Facial Input Decompositions for Robust Peak Estimation under Polyhedral Uncertainty. Issue 25 (2022)

• Record Type: Journal Article
• Authors: Miller, Jared; Sznaier, Mario
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 25(2022)
• Page Start: 55
• Page End: 60
• Publication Date: 2022
• Subjects: Robust Optimization in Control -- Uncertain Systems -- Sum-of-Squares -- Computational Methods in Control -- Safety Certificates; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.09.323
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This work bounds extreme values of state functions for a class of input-affine continuous-time systems that are affected by polyhedral-bounded uncertainty. Instances of these systems may arise in data-driven peak estimation, in which the state function must be bounded for all systems that are consistent with a set of state-derivative data records corrupted under L-infinity bounded noise. Existing occupation measure-based methods form a convergent sequence of outer approximations to the true peak value, given an initial set, by solving a hierarchy of semidefinite programs in increasing size. These techniques scale combinatorially in the number of state variables and uncertain parameters. We present tractable algorithms for peak estimation that scale linearly in the number of faces of the uncertainty-bounding polytope rather than combinatorially in the number of uncertain parameters by leveraging convex duality and a theorem of alternatives (facial decomposition). The sequence of decomposed semidefinite programs will converge to the true peak value under mild assumptions (convergence and smoothness of dynamics).