Luenberger observers for discrete-time nonlinear systems
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Abstract (arXiv)
In this paper, we consider the problem of designing an asymptotic observer for a nonlin-ear dynamical system in discrete-time following Luenberger's original idea. This approach is a two-step design procedure. In a first step, the problem is to estimate a function of the state. The state estimation is obtained by inverting this mapping. Similarly to the continuous-time context, we show that the first step is always possible provided a linear and stable discrete-time system fed by the output is introduced. Based on a weak observ-ability assumption, it is shown that picking the dimension of the stable auxiliary system sufficiently large, the estimated function of the state is invertible. This approach is illustrated on linear systems with polynomial output. The link with the Luenberger observer obtained in the continuous-time case is also investigated.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 7 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
6 papers.
- 2021Learning Stable Koopman Embeddingscited by 18
- 2024Nonlinear Discrete-Time Observers with Physics-Informed Neural Networkscited by 8
- 2025Universal Learning of Nonlinear Dynamicscited by 5
- 2024Spatiotemporal Observer Design for Predictive Learning of High-Dimensional Datacited by 2
- 2023Learning Hybrid Dynamics Models With Simulator-Informed Latent Statescited by 1
- 2026A KKL Observer Perspective on Reservoir Computingcited by 0
- 2026A KKL Observer Perspective on Reservoir Computing3 Oct 2026
- 2025Universal Learning of Nonlinear Dynamics16 Aug 2025
- 2024Spatiotemporal Observer Design for Predictive Learning of High-Dimensional Data23 Feb 2024
- 2024Nonlinear Discrete-Time Observers with Physics-Informed Neural Networks19 Feb 2024
- 2023Learning Hybrid Dynamics Models With Simulator-Informed Latent States6 Sep 2023
- 2021Learning Stable Koopman Embeddings13 Oct 2021