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

Meichen Guo; Claudio De Persis; Pietro Tesi

doi:10.1016/j.ifacol.2023.10.1787

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

Meichen Guo^*, Claudio De Persis, Pietro Tesi

^*Corresponding author for this work

Team Meichen Guo

Research output: Contribution to journal › Conference article › Scientific › peer-review

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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.

Original language	English
Pages (from-to)	1357-1362
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	56
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.1787
Publication status	Published - 2023
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Keywords

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

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10.1016/j.ifacol.2023.10.1787

1-s2.0-S2405896323021961-mainLicence: CC BY-NC-ND

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title = "Data-driven control of input-affine systems via approximate nonlinearity cancellation",

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.",

keywords = "Data-driven control, learning control, nonlinear control, region of attraction estimation, robust control",

author = "Meichen Guo and {De Persis}, Claudio and Pietro Tesi",

year = "2023",

doi = "10.1016/j.ifacol.2023.10.1787",

language = "English",

volume = "56",

pages = "1357--1362",

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T1 - Data-driven control of input-affine systems via approximate nonlinearity cancellation

AU - Guo, Meichen

AU - De Persis, Claudio

AU - Tesi, Pietro

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Data-driven control

KW - learning control

KW - nonlinear control

KW - region of attraction estimation

KW - robust control

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DO - 10.1016/j.ifacol.2023.10.1787

M3 - Conference article

AN - SCOPUS:85184961280

SN - 2405-8963

VL - 56

SP - 1357

EP - 1362

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 2

T2 - 22nd IFAC World Congress

Y2 - 9 July 2023 through 14 July 2023

ER -

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

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