Data driven stability analysis of black-box switched linear systems

DOI10.1016/j.automatica.2019.108533zbMath1429.93266arXiv1803.03687OpenAlexW2969264971WikidataQ127346736 ScholiaQ127346736MaRDI QIDQ2280933

Joris Kenanian, Raphaël M. Jungers, Paulo Tabuada, Ayça Balkan

Publication date: 19 December 2019

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.03687

zbMATH Keywords

optimizationstabilitylearning algorithmsswitched systemsLyapunov methodsdata-driven verification

Mathematics Subject Classification ID

Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Linear systems in control theory (93C05) Control/observation systems governed by functional relations other than differential equations (such as hybrid and switching systems) (93C30)

Related Items (8)

Comments on: ``Data driven stability analysis of black-box switched linear systems ⋮ Data-driven switching modeling for MPC using regression trees and random forests ⋮ Learning stability guarantees for constrained switching linear systems from noisy observations ⋮ Counterexample-guided computation of polyhedral Lyapunov functions for piecewise linear systems ⋮ Linear matrix inequality relaxations and its application to data‐driven control design for switched affine systems ⋮ Strategy synthesis for partially-known switched stochastic systems ⋮ Computing Controlled Invariant Sets from Data Using Convex Optimization ⋮ Stability analysis of switched ARX models and application to learning with guarantees

Uses Software

• DLMF
• AS 64
• JSR

Cites Work

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• On the complexity of switching linear regression
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• Simulation-guided lyapunov analysis for hybrid dynamical systems
• JSR
• Proofs from simulations and modular annotations
• A delta-sampling verification theorem for discrete-time, possibly discontinuous systems
• Case Studies in Data-Driven Verification of Dynamical Systems
• Random Convex Programs
• The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
• The Scenario Approach to Robust Control Design
• Model-Free Plant Tuning
• Convex Approximations of Chance Constrained Programs

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