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Data-Driven Computation of Robust Control Invariant Sets With Concurrent Model Selection

Chen, Yuxiao and Ozay, Necmiye (2022) Data-Driven Computation of Robust Control Invariant Sets With Concurrent Model Selection. IEEE Transactions on Control Systems Technology, 30 (2). pp. 495-506. ISSN 1063-6536. doi:10.1109/TCST.2021.3069759.

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Set invariance in the presence of uncertainty and disturbance is of central importance for the safety of control systems. This article proposes a data-driven method to compute an approximation of a minimal robust control invariant set (mRCI) from experimental data. For a given dynamical model with additive and multiplicative uncertainty, the proposed method is able to compute a polytopic mRCI with fixed complexity via linear programming (LP). Moreover, the method can be combined with model selection to enable mRCI computation directly from experiment data when the system dynamics are unknown. Specifically, given a model structure, our algorithm begins by identifying the set of admissible models with constraints extracted from the experimental data. Each model in the set of admissible models contains information about the nominal model and the characterization of the model uncertainties. Then, two iterative algorithms based on robust optimization are proposed to compute an mRCI while simultaneously searching for a model “optimal” with regard to the mRCI computation and the corresponding invariance-inducing controller. Finally, the method is demonstrated in an experiment with an autonomous vehicle lane-keeping control example.

Item Type:Article
Related URLs:
URLURL TypeDescription
Chen, Yuxiao0000-0001-5276-7156
Ozay, Necmiye0000-0002-5552-4392
Additional Information:© 2021 IEEE. Manuscript received July 12, 2020; revised January 10, 2021; accepted March 8, 2021. Date of publication April 9, 2021; date of current version February 10, 2022. Manuscript received in final form March 27, 2021. This work was supported by the NSF under Grant CNS-1239037. The work of Necmiye Ozay was also supported in part by the NSF under Grant ECCS-1553873 and ONR under Grant N00014-18-1-2501. Recommended by Associate Editor L. Fagiano. The authors gratefully acknowledge Prof. Huei Peng and Prof. Jessy Grizzle for their help during the early phases of this project. They also thank Dr. Shaobing Xu who helped them conduct the experiments at Mcity.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-18-1-2501
Subject Keywords:Automotive control, learning, robust control invariant (RCI) set, safety-critical control, system identification
Issue or Number:2
Record Number:CaltechAUTHORS:20210419-132038270
Persistent URL:
Official Citation:Y. Chen and N. Ozay, "Data-Driven Computation of Robust Control Invariant Sets With Concurrent Model Selection," in IEEE Transactions on Control Systems Technology, vol. 30, no. 2, pp. 495-506, March 2022, doi: 10.1109/TCST.2021.3069759
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108760
Deposited By: Tony Diaz
Deposited On:19 Apr 2021 21:04
Last Modified:02 Mar 2022 22:44

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