Data-driven control of input-affine systems via approximate nonlinearity cancellation
M. Guo (TU Delft - Team Meichen Guo)
Claudio De Persis (Rijksuniversiteit Groningen)
Pietro Tesi (University of Florence)
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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.
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