Fractional data-driven model for stabilization of uncertain discrete-time nonlinear systems. (English) Zbl 1501.93109
Summary: This paper proposes a novel data-driven control for stabilization of a class of uncertain discrete-time nonlinear systems. The proposed method is based on the compact form dynamic linearization technique, which relates the first variation of the output signal with the fractional-order variation of the input one. Then, a discrete-time controller is proposed, based on the obtained fractional-order data-driven equivalent model. In order to compute the proposed controller and estimator, only input-output data information is considered. The uniform ultimately boundedness of the tracking errors are demonstrated by a formal analysis. Finally, comparison results based on simulations are presented to highlight the effectiveness of the proposed methodology.
MSC:
| 93D05 | Lyapunov and other classical stabilities (Lagrange, Poisson, \(L^p, l^p\), etc.) in control theory |
| 93C57 | Sampled-data control/observation systems |
| 26A33 | Fractional derivatives and integrals |
| 93C55 | Discrete-time control/observation systems |
| 93C41 | Control/observation systems with incomplete information |
| 93C10 | Nonlinear systems in control theory |
Keywords:
data-driven control; stabilization; uncertain discrete-time nonlinear systems; fractional-order variationReferences:
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