TR2022-125
Self-tuning Optimal Torque Control for Servomotor Drives Via Adaptive Dynamic Programming
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- "Self-tuning Optimal Torque Control for Servomotor Drives Via Adaptive Dynamic Programming", IEEE International Conference on Systems, Man, and Cybernetics (SMC), DOI: 10.1109/SMC53654.2022.9945395, October 2022.BibTeX TR2022-125 PDF
- @inproceedings{Wang2022oct,
- author = {Wang, Yebin},
- title = {Self-tuning Optimal Torque Control for Servomotor Drives Via Adaptive Dynamic Programming},
- booktitle = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
- year = 2022,
- month = oct,
- doi = {10.1109/SMC53654.2022.9945395},
- url = {https://www.merl.com/publications/TR2022-125}
- }
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- "Self-tuning Optimal Torque Control for Servomotor Drives Via Adaptive Dynamic Programming", IEEE International Conference on Systems, Man, and Cybernetics (SMC), DOI: 10.1109/SMC53654.2022.9945395, October 2022.
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Abstract:
Data-driven methods for learning optimal control policies such as adaptive dynamic programming have garnered widespread attention. A strong contrast to full-fledged theoretical research is the scarcity of demonstrated successes in industrial applications. This paper extends an established data-driven solution for a class of adaptive optimal linear output regulation problem to achieve self-tuning torque control of servomotor drives, and thus enables online adaptation to unknown motor resistance, inductance, and permanent magnet flux. We make contributions by tackling three practical issues/challenges: 1) tailor the baseline algorithm to reduce computation burden; 2) demonstrate the necessity of perturbing reference in order to learn feedforward gain matrix; 3) generalize the algorithm to the case where F matrix in the output equation is unknown. Simulation demonstrates that the deployment of adaptive dynamic programming lands at optimal torque tracking policies.
Related News & Events
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NEWS Yebin Wang delivered an invited industry talk at the 1st IEEE Industrial Electronics Society Annual On-Line Conference Date: December 9, 2022 - December 11, 2022
MERL Contact: Yebin Wang
Research Areas: Communications, Control, OptimizationBrief- Future factory, in the era of industry 4.0, is characterized by autonomy, digital twin, and mass customization. This talk, titled "Future factory automation and cyber-physical system: an industrial perspective," focuses on tackling the challenges arising from mass customization, for example reconfigurable machine controller and material flow.