.png){kind=icon}
.png){kind=icon}
Return
Data-Driven MPC With Stability Guarantees Using Extended Dynamic Mode Decomposition
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2024.3431169.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
For nonlinear (control) systems, extended dynamic mode decomposition (EDMD) is a popular method to obtain data-driven surrogate models. Its theoretical foundation is the Koopman framework, in which one propagates observable functions of the state to obtain a linear representation in an infinite-dimensional space. In this article, we prove practical asymptotic stability of an (controlled) equilibrium for EDMD-based model predictive control, in which the optimization step is conducted using the data-based surrogate model. To this end, we derive novel bounds on the estimation error that are proportional to the norm of state and control. This enables us to show that, if the underlying system is cost controllable, this stabilizablility property is preserved. We conduct numerical simulations illustrating the proven practical asymptotic stability.
Keywords:
Asymptotic stability
Costs
Generators
Dictionaries
Controllability
Numerical stability
Computational modeling
Cost controllability
data-driven
dynamic mode decomposition
model predictive control (MPC)
stability
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W