehicular Technologies

      MERL is seeking a highly motivated, qualified intern to collaborate with the Signal Processing group and the Control for Autonomy team in developing technologies for Connected Automated Vehicles. The ideal candidate is expected to be involved in research on collaborative learning between infrastructure and vehicles. The candidate is expected to develop learning-based technologies to achieve vehicle coordination, estimation and GNSS-based localization using data and computation sharing between vehicle and infrastructure. The candidates should have knowledge of machine learning, connected vehicles and V2X communications. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) and GNSS is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. The expected duration of the internship is 3-6 months, with start date in September/October 2020.

    • MD1377: Adaptive Optimal Control of Electrical Machines

      MERL is seeking a motivated and qualified individual to conduct research in control of electrical machines. The ideal candidate should have solid backgrounds in adaptive dynamic programming and state/parameter estimation for electrical machines, demonstrated capability to publish results in leading conferences/journals, and experience with real-time control experiments involving high power devices. Senior Ph.D. students are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • MS1461: Online Bayesian Optimization

      The Multiphysical Systems (MS) team at MERL is seeking a highly motivated intern to conduct research on model-free optimization of HVAC systems, with special emphasis on online and scalable Bayesian optimization. The ideal candidate is enrolled in a PhD program and is pursuing research in machine learning for optimization/control. The ideal candidate will have experience in (one or more of) Bayesian optimization, Bayesian neural nets, Gaussian processes, and must be fluent in Python and standard ML toolkits e.g. PyTorch/Tensorflow. The expected duration of the (virtual) internship is 3-6 months.


    See All Internships for Control

  • Recent Publications

    •  Kalabic, U., Chiu, M., "Cap-and-trade scheme for ridesharing", Intelligent Transportation Systems Conference, September 2020.
      BibTeX TR2020-129 PDF
      • @inproceedings{Kalabic2020sep,
      • author = {Kalabic, Uros and Chiu, Michael},
      • title = {Cap-and-trade scheme for ridesharing},
      • booktitle = {Intelligent Transportation Systems Conference},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-129}
      • }
    •  Chakrabarty, A., Danielson, C., Wang, Y., "Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems", IEEE Conference on Control Technology and Applications, August 2020.
      BibTeX TR2020-116 PDF
      • @inproceedings{Chakrabarty2020aug,
      • author = {Chakrabarty, Ankush and Danielson, Claus and Wang, Yebin},
      • title = {Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems},
      • booktitle = {IEEE Conference on Control Technology and Applications},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-116}
      • }
    •  Wang, Y., Satake, A., Furutani, S., Sano, S., "Stable Adaptive Estimation for Speed-sensorless Induction Motor Drives: A Geometric Approach", International Conference on Electrical Machines (ICEM), August 2020.
      BibTeX TR2020-122 PDF
      • @inproceedings{Wang2020aug,
      • author = {Wang, Yebin and Satake, Akira and Furutani, Shinichi and Sano, Sota},
      • title = {Stable Adaptive Estimation for Speed-sensorless Induction Motor Drives: A Geometric Approach},
      • booktitle = {International Conference on Electrical Machines (ICEM)},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-122}
      • }
    •  Zhou, L., Wang, Y., "Improve Speed Estimation for Speed-Sensorless Induction Machines: A Variable Adaptation Gain and Feedforward Approach", International Conference on Electrical Machines (ICEM), August 2020.
      BibTeX TR2020-123 PDF
      • @inproceedings{Zhou2020aug,
      • author = {Zhou, Lei and Wang, Yebin},
      • title = {Improve Speed Estimation for Speed-Sensorless Induction Machines: A Variable Adaptation Gain and Feedforward Approach},
      • booktitle = {International Conference on Electrical Machines (ICEM)},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-123}
      • }
    •  Samad, T., Di Cairano, S., Fagiano, L., Odgaard, P., Rhinehart, R., Bortoff, S.A., "Industry Engagement with Control Research: Perspective and Messages", Annual Reviews In Control, August 2020.
      BibTeX TR2020-119 PDF
      • @article{Samad2020aug,
      • author = {Samad, Tariq and Di Cairano, Stefano and Fagiano, Lorenzo and Odgaard, Peter and Rhinehart, Russel and Bortoff, Scott A.},
      • title = {Industry Engagement with Control Research: Perspective and Messages},
      • journal = {Annual Reviews In Control},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-119}
      • }
    •  Guay, M., Benosman, M., "Finite-time extremum seeking control for a class of unknown static maps", International journal of adaptive control and signal processing, July 2020.
      BibTeX TR2020-117 PDF
      • @article{Guay2020jul,
      • author = {Guay, Martin and Benosman, Mouhacine},
      • title = {Finite-time extremum seeking control for a class of unknown static maps},
      • journal = {International journal of adaptive control and signal processing},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-117}
      • }
    •  Steeves, D., Camacho-Solorio, L., Benosman, M., Krstic, M., "Prescribed–time tracking for triangular systems of reaction–diffusion PDEs", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-118 PDF
      • @inproceedings{Steeves2020jul,
      • author = {Steeves, Drew and Camacho-Solorio, Leobardo and Benosman, Mouhacine and Krstic, Miroslav},
      • title = {Prescribed–time tracking for triangular systems of reaction–diffusion PDEs},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-118}
      • }
    •  Di Cairano, S., Danielson, C., "Indirect Adaptive Model Predictive Control and its Application to Uncertain Linear Systems", International Journal of Robust and Nonlinear Control, July 2020.
      BibTeX TR2020-115 PDF
      • @article{DiCairano2020jul,
      • author = {Di Cairano, Stefano and Danielson, Claus},
      • title = {Indirect Adaptive Model Predictive Control and its Application to Uncertain Linear Systems},
      • journal = {International Journal of Robust and Nonlinear Control},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-115}
      • }
    See All Publications for Control

  • Videos

  • Software Downloads