Abstract
This paper proposes a data-driven stability criterion for quadratic stabilization of unknown nonlinear discrete-time systems. The novelty of this quadratic stability criterion lies in the direct use of the time series of system states, instead of using mathematical models. The data-driven stability criterion is utilized to design a control for stabilizing unknown nonlinear systems using online black-box system identification. The effectiveness and the adaptability of the proposed approach are compared with those of adaptive feedback linearization method with an example of stabilizing a nonlinear aeroelastic system.
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