Name
Title | ||
|---|---|---|
Immersion-based model predictive control of constrained nonlinear systems: Polyflow approximation |
Abstract | ||
|---|---|---|
In the framework of Model Predictive Control (MPC), the control input is typically computed by solving optimization problems repeatedly online. For general nonlinear systems, the online optimization problems are non-convex and computationally expensive or even intractable. In this paper, we propose to circumvent this issue by computing a high-dimensional linear embedding of discrete-time nonlinear systems. The computation relies on an algebraic condition related to the immersibility property of nonlinear systems and can be implemented offline. With the high-dimensional linear model, we then define and solve a convex online MPC problem. We also provide an interpretation of our approach under the Koopman operator framework. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/ECC54610.2021.9655233 | 2021 EUROPEAN CONTROL CONFERENCE (ECC) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheming Wang | 1 | 30 | 8.12 |
| Raphaël M. Jungers | 2 | 0 | 1.35 |