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

Meichen Guo*, Claudio De Persis, Pietro Tesi

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-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 languageEnglish
Pages (from-to)1357-1362
Number of pages6
JournalIFAC-PapersOnLine
Volume56
Issue number2
DOIs
Publication statusPublished - 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023

Keywords

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

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