Asymptotic Stability of POD based Model Predictive Control for a semilinear parabolic PDE
Not on the map. Why?
Abstract (arXiv)
In this article a stabilizing feedback control is computed for a semilinear parabolic partial differential equation utilizing a nonlinear model predictive (NMPC) method. In each level of the NMPC algorithm the finite time horizon open loop problem is solved by a reduced-order strategy based on proper orthogonal decomposition (POD). A stability analysis is derived for the combined POD-NMPC algorithm so that the lengths of the finite time horizons are chosen in order to ensure the asymptotic stability of the computed feedback controls. The proposed method is successfully tested by numerical examples.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2013.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.
- 2024Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order modelscited by 4
- 2024Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order modelscited by 0
- 2026Real-time optimal control with shallow recurrent decoder networkscited by 0
- 2026Real-time optimal control with shallow recurrent decoder networks21 Jul 2026
- 2024Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models13 Dec 2024
- 2024Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models9 Sep 2024