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.
    •  

    See All Awards for MERL
  • News & Events


    See All News & Events for Control
  • 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.

    • 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.

    • CA1399: Optimization Algorithms for Stochastic Predictive Control

      MERL is looking for a highly motivated individual to work on tailored numerical optimization algorithms and applications of stochastic learning-based model predictive control (MPC) methods. The research will involve the study and development of novel optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), convex and non-convex optimization, machine learning, numerical optimization and (inverse) optimal control. PhD students in engineering or mathematics with a focus on stochastic (learning-based) MPC or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab 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 flexible.


    See All Internships for Control
  • 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}
      • }
    See All Publications for Control
  • Videos

  • Software Downloads