Data-Driven Observability Analysis for Nonlinear Stochastic Systems
RWTH Publications (RWTH Aachen)
Abstract
Distinguishability and, by extension, observability are key properties of dynamical systems. Establishing these properties is challenging, especially when no analytical model is available and they are to be inferred directly from measurement data. The presence of noise further complicates this analysis, as standard notions of distinguishability are tailored to deterministic systems. We build on distributional distinguishability, which extends the deterministic notion by comparing distributions of outputs of stochastic systems. We first show that both concepts are equivalent for a class of systems that includes linear systems. We then present a method to assess and quantify distributional distinguishability from output data. Specifically, our quantification measures how much data is required to tell apart two initial states, inducing a continuous spectrum of distinguishability. We propose a statistical test to determine a threshold above which two states can be considered distinguishable with high confidence. We illustrate these tools by computing distinguishability maps over the state space in simulation, then leverage the test to compare sensor configurations on hardware.
Authors 5
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RWTH Aachen University · Université Paris Sciences et Lettres · École Nationale Supérieure des Mines de Paris · Ansys (France) · Centre Automatique et Systèmes
Affiliation as printed
Institute for Data Science in Mechanical Engineering , RWTH Aachen University , 52068 Aachen , Germany
Ansys Research Team, Ansys France, 69100 Villeurbanne, France
Centre Automatique et Systèmes, Mines Paris -PSL university, 75006 Paris, France
Institute for Data Science in Mechanical Engineering, RWTH Aachen University, 52068 Aachen, Germany
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RWTH Aachen University · Université Paris Sciences et Lettres · École Nationale Supérieure des Mines de Paris · Ansys (France) · Centre Automatique et Systèmes
Affiliation as printed
Ansys Research Team, Ansys France, 69100 Villeurbanne, France
Centre Automatique et Systèmes, Mines Paris -PSL university, 75006 Paris, France
Institute for Data Science in Mechanical Engineering, RWTH Aachen University, 52068 Aachen, Germany
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RWTH Aachen University · Université Paris Sciences et Lettres · École Nationale Supérieure des Mines de Paris · Ansys (France) · Centre Automatique et Systèmes
Affiliation as printed
Institute for Data Science in Mechanical Engineering , RWTH Aachen University , 52068 Aachen , Germany
Ansys Research Team, Ansys France, 69100 Villeurbanne, France
Centre Automatique et Systèmes, Mines Paris -PSL university, 75006 Paris, France
Institute for Data Science in Mechanical Engineering, RWTH Aachen University, 52068 Aachen, Germany
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RWTH Aachen University · Université Paris Sciences et Lettres · École Nationale Supérieure des Mines de Paris · Ansys (France) · Centre Automatique et Systèmes
Affiliation as printed
Ansys Research Team, Ansys France, 69100 Villeurbanne, France
Centre Automatique et Systèmes, Mines Paris -PSL university, 75006 Paris, France
Institute for Data Science in Mechanical Engineering, RWTH Aachen University, 52068 Aachen, Germany
-
RWTH Aachen University · Université Paris Sciences et Lettres · École Nationale Supérieure des Mines de Paris · Ansys (France) · Centre Automatique et Systèmes
Affiliation as printed
Institute for Data Science in Mechanical Engineering , RWTH Aachen University , 52068 Aachen , Germany
Ansys Research Team, Ansys France, 69100 Villeurbanne, France
Centre Automatique et Systèmes, Mines Paris -PSL university, 75006 Paris, France
Institute for Data Science in Mechanical Engineering, RWTH Aachen University, 52068 Aachen, Germany
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