Details
| Original language | English |
|---|---|
| Pages (from-to) | 617-624 |
| Number of pages | 8 |
| Journal | IFAC-PapersOnLine |
| Volume | 56 |
| Issue number | 2 |
| Early online date | 22 Nov 2023 |
| Publication status | Published - 2023 |
| Event | IFAC World Congress 2023 - Pacific Convention Plaza Yokohama, Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 |
Abstract
Keywords
- Data-driven predictive control, Data-based control, Feedback linearization, Nonlinear predictive control
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 56, No. 2, 2023, p. 617-624.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Data-driven Nonlinear Predictive Control for Feedback Linearizable Systems
AU - Alsalti, Mohammad Salahaldeen Ahmad
AU - Lopez Mejia, Victor Gabriel
AU - Berberich, Julian
AU - Allgöwer, Frank
AU - Müller, Matthias A.
N1 - Publisher Copyright: Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
PY - 2023
Y1 - 2023
N2 - We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input and noisy output data along with a set of basis functions that approximate the unknown nonlinearities. Despite the noisy output data as well as the mismatch caused by the use of basis functions, we show that the proposed multi-step robust data-driven nonlinear predictive control scheme is recursively feasible and renders the closed-loop system practically exponentially stable. We illustrate our results on a model of a fully-actuated double inverted pendulum.
AB - We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input and noisy output data along with a set of basis functions that approximate the unknown nonlinearities. Despite the noisy output data as well as the mismatch caused by the use of basis functions, we show that the proposed multi-step robust data-driven nonlinear predictive control scheme is recursively feasible and renders the closed-loop system practically exponentially stable. We illustrate our results on a model of a fully-actuated double inverted pendulum.
KW - Data-driven predictive control
KW - Data-based control
KW - Feedback linearization
KW - Nonlinear predictive control
UR - http://www.scopus.com/inward/record.url?scp=85175777808&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2023.10.1636
DO - 10.1016/j.ifacol.2023.10.1636
M3 - Conference article
VL - 56
SP - 617
EP - 624
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 2
T2 - IFAC World Congress 2023
Y2 - 9 July 2023 through 14 July 2023
ER -