Control Contraction Metric Synthesis for Discrete-time Nonlinear Systems
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Abstract (arXiv)
Flexible manufacturing has been the trend in the area of the modern chemical process nowadays. One of the essential characteristics of flexible manufacturing is to track time-varying target trajectories (e.g. diversity and quantity of products). A possible tool to achieve time-varying targets is contraction theory. However, the contraction theory was developed for continuous time systems and there lacks analysis and synthesis tools for discrete-time systems. This article develops a systematic approach to discrete-time contraction analysis and control synthesis using Discrete-time Control Contraction Metrics (DCCM) which can be implemented using Sum of Square (SOS) programming. The proposed approach is demonstrated by illustrative example.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Apr 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2025Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systemscited by 5
- 2021Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networkscited by 2
- 2026Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorialcited by 1
- 2026Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial26 May 2026
- 2025Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems23 Apr 2025
- 2021Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks12 May 2021