Neural network-based finite-time control of quantized stochastic nonlinear systems

Abstract The finite-time tracking control of a class of stochastic quantized nonlinear systems is thought about in this article. Different from the studies on conventional finite-time control of stochastic systems, the quantized control problem is first taken into account and the nonlinear terms may be completely unknown. The quantization error and unknown nonlinearities make the existing finite-time stability criterion unavailable. By adopting the approximation ability of neural network, a novel adaptive neural control strategy is proposed, which removes the linear growth condition assumption for nonlinearities in existing finite-time studies. To be convenient for finite-time stability analysis of stochastic nonlinear systems, an important finite time stability criterion in integral form is first set up. Then, combining Jessen’s inequality and the proposed finite-time stability criterion, the finite-time mean square stability of stochastic nonlinear system is proved.

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