Event-triggered fast finite-time adaptive neural prescribed tracking control for non-affine stochastic nonlinear systems with deception attacks

Abstract

This paper copes with a matter of event-triggered fast finite-time prescribed tracking control for non-affine stochastic nonlinear systems, in which deception attacks are taken into consideration. The main objective of the proposed controller is that all the closed-loop variables are bounded in probability, and the tracking error is constrained to the predefined funnel functions by choosing suitable parameters. Primarily, the mean value theorem is utilised to transform the considered systems into the equivalent systems with affine structures. Next, based on the backstepping algorithm, a modified event-triggered scheme is developed to save the network resources. Meanwhile, the unknown parts in the designed systems are approximated by neural networks. Finally, an actual simulation is conducted to verify the validity of the theorem.

Keywords:

• Adaptive neural control
• event-triggered control(ETC)
• non-affine stochastic nonlinear systems
• fast finite-time
• deception attacks

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data included in this study are available upon request by contact with the corresponding author.

Additional information

Funding

This work was supported by the Key Natural Science Research Foundation for University of Anhui [grant/award number: 2022AH051449].