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    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 Contacts: Karl Berntorp; Marcus Greiff
      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

    •  TALK    [MERL Seminar Series 2022] Prof. Michael Posa presents talk titled Hybrid robotics and implicit learning
      Date & Time: Tuesday, May 3, 2022; 1:00 PM
      Speaker: Michael Posa, University of Pennsylvania
      MERL Host: Devesh K. Jha
      Research Areas: Control, Optimization, Robotics
      Abstract
      • Machine learning has shown incredible promise in robotics, with some notable recent demonstrations in manipulation and sim2real transfer. These results, however, require either an accurate a priori model (for simulation) or a large amount of data. In contrast, my lab is focused on enabling robots to enter novel environments and then, with minimal time to gather information, accomplish complex tasks. In this talk, I will argue that the hybrid or contact-driven nature of real-world robotics, where a robot must safely and quickly interact with objects, drives this high data requirement. In particular, the inductive biases inherent in standard learning methods fundamentally clash with the non-differentiable physics of contact-rich robotics. Focusing on model learning, or system identification, I will show both empirical and theoretical results which demonstrate that contact stiffness leads to poor training and generalization, leading to some healthy skepticism of simulation experiments trained on artificially soft environments. Fortunately, implicit learning formulations, which embed convex optimization problems, can dramatically reshape the optimization landscape for these stiff problems. By carefully reasoning about the roles of stiffness and discontinuity, and integrating non-smooth structures, we demonstrate dramatically improved learning performance. Within this family of approaches, ContactNets accurately identifies the geometry and dynamics of a six-sided cube bouncing, sliding, and rolling across a surface from only a handful of sample trajectories. Similarly, a piecewise-affine hybrid system with thousands of modes can be identified purely from state transitions. Time permitting, I'll discuss how these learned models can be deployed for control via recent results in real-time, multi-contact MPC.
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    •  TALK    [MERL Seminar Series 2022] Prof. Sebastien Gros presents talk titled RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?
      Date & Time: Tuesday, April 12, 2022; 11:00 AM EDT
      Speaker: Sebastien Gros, NTNU
      MERL Host: Rien Quirynen
      Research Areas: Control, Dynamical Systems, Optimization
      Abstract
      • Reinforcement Learning (RL), similarly to many AI-based techniques, is currently receiving a very high attention. RL is most commonly supported by classic Machine Learning techniques, i.e. typically Deep Neural Networks (DNNs). While there are good motivations for using DNNs in RL, there are also significant drawbacks. The lack of “explainability” of the resulting control policies, and the difficulty to provide guarantees on their closed-loop behavior (safety, stability) makes DNN-based policies problematic in many applications. In this talk, we will discuss an alternative approach to support RL, via formal optimal control tools based on Model Predictive Control (MPC). This approach alleviates the issues detailed above, but also presents some challenges. In this talk, we will discuss why MPC is a valid tool to support RL, and how MPC can be combined with RL (RLMPC). We will then discuss some recent results regarding this combination, the known challenges, and the kind of control applications where we believe that RLMPC will be a valuable approach.
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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.

    • MD1746: PWM inverter circuit design

      MERL is looking for a self-motivated intern to work on PWM inverter drive circuit design and fabrication. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in power electronics. Experience in PWM inverter design, switching loss estimation, and EMI is desired. The intern is expected to collaborate with MERL researchers to design, simulate, and fabricate circuits, carry out experiments, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is 3 months.

    • CA1742: Mixed-Integer Programming for Motion Planning and Control

      MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, motion planning and control of hybrid systems. The research will involve the study and development of numerical optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: branch-and-bound type methods, heuristics for mixed-integer programming (pre-solve, cutting planes, warm starting, integer-feasible solutions), modeling and formulation of MIPs for hybrid control systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on mixed-integer programming 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/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 flexible.


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  • Recent Publications

    •  Vinod, A.P., Safaoui, S., Chakrabarty, A., Quirynen, R., yoshikawa, N., Di Cairano, S., "Safe multi-agent motion planning via filtered reinforcement learning", IEEE International Conference on Robotics and Automation (ICRA) 2022, May 2022.
      BibTeX TR2022-053 PDF Video
      • @inproceedings{Vinod2022may,
      • author = {Vinod, Abraham P. and Safaoui, Sleiman and Chakrabarty, Ankush and Quirynen, Rien and yoshikawa, nobuyuki and Di Cairano, Stefano},
      • title = {Safe multi-agent motion planning via filtered reinforcement learning},
      • booktitle = {IEEE International Conference on Robotics and Automation (ICRA) 2022},
      • year = 2022,
      • month = may,
      • url = {https://www.merl.com/publications/TR2022-053}
      • }
    •  Lin, C., Ma, Y., Sels, D., "Application of Pontryagin’s Maximum Principle to Quantum Metrology in Dissipative Systems", Physical Reivew A, May 2022.
      BibTeX TR2022-048 PDF
      • @article{Lin2022may,
      • author = {Lin, Chungwei and Ma, Yanting and Sels, Dries},
      • title = {Application of Pontryagin’s Maximum Principle to Quantum Metrology in Dissipative Systems},
      • journal = {Physical Reivew A},
      • year = 2022,
      • month = may,
      • url = {https://www.merl.com/publications/TR2022-048}
      • }
    •  Ma, Y., Guo, J., Wang, Y., Chakrabarty, A., Ahn, H., Orlik, P.V., Guan, X., Lu, C., "Optimal Dynamic Transmission Scheduling for Wireless Networked Control Systems", IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, May 2022.
      BibTeX TR2022-043 PDF
      • @article{Ma2022may,
      • author = {Ma, Yehan and Guo, Jianlin and Wang, Yebin and Chakrabarty, Ankush and Ahn, Heejin and Orlik, Philip V. and Guan, Xinping and Lu, Chenyang},
      • title = {Optimal Dynamic Transmission Scheduling for Wireless Networked Control Systems},
      • journal = {IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY},
      • year = 2022,
      • month = may,
      • url = {https://www.merl.com/publications/TR2022-043}
      • }
    •  Cauligi, A., Chakrabarty, A., Di Cairano, S., Quirynen, R., "PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control", Learning for Dynamics and Control Conference (L4DC), April 2022.
      BibTeX TR2022-039 PDF
      • @inproceedings{Cauligi2022apr,
      • author = {Cauligi, Abhishek and Chakrabarty, Ankush and Di Cairano, Stefano and Quirynen, Rien},
      • title = {PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control},
      • booktitle = {Learning for Dynamics and Control Conference (L4DC)},
      • year = 2022,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2022-039}
      • }
    •  Bortoff, S.A., Schwerdtner, P., Danielson, C., Di Cairano, S., Burns, D.J., "H-Infinity Loop-Shaped Model Predictive Control with HVAC Application", IEEE Transactions on Control Systems Technology, March 2022.
      BibTeX TR2022-028 PDF
      • @article{Bortoff2022mar,
      • author = {Bortoff, Scott A. and Schwerdtner, Paul and Danielson, Claus and Di Cairano, Stefano and Burns, Daniel J.},
      • title = {H-Infinity Loop-Shaped Model Predictive Control with HVAC Application},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2022,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2022-028}
      • }
    •  Chakrabarty, A., Danielson, C., Bortoff, S.A., Laughman, C.R., "Accelerating self-optimization control of refrigerant cycles with Bayesian optimization and adaptive moment estimation", Applied Thermal Engineering, DOI: 10.1016/​j.applthermaleng.2021.117335, Vol. 197, pp. 117335, February 2022.
      BibTeX TR2022-010 PDF
      • @article{Chakrabarty2022feb,
      • author = {Chakrabarty, Ankush and Danielson, Claus and Bortoff, Scott A. and Laughman, Christopher R.},
      • title = {Accelerating self-optimization control of refrigerant cycles with Bayesian optimization and adaptive moment estimation},
      • journal = {Applied Thermal Engineering},
      • year = 2022,
      • volume = 197,
      • pages = 117335,
      • month = feb,
      • doi = {10.1016/j.applthermaleng.2021.117335},
      • url = {https://www.merl.com/publications/TR2022-010}
      • }
    •  Vijayshankar, S., Chakrabarty, A., Grover, P., Nabi, S., "Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data", IFAC Journal of Systems and Control, DOI: 10.1016/​j.ifacsc.2021.100181, Vol. 19, pp. 100181, January 2022.
      BibTeX TR2022-009 PDF
      • @article{Vijayshankar2022jan,
      • author = {Vijayshankar, Sanjana and Chakrabarty, Ankush and Grover, Piyush and Nabi, Saleh},
      • title = {Co-Design of Reduced-Order Models and Observers from Thermo-Fluid Data},
      • journal = {IFAC Journal of Systems and Control},
      • year = 2022,
      • volume = 19,
      • pages = 100181,
      • month = jan,
      • doi = {10.1016/j.ifacsc.2021.100181},
      • url = {https://www.merl.com/publications/TR2022-009}
      • }
    •  Jeon, W., Chakrabarty, A., Zemouche, A., Rajamani, R., "Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications", IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/​TMECH.2021.3081035, Vol. 26, No. 4, pp. 1941-1950, January 2022.
      BibTeX TR2022-003 PDF
      • @article{Jeon2022jan,
      • author = {Jeon, Woongsun and Chakrabarty, Ankush and Zemouche, Ali and Rajamani, Rajesh},
      • title = {Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications},
      • journal = {IEEE/ASME Transactions on Mechatronics},
      • year = 2022,
      • volume = 26,
      • number = 4,
      • pages = {1941--1950},
      • month = jan,
      • doi = {10.1109/TMECH.2021.3081035},
      • url = {https://www.merl.com/publications/TR2022-003}
      • }
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  • Videos

  • Software Downloads