Data-driven control of input-affine systems via approximate nonlinearity cancellation

Guo, M.; De Persis, Claudio; Tesi, Pietro

doi:10.1016/j.ifacol.2023.10.1787

Data-driven control of input-affine systems via approximate nonlinearity cancellation

Title

Data-driven control of input-affine systems via approximate nonlinearity cancellation

Author

Guo, M. (TU Delft Team Meichen Guo)
De Persis, Claudio (Rijksuniversiteit Groningen)
Tesi, Pietro (University of Florence)

Date

2023

Abstract

We consider data-driven control of input-affine systems via approximate nonlinearity cancellation. Data-dependent semi-definite program is developed to characterize the stabilizer such that the linear dynamics of the closed-loop systems is stabilized and the influence of the nonlinear dynamics is decreased. Because of the additional nonlinearity brought by the state-dependent input vector field, nonlinearity cancellation is more difficult to achieve. We show that under some assumptions on the nonlinearity, the nonlinearity cancellation control approach can render the equilibrium locally asymptotically stable even if the additional nonlinearity is neglected. Data-based estimation of the region of the attraction is also presented.

Subject

Data-driven control
learning control
nonlinear control
region of attraction estimation
robust control

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http://resolver.tudelft.nl/uuid:bd4f8e76-e4f2-4054-b376-a873aee5ac17

DOI

https://doi.org/10.1016/j.ifacol.2023.10.1787

Source

IFAC-PapersOnLine, 56 (2), 1357-1362

Event

22nd IFAC World Congress, 2023-07-09 → 2023-07-14, Yokohama, Japan

Part of collection

Institutional Repository