NEWS    MERL Researcher Ankush Chakrabarty organized a special session on data-driven control at IEEE CCTA 2020

Date:

August 25, 2020

Description:

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.

Research Areas:

Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics

• Related Publications

   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, DOI: 10.1109/​CCTA41146.2020.9206315, August 2020, pp. 352-357.

  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,
  • pages = {352--357},
  • month = aug,
  • publisher = {IEEE},
  • doi = {10.1109/CCTA41146.2020.9206315},
  • url = {https://www.merl.com/publications/TR2020-116}
  • }