High Gain Embedding Observer Design: Combining Differential Geometry and Algebra with Machine Learning
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
High gain observers are often used for the real-time estimation of the state of nonlinear systems. Several design methods are based on normal forms, which are based on differential geometric considerations. The embedding into a higher dimensional state could be advantageous regarding existence conditions and convergence. However, computation and implementation of such embedding observers is usually significantly more challenging. In this paper, we circumvent some of these problems by employing machine learning. The required functions for the implementation of the observer are approximated by neural networks.
Details
Original language | English |
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Title of host publication | 2023 27th International Conference on System Theory, Control and Computing (ICSTCC) |
Publisher | IEEE |
Pages | 62-69 |
Number of pages | 8 |
ISBN (electronic) | 9798350337983 |
ISBN (print) | 979-8-3503-3799-0 |
Publication status | Published - 13 Oct 2023 |
Peer-reviewed | Yes |
Conference
Title | 2023 27th International Conference on System Theory, Control and Computing |
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Abbreviated title | ICSTCC 2023 |
Conference number | 27 |
Duration | 11 - 13 October 2023 |
Website | |
Degree of recognition | International event |
Location | Ibis Timisoara City Center Hotel |
City | Timisoara |
Country | Romania |
External IDs
Scopus | 85179509515 |
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ORCID | /0000-0002-3347-0864/work/156338278 |
Keywords
Keywords
- Design methodology, Machine learning, Neural networks, Noise measurement, Observers, Real-time systems, Uncertain systems