Hybrid Impulsive Feedback Control for Drive–Response Synchronization of Fractional-Order Multi-Link Memristive Neural Networks with Multi-Delays

Abstract

This article addresses the issue of drive–response synchronization in fractional-order multi-link memristive neural networks (FMMNN) with multiple delays, under hybrid impulsive feedback control. To address the impact of multiple delays on system synchronization, an extended fractional-order delayed comparison principle incorporating impulses is established. By leveraging Laplace transform, Mittag–Leffler functions, the generalized comparison principle, and hybrid impulsive feedback control schemes, several new sufficient conditions are derived to ensure synchronization in the addressed FMMNN. Unlike existing studies on fractional-order single-link memristor-based systems, our response network is a multi-link model that considers impulsive effects. Notably, the impulsive gains αi are not limited to a small interval, thus expanding the application range of our approach (αi∈(−2,0)∪(−∞,−2)∪(0,+∞)). This feature allows one to choose impulsive gains and corresponding impulsive intervals that are appropriate for the system environment and control requirements. The theoretical results obtained in this study contribute to expanding the relevant theoretical achievements of fractional-order neural networks incorporating memristive characteristics.

Adaptive prescribed performance tracking control for underactuated unmanned surface ships with input quantization

“…However, the dynamics of these ships are time-varying, nonlinear, and often subject to complex ocean disturbances such as currents, waves, and tides. Despite many challenges, some control methods have been proposed to effectively reduce the influence of external unknown interference and model uncertainty, such as sliding mode control [1][2][3] , backstepping adaptive control [4][5][6] , and neural network-based control [7][8][9][10][11][12][13] .…”

A memristive system: dynamics, synchronization, parameter modulation and demodulation

“…The fractional-order delayed memristive fuzzy neural networks was investigated in [13]. Additionally, many synchronization schemes for memristive system have been studied, such as finite-time quantized synchronization method was studied in [14], UDE-based complete synchronization and antisynchronization method was proposed in [15], switching SMC combina tion synchronization and parameter identification method was considered in [16], finite-time H ∞ synchronization method was described in [17], adaptive fuzzy-impulsive synchronization method was focused in [18], active projective synchronization method was described in [19], passive-constrained synchronization control method was given in [20], inverse matrix projective difference synchronization method and its application were investigated in [21], and other synchronization control schemes were described in [22][23][24][25][26]. An image encryption method employing memristive map was introduced in [27].…”