Robust Dual Control MPC with Application to Soft-Landing Control

    •  Cheng, Y., Haghighat, S., Di Cairano, S., "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 = {}
      • }
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  • Research Areas:

    Control, Optimization, Dynamical Systems


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.


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