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Observer-based Finite-time Control of Stochastic Non-strict-feedback Nonlinear Systems

Yan Zhang and Fang Wang*
International Journal of Control, Automation, and Systems, vol. 19, no. 2, pp.655-665, 2021

Abstract : This paper investigates the observer-based adaptive finite-time neural control issue of stochastic nonstrict-feedback nonlinear systems. By establishing a state observer and utilizing the approximation property of the neural network, an adaptive neural network output-feedback controller is constructed. The controller solves the issue that the states of stochastic nonlinear system cannot be measured, and assures that all signals in the closed-loop system are bounded. Different from the existing adaptive control researches of stochastic nonlinear systems with unmeasured states, the proposed control scheme can guarantee the finite-time stability of the stochastic nonlinear systems. Furthermore, the effectiveness of the proposed control approach is verified by the simulation results.

Keyword : Adaptive neural control, finite-time control, non-strict-feedback form, state observer, stochastic nonlinear systems

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