Interval Observers for a Class of Nonlinear Systems Using Gaussian Process Models
Abstract:
An interval observer design approach for partially
unknown nonlinear systems is developed, where the unknown
system component is modeled using Gaussian processes and
noisy system measurements. The proposed method is applicable
for bounded nonlinear systems where the system uncertainty
is described by a Lipschitz continuous function. The interval
observer generates a correct estimation error with high probability,
and the error bound is decreased by employing new
training data points
Keywords:
Gaussian process, data-driven, machine learning, interval observer, nonlinear system