Details
| Original language | English |
|---|---|
| Article number | 8723153 |
| Pages (from-to) | 859-864 |
| Number of pages | 6 |
| Journal | IEEE Control Systems Letters |
| Volume | 3 |
| Issue number | 4 |
| Early online date | 27 May 2019 |
| Publication status | Published - Oct 2019 |
Abstract
In systems subject to communication constraints, carefully scheduling the transmission of updated control values can greatly improve the trade-off between communication effort and control performance. In this letter, we consider a dynamical communication network together with a predictive controller that has explicit knowledge thereof. In the usual fashion of rollout strategies in networked control, the controller both schedules transmissions and computes the corresponding control values. Using tools from model predictive control, stability of the considered setup for nonlinear, constrained plants is established. The special case of linear plants is investigated in more detail. Furthermore, strict performance improvement over a feasible baseline control is established in case that the plant is additionally unconstrained. By means of a numerical example, effectiveness of the considered approach is demonstrated.
Keywords
- control over communications, Networked control systems, predictive control for nonlinear systems
ASJC Scopus subject areas
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Control and Systems Engineering
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In: IEEE Control Systems Letters, Vol. 3, No. 4, 8723153, 10.2019, p. 859-864.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predictive Control Over a Dynamical Token Bucket Network
AU - Wildhagen, Stefan
AU - Muller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding information: This work was supported by the German Research Foundation under Project AL 316/13-1.
PY - 2019/10
Y1 - 2019/10
N2 - In systems subject to communication constraints, carefully scheduling the transmission of updated control values can greatly improve the trade-off between communication effort and control performance. In this letter, we consider a dynamical communication network together with a predictive controller that has explicit knowledge thereof. In the usual fashion of rollout strategies in networked control, the controller both schedules transmissions and computes the corresponding control values. Using tools from model predictive control, stability of the considered setup for nonlinear, constrained plants is established. The special case of linear plants is investigated in more detail. Furthermore, strict performance improvement over a feasible baseline control is established in case that the plant is additionally unconstrained. By means of a numerical example, effectiveness of the considered approach is demonstrated.
AB - In systems subject to communication constraints, carefully scheduling the transmission of updated control values can greatly improve the trade-off between communication effort and control performance. In this letter, we consider a dynamical communication network together with a predictive controller that has explicit knowledge thereof. In the usual fashion of rollout strategies in networked control, the controller both schedules transmissions and computes the corresponding control values. Using tools from model predictive control, stability of the considered setup for nonlinear, constrained plants is established. The special case of linear plants is investigated in more detail. Furthermore, strict performance improvement over a feasible baseline control is established in case that the plant is additionally unconstrained. By means of a numerical example, effectiveness of the considered approach is demonstrated.
KW - control over communications
KW - Networked control systems
KW - predictive control for nonlinear systems
UR - http://www.scopus.com/inward/record.url?scp=85067098818&partnerID=8YFLogxK
U2 - 10.1109/LCSYS.2019.2919264
DO - 10.1109/LCSYS.2019.2919264
M3 - Article
VL - 3
SP - 859
EP - 864
JO - IEEE Control Systems Letters
JF - IEEE Control Systems Letters
IS - 4
M1 - 8723153
ER -