NEWS MERL Researcher Ankush Chakrabarty organized a special session on data-driven control at IEEE CCTA 2020
Date released: August 25, 2020
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NEWS MERL Researcher Ankush Chakrabarty organized a special session on data-driven control at IEEE CCTA 2020 Date:
August 25, 2020
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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. -
MERL Contact:
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Research Areas:
Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
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Related Publications
- "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}
- }
- "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.
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