Convergence Properties for Discrete-time Nonlinear Systems
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Abstract (arXiv)
Three similar convergence notions are considered. Two of them are the long established notions of convergent dynamics and incremental stability. The other is the more recent notion of contraction analysis. All three convergence notions require that all solutions of a system converge to each other. In this paper, we investigate the differences between these convergence properties for discrete-time, time-varying nonlinear systems by comparing the properties in pairs and using examples. We also demonstrate a time-varying smooth Lyapunov function characterization for each of these convergence notions, and, with appropriate assumptions, we provide several sufficient conditions to establish relationships between these properties in terms of Lyapunov functions.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2016.
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Citing papers per year: 2021: 4; 2022: 0; 2023: 2; 2024: 0; 2025: 3; 2026: 1.
10 papers.
- 2023Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems (Extended Version)cited by 6
- 2025Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systemscited by 5
- 2021Predictability and Fairness in Load Aggregation and Operations of Virtual Power Plantscited by 4
- 2021Nonlinear Autoregression with Convergent Dynamics on Novel Computational Platformscited by 3
- 2023Improving the Performance of Echo State Networks Through State Feedbackcited by 2
- 2025Echoes of the Past: A Unified Perspective on Fading memory and Echo Statescited by 2
- 2026Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorialcited by 1
- 2021Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustnesscited by 0
- 2021Distributed Identification of Contracting and/or Monotone Network Dynamicscited by 0
- 2025A Solvable Molecular Switch Model for Stable Temporal Information Processingcited by 0
- 2026Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial26 May 2026
- 2025Echoes of the Past: A Unified Perspective on Fading memory and Echo States26 Aug 2025
- 2025A Solvable Molecular Switch Model for Stable Temporal Information Processing21 Aug 2025
- 2025Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems23 Apr 2025
- 2023Improving the Performance of Echo State Networks Through State Feedback23 Dec 2023
- 2023Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems (Extended Version)12 Apr 2023
- 2021Predictability and Fairness in Load Aggregation and Operations of Virtual Power Plants6 Oct 2021
- 2021Nonlinear Autoregression with Convergent Dynamics on Novel Computational Platforms18 Aug 2021
- 2021Distributed Identification of Contracting and/or Monotone Network Dynamics29 Jul 2021
- 2021Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness30 Mar 2021