Details
| Original language | English |
|---|---|
| Pages (from-to) | 442-447 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 51 |
| Issue number | 20 |
| Early online date | 22 Nov 2018 |
| Publication status | Published - 2018 |
| Externally published | Yes |
Abstract
A robust model predictive control (RMPC) approach for linear systems with bounded state-dependent uncertainties is proposed. Such uncertainties can arise from unmodeled non-linearities or external disturbances, for example. By explicitly considering the state dependency of the uncertainty sets in the RMPC approach, it is shown how closed-loop performance can be improved over existing approaches that consider worst-case uncertainty. Being able to handle state-dependent uncertainties is particularly relevant in learning-based MPC where the system model is learned from data and confidence in the model typically varies over the state space. The efficacy of the proposed approach for learning-based RMPC is illustrated with a numerical example, where uncertainty sets are obtained from data using Gaussian Process regression.
Keywords
- Gaussian Process, Learning-based MPC, Robust MPC, State-dependent uncertainty
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 51, No. 20, 2018, p. 442-447.
Research output: Contribution to journal › Article › Research
}
TY - JOUR
T1 - Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty
AU - Soloperto, Raffaele
AU - Müller, Matthias A.
AU - Trimpe, Sebastian
AU - Allgöwer, Frank
N1 - Publisher Copyright: © 2018 Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - A robust model predictive control (RMPC) approach for linear systems with bounded state-dependent uncertainties is proposed. Such uncertainties can arise from unmodeled non-linearities or external disturbances, for example. By explicitly considering the state dependency of the uncertainty sets in the RMPC approach, it is shown how closed-loop performance can be improved over existing approaches that consider worst-case uncertainty. Being able to handle state-dependent uncertainties is particularly relevant in learning-based MPC where the system model is learned from data and confidence in the model typically varies over the state space. The efficacy of the proposed approach for learning-based RMPC is illustrated with a numerical example, where uncertainty sets are obtained from data using Gaussian Process regression.
AB - A robust model predictive control (RMPC) approach for linear systems with bounded state-dependent uncertainties is proposed. Such uncertainties can arise from unmodeled non-linearities or external disturbances, for example. By explicitly considering the state dependency of the uncertainty sets in the RMPC approach, it is shown how closed-loop performance can be improved over existing approaches that consider worst-case uncertainty. Being able to handle state-dependent uncertainties is particularly relevant in learning-based MPC where the system model is learned from data and confidence in the model typically varies over the state space. The efficacy of the proposed approach for learning-based RMPC is illustrated with a numerical example, where uncertainty sets are obtained from data using Gaussian Process regression.
KW - Gaussian Process
KW - Learning-based MPC
KW - Robust MPC
KW - State-dependent uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85056847649&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2018.11.052
DO - 10.1016/j.ifacol.2018.11.052
M3 - Article
VL - 51
SP - 442
EP - 447
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 20
ER -