TR2019-116
Near-optimal control of motor drives via approximate dynamic programming
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- "Near-optimal control of motor drives via approximate dynamic programming", IEEE International Conference on Systems, Man, and Cybernetics, DOI: 10.1109/SMC.2019.8914595, October 2019, pp. 3679-3686.BibTeX TR2019-116 PDF
- @inproceedings{Wang2019oct,
- author = {Wang, Yebin and Chakrabarty, Ankush and Zhou, Mengchu and Zhang, Jinyun},
- title = {Near-optimal control of motor drives via approximate dynamic programming},
- booktitle = {IEEE International Conference on Systems, Man, and Cybernetics},
- year = 2019,
- pages = {3679--3686},
- month = oct,
- publisher = {IEEE},
- doi = {10.1109/SMC.2019.8914595},
- url = {https://www.merl.com/publications/TR2019-116}
- }
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- "Near-optimal control of motor drives via approximate dynamic programming", IEEE International Conference on Systems, Man, and Cybernetics, DOI: 10.1109/SMC.2019.8914595, October 2019, pp. 3679-3686.
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Research Areas:
Abstract:
Data-driven methods for learning near-optimal control policies through approximate dynamic programming (ADP) have garnered widespread attention. In this paper, we investigate how data-driven control methods can be leveraged to imbue near-optimal performance in a core component in modern factory systems: the electric motor drive. We apply policy iteration-based ADP on an induction motor model in order to construct a state feedback control policy for a given cost functional. Approximate error convergence properties of policy iteration methods imply that the learned control policy is near-optimal. We demonstrate that carefully selecting a cost functional and initial control policy yields a near-optimal control policy that outperforms both a baseline nonlinear control policy based on backstepping, as well as the initial control policy.