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.

  • Researchers

  • Awards

    •  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.
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  • News & Events


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  • Internships

    • CA1401: Formal Synthesis for Planning and Control for Autonomous Systems

      The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on planning and control by formal methods, in particular temporal logics specifications and their synthesis by mixed-integer inequalities. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Computer Science or related program, with focus on Control Theory. The ideal candidate will have experience in (one or more of) formal methods, particularly temporal logics and signal temporal logics, reachability analysis, abstractions of dynamical systems, hybrid predictive control, and mixed integer programming. Good programming skills in Matlab (or alternatively Python) are required, working knowledge of C/C++ is a plus. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

    • CA1400: Autonomous Vehicle Planning and Control

      The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at different levels of expertise to conduct 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. PhD students will be considered for algorithm development and analysis, and property proving. Master students will be considered for development and implementation in a scaled robotic test bench for autonomous vehicles. For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++. For algorithm implementation, it is required to have working knowledge of Matlab, C++, and ROS, and it is a plus to have background in some of the above mentioned methods. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

    • MD1300: Compiler Optimizations for Linear Algebra Kernels

      MERL is looking for a highly motivated individual to work on automatic, compiler based techniques for optimizing linear algebra kernels. The ideal candidate is a Ph.D. student in computer science with extensive experience in compiler design and source code optimization techniques. In particular, the successful candidate will have a strong working knowledge of polyhedral optimization techniques, the LLVM compiler, and Polly. Strong C/C++ skills and knowledge of LLVM at the source level are required. Publication of results in conference proceedings and journals is expected. The expected duration of the internship is 3 months and the start date is flexible.


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  • Recent Publications

    •  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}
      • }
    •  Chakrabarty, A., Jha, D., Buzzard, G.T., Wang, Y., Vamvoudakis, K., "Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation", IEEE Transactions on Neural Networks and Learning Systems, July 2020.
      BibTeX TR2020-108 PDF
      • @article{Chakrabarty2020jul2,
      • author = {Chakrabarty, Ankush and Jha, Devesh and Buzzard, Gregery T. and Wang, Yebin and Vamvoudakis, Kyriakos},
      • title = {Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation},
      • journal = {IEEE Transactions on Neural Networks and Learning Systems},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-108}
      • }
    •  Menner, M., Berntorp, K., Di Cairano, S., "Inverse Learning for Data-driven Calibration of Model-based Statistical Path Planning", Transactions on Intelligent Vehicles, July 2020.
      BibTeX TR2020-106 PDF
      • @article{Menner2020jul,
      • author = {Menner, Marcel and Berntorp, Karl and Di Cairano, Stefano},
      • title = {Inverse Learning for Data-driven Calibration of Model-based Statistical Path Planning},
      • journal = {Transactions on Intelligent Vehicles},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-106}
      • }
    •  Berntorp, K., "Online Bayesian Tire-Friction Learning by Gaussian-Process State-Space Models", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-104 PDF
      • @inproceedings{Berntorp2020jul,
      • author = {Berntorp, Karl},
      • title = {Online Bayesian Tire-Friction Learning by Gaussian-Process State-Space Models},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-104}
      • }
    •  Quirynen, R., Feng, X., Di Cairano, S., "Inexact Adjoint-based SQP Algorithm for Real-Time Stochastic Nonlinear MPC", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-103 PDF
      • @inproceedings{Quirynen2020jul2,
      • author = {Quirynen, Rien and Feng, Xuhui and Di Cairano, Stefano},
      • title = {Inexact Adjoint-based SQP Algorithm for Real-Time Stochastic Nonlinear MPC},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-103}
      • }
    •  Quirynen, R., Frey, J., Di Cairano, S., "Active-Set based Inexact Interior Point QP Solver for Model Predictive Control", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-105 PDF
      • @inproceedings{Quirynen2020jul3,
      • author = {Quirynen, Rien and Frey, Jonathan and Di Cairano, Stefano},
      • title = {Active-Set based Inexact Interior Point QP Solver for Model Predictive Control},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-105}
      • }
    •  Muralidharan, V., Weiss, A., Kalabic, U., "Tracking neighboring quasi-satellite orbits around Phobos", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-102 PDF
      • @inproceedings{Muralidharan2020jul,
      • author = {Muralidharan, Vivek and Weiss, Avishai and Kalabic, Uros},
      • title = {Tracking neighboring quasi-satellite orbits around Phobos},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-102}
      • }
    •  Maske, H., Chu, T., Kalabic, U., "Control of traffic light timing using decentralized deep reinforcement learning", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-101 PDF
      • @inproceedings{Maske2020jul,
      • author = {Maske, Harshal and Chu, Tianshu and Kalabic, Uros},
      • title = {Control of traffic light timing using decentralized deep reinforcement learning},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-101}
      • }
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