TR2020-116
Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems
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- , "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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MERL Contacts:
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Research Areas:
Abstract:
We design real-time optimal tracking controllers for servomotor systems engaged in single-axis point-to-point positioning tasks. The design is challenging due to the presence of unmodeled dynamics, along with speed and acceleration constraints. As model-based optimal control design methods cannot be applied directly to this uncertain system, we propose a data-driven approximate dynamic programming approach to learn an optimal tracking controller that is constraint-enforcing. The potential of our proposed method is illustrated on a servomotor that positions the head of a laser drilling machine.
Related News & Events
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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
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, RoboticsBrief- 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.
- 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.