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Observability of Uncertain Nonlinear Systems Using Interval Analysis

Chair of Automation and Control Theory, Bergische Universität Wuppertal, 42119 Wuppertal, Germany
Author to whom correspondence should be addressed.
Received: 31 January 2020 / Revised: 12 March 2020 / Accepted: 12 March 2020 / Published: 16 March 2020
(This article belongs to the Special Issue Algorithms for Reliable Estimation, Identification and Control)
In the field of control engineering, observability of uncertain nonlinear systems is often neglected and not examined. This is due to the complex analytical calculations required for the verification. Therefore, the aim of this work is to provide an algorithm which numerically analyzes the observability of nonlinear systems described by finite-dimensional, continuous-time sets of ordinary differential equations. The algorithm is based on definitions for distinguishability and local observability using a rank check from which conditions are deduced. The only requirements are the uncertain model equations of the system. Further, the methodology verifies observability of nonlinear systems on a given state space. In case that the state space is not fully observable, the algorithm provides the observable set of states. In addition, the results obtained by the algorithm allows insight into why the remaining states cannot be distinguished. View Full-Text
Keywords: observability; uncertain nonlinear systems; interval analysis observability; uncertain nonlinear systems; interval analysis
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MDPI and ACS Style

Paradowski, T.; Lerch, S.; Damaszek, M.; Dehnert, R.; Tibken, B. Observability of Uncertain Nonlinear Systems Using Interval Analysis. Algorithms 2020, 13, 66.

AMA Style

Paradowski T, Lerch S, Damaszek M, Dehnert R, Tibken B. Observability of Uncertain Nonlinear Systems Using Interval Analysis. Algorithms. 2020; 13(3):66.

Chicago/Turabian Style

Paradowski, Thomas, Sabine Lerch, Michelle Damaszek, Robert Dehnert, and Bernd Tibken. 2020. "Observability of Uncertain Nonlinear Systems Using Interval Analysis" Algorithms 13, no. 3: 66.

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