Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2020 59th IEEE Conference on Decision and Control, CDC 2020 |
| Seiten | 4604-4609 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781728174471 |
| Publikationsstatus | Veröffentlicht - 2020 |
Publikationsreihe
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Band | 2020-December |
| ISSN (Print) | 0743-1546 |
| ISSN (elektronisch) | 2576-2370 |
Abstract
In this paper, we show that a simple model predictive control (MPC) scheme can solve the constrained nonlinear output regulation problem without explicitly solving the classical regulator (Francis-Byrnes-Isidori) equations. We first study the general problem of stabilizing a set with MPC using a positive semidefinite (input/output) cost function under suitable stabilizability and detectability assumptions, similar to Grimm et al. (2005) [1]. We show that in the output regulation setting, these conditions hold, if the nonlinear constrained regulation problem is (strictly) feasible, the plant is detectable (i-IOSS) and the control input can be uniquely reconstructed from the plant/reference output. Given these structural assumptions, by simply penalizing the predicted output error in the MPC stage cost, the closed loop implicitly stabilizes a state trajectory that solves the regulator equations, if a sufficiently large prediction horizon is used.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Steuerung und Optimierung
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Mathematik (insg.)
- Modellierung und Simulation
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2020 59th IEEE Conference on Decision and Control, CDC 2020. 2020. S. 4604-4609 9303983 (Proceedings of the IEEE Conference on Decision and Control; Band 2020-December).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung
}
TY - GEN
T1 - Implicit solutions to constrained nonlinear output regulation using MPC
AU - Köhler, Johannes
AU - Müller, Matthias
AU - Allgöwer, Frank
N1 - Funding information: Johannes Köhler would like to thank the German Research Foundation (DFG) for financial support of the project within the International Research Training Group “Soft Tissue Robotics” (GRK 2198/1 - 277536708).
PY - 2020
Y1 - 2020
N2 - In this paper, we show that a simple model predictive control (MPC) scheme can solve the constrained nonlinear output regulation problem without explicitly solving the classical regulator (Francis-Byrnes-Isidori) equations. We first study the general problem of stabilizing a set with MPC using a positive semidefinite (input/output) cost function under suitable stabilizability and detectability assumptions, similar to Grimm et al. (2005) [1]. We show that in the output regulation setting, these conditions hold, if the nonlinear constrained regulation problem is (strictly) feasible, the plant is detectable (i-IOSS) and the control input can be uniquely reconstructed from the plant/reference output. Given these structural assumptions, by simply penalizing the predicted output error in the MPC stage cost, the closed loop implicitly stabilizes a state trajectory that solves the regulator equations, if a sufficiently large prediction horizon is used.
AB - In this paper, we show that a simple model predictive control (MPC) scheme can solve the constrained nonlinear output regulation problem without explicitly solving the classical regulator (Francis-Byrnes-Isidori) equations. We first study the general problem of stabilizing a set with MPC using a positive semidefinite (input/output) cost function under suitable stabilizability and detectability assumptions, similar to Grimm et al. (2005) [1]. We show that in the output regulation setting, these conditions hold, if the nonlinear constrained regulation problem is (strictly) feasible, the plant is detectable (i-IOSS) and the control input can be uniquely reconstructed from the plant/reference output. Given these structural assumptions, by simply penalizing the predicted output error in the MPC stage cost, the closed loop implicitly stabilizes a state trajectory that solves the regulator equations, if a sufficiently large prediction horizon is used.
UR - http://www.scopus.com/inward/record.url?scp=85094769583&partnerID=8YFLogxK
U2 - 10.1109/CDC42340.2020.9303983
DO - 10.1109/CDC42340.2020.9303983
M3 - Conference contribution
SN - 978-1-7281-7446-4
SN - 978-1-7281-7448-8
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 4604
EP - 4609
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
ER -