TR2015-064
Robust Dual Control MPC with Application to Soft-Landing Control
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- "Robust Dual Control MPC with Application to Soft-Landing Control", American Control Conference (ACC), DOI: 10.1109/ACC.2015.7171932, July 2015, pp. 3862-3867.BibTeX TR2015-064 PDF
- @inproceedings{Cheng2015jul,
- author = {Cheng, Y. and Haghighat, S. and {Di Cairano}, S.},
- title = {Robust Dual Control MPC with Application to Soft-Landing Control},
- booktitle = {American Control Conference (ACC)},
- year = 2015,
- pages = {3862--3867},
- month = jul,
- publisher = {IEEE},
- doi = {10.1109/ACC.2015.7171932},
- issn = {0743-1619},
- isbn = {978-1-4799-8685-9},
- url = {https://www.merl.com/publications/TR2015-064}
- }
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- "Robust Dual Control MPC with Application to Soft-Landing Control", American Control Conference (ACC), DOI: 10.1109/ACC.2015.7171932, July 2015, pp. 3862-3867.
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Abstract:
Dual control frameworks for systems subject to uncertainties aim at simultaneously learning the unknown parameters while controlling the system dynamics. We propose a robust dual model predictive control algorithm for systems with bounded uncertainty with application to soft landing control. The algorithm exploits a robust control invariant set to guarantee constraint enforcement in spite of the uncertainty, and a constrained estimation algorithm to guarantee admissible parameter estimates. The impact of the control input on parameter learning is accounted for by including in the cost function a reference input, which is designed online to provide persistent excitation. The reference input design problem is non-convex, and here is solved by a sequence of relaxed convex problems. The results of the proposed method in a soft-landing control application in transportation systems are shown.
Related News & Events
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NEWS MERL researchers present 10 papers at the American Controls Conference Date: July 3, 2015
MERL Contacts: Daniel N. Nikovski; Yebin Wang; Stefano Di Cairano; Arvind Raghunathan; Avishai WeissBrief- MERL researchers presented 10 papers at the American Controls Conference, in Chicago, USA. The ACC is one of the most important conferences on control systems in the world. Topics ranged from theoretical, including new algorithms for Model Predictive Control and Co-Design, to applications including spacecraft control and HVAC systems.