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Nonlinear observability algorithms with known and unknown inputs:\n analysis and implementation

2020/06/01 by Martínez, Nerea, Alejandro F. Villaverde, Villaverde, Alejandro F.
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #Differential Geometry (math.DG) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Symbolic Computation (cs.SC) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2006.00859

openalex publication_date 2020/06/01 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

Abstract

The observability of a dynamical system is affected by the presence of\nexternal inputs, either known (such as control actions) or unknown\n(disturbances). Inputs of unknown magnitude are especially detrimental for\nobservability, and they also complicate its analysis. Hence the availability of\ncomputational tools capable of analysing the observability of nonlinear systems\nwith unknown inputs has been limited until lately. Two symbolic algorithms\nbased on differential geometry, ORC-DF and FISPO, have been recently proposed\nfor this task, but their critical analysis and comparison is still lacking.\nHere we perform an analytical comparison of both algorithms and evaluate their\nperformance on a set of problems, discussing their strengths and limitations.\nAdditionally, we use these analyses to provide insights about certain aspects\nof the relationship between inputs and observability. We find that, while\nORC-DF and FISPO follow a similar approach, they differ in key aspects that can\nhave a substantial influence on their applicability and computational cost. The\nFISPO algorithm is more generally applicable, since it can analyse any\nnonlinear ODE model. The ORC-DF algorithm analyses models that are affine in\nthe inputs, and if those models have known inputs it is sometimes more\nefficient. Thus, the optimal choice of a method depends on the characteristics\nof the problem under consideration. To facilitate the use of both algorithms we\nimplement the ORC-DF algorithm in a new version of STRIKE-GOLDD, a MATLAB\ntoolbox for structural identifiability and observability analysis. Since this\nsoftware tool already had an implementation of the FISPO algorithm, the new\nrelease allows modellers and model users the convenience of choosing between\ndifferent algorithms in a single tool, without changing the coding of their\nmodel.\n

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