A data-driven quadratic stability condition and its application for stabilizing unknown nonlinear systems
نویسندگان
چکیده
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 recurrent neural networks. The effectiveness and the adaptability of the proposed approach is compared with the adaptive feedback linearization method with an example of stabilizing a nonlinear aeroelastic system.
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