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Safety-Critical Control Synthesis for network systems with Control Barrier Functions and Assume-Guarantee Contracts

Chen, Yuxiao and Anderson, James and Kalsi, Karan and Ames, Aaron D. and Low, Steven H. (2019) Safety-Critical Control Synthesis for network systems with Control Barrier Functions and Assume-Guarantee Contracts. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200707-113800670

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Abstract

This paper presents a contract based framework for safety-critical control synthesis for network systems. To handle the large state dimension of such systems, an assume-guarantee contract is used to break the large synthesis problem into smaller subproblems. Parameterized signal temporal logic (pSTL) is used to formally describe the behaviors of the subsystems, which we use as the template for the contract. We show that robust control invariant sets (RCIs) for the subsystems can be composed to form a robust control invariant set for the whole network system under a valid assume-guarantee contract. An epigraph algorithm is proposed to solve for a contract that is valid, ---an approach that has linear complexity for a sparse network, which leads to a robust control invariant set for the whole network. Implemented with control barrier function (CBF), the state of each subsystem is guaranteed to stay within the safe set. Furthermore, we propose a contingency tube Model Predictive Control (MPC) approach based on the robust control invariant set, which is capable of handling severe contingencies, including topology changes of the network. A power grid example is used to demonstrate the proposed method. The simulation result includes both set point control and contingency recovery, and the safety constraint is always satisfied.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1911.03452arXivDiscussion Paper
ORCID:
AuthorORCID
Chen, Yuxiao0000-0001-5276-7156
Anderson, James0000-0002-2832-8396
Ames, Aaron D.0000-0003-0848-3177
Low, Steven H.0000-0001-6476-3048
Additional Information:This work is supported by the Battelle Memorial Institute, Pacific Northwest Division, Grant #424858.
Funders:
Funding AgencyGrant Number
Battelle Memorial Institute424858
Record Number:CaltechAUTHORS:20200707-113800670
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200707-113800670
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104252
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Jul 2020 18:41
Last Modified:03 Aug 2020 21:31

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