Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 2419-2434 |
| Seitenumfang | 16 |
| Fachzeitschrift | IEEE Transactions on Automatic Control |
| Jahrgang | 67 |
| Ausgabenummer | 5 |
| Publikationsstatus | Veröffentlicht - 18 Mai 2021 |
Abstract
We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. The theoretical results are illustrated with an example involving offset free tracking.
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in: IEEE Transactions on Automatic Control, Jahrgang 67, Nr. 5, 18.05.2021, S. 2419-2434.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Constrained nonlinear output regulation using model predictive control
AU - Koehler, Johannes
AU - Muller, Matthias A.
AU - Allgower, Frank
N1 - Publisher Copyright: © 1963-2012 IEEE.
PY - 2021/5/18
Y1 - 2021/5/18
N2 - We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. The theoretical results are illustrated with an example involving offset free tracking.
AB - We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. The theoretical results are illustrated with an example involving offset free tracking.
KW - Constrained control
KW - Disturbance rejection
KW - Incremental system properties
KW - Mathematical model
KW - Minimum phase
KW - Nonresonance condition
KW - Output regulation
KW - Predictive control
KW - Predictive control for nonlinear systems
KW - Regulation
KW - Regulators
KW - Steady-state
KW - Trajectory
KW - Trajectory tracking
KW - Zero dynamics
UR - http://www.scopus.com/inward/record.url?scp=85107227245&partnerID=8YFLogxK
U2 - 10.1109/TAC.2021.3081080
DO - 10.1109/TAC.2021.3081080
M3 - Article
AN - SCOPUS:85107227245
VL - 67
SP - 2419
EP - 2434
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
SN - 0018-9286
IS - 5
ER -