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Learning-based Desi...
Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems
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- Niazi, Muhammad Umar B. (author)
- KTH,Reglerteknik,MIT, Lab Informat & Decis Syst, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
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- Cao, John (author)
- KTH,Reglerteknik
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- Sun, Xudong (author)
- KTH,Reglerteknik
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- Das, Amritam (author)
- KTH,Reglerteknik
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- Johansson, Karl Henrik, 1967- (author)
- KTH,Reglerteknik
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- English.
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In: 2023 American Control Conference , ACC. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3048-3055
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.2...
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Abstract
Subject headings
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- Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection. The observer then estimates the system's state in the original coordinates by inverting the transformation map. However, finding a suitable injective transformation whose inverse can be derived remains a primary challenge for general nonlinear systems. We propose a novel approach that uses supervised physics-informed neural networks to approximate both the transformation and its inverse. Our method exhibits superior generalization capabilities to contemporary methods and demonstrates robustness to both neural network's approximation errors and system uncertainties.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- Nonlinear observer design
- robust estimation
- physics-informed learning
- empirical generalization error
Publication and Content Type
- ref (subject category)
- kon (subject category)
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