Preassigned-Time Sliding-Mode Control of Chaotic Memristive Neural Networks With Time-Varying Delays

Abstract

Preassigned-time (PASST) control of memrisitive neural networks has been a hot research point recently. Different from the finite-time control with stable time dependent on the initial condition of the system, this brief studies the PASST control, and the stable time of which is uncorrelated with the initial condition and can be set in advance. For a class of chaotic memristive neural networks with time delays, a sliding-mode based approach is designed to realize the PASST stability. Different from the finite-time stability, the upper bound of stable time is not related to or constricted by the initial condition, and it can be arbitrarily defined for practical requirement. Moreover, as the special cases, the exponential stability and fixed-time stability are also presented via the same framework of the sliding-mode based approach. Finally, a chaotic numerical example with several comparative cases are given to verify the validity of the control method of PASST stability results.