Mean-Square Ulam–Hyers–Rassias Stability of Riemann–Liouville Fractional Stochastic Differential Equations

Fractional stochastic differential equations with memory effects are fundamental in modeling phenomena across physics, biology, and finance, where long-range dependencies and random fluctuations coexist, yet their stability analysis under non-Lipschitz conditions remains a significant challenge, particularly for systems involving Riemann–Liouville fractional operators with stochastic perturbations. To address this challenge, this article establishes sufficient conditions for the existence, uniqueness, and Ulam–Hyers–Rassias (UHR) stability of mild solutions to nonlinear Riemann–Liouville fractional stochastic differential equations (RL-FSDEs) driven by additive white noise. Through the application of a generalized Banach contraction principle within a carefully constructed weighted metric space, our methodology relaxes the restrictive Lipschitz constraints commonly found in existing literature, thereby significantly broadening the class of admissible nonlinearities. The UHR stability is rigorously established in the mean-square sense by deriving explicit bounds that systematically relate approximate solutions to exact trajectories. Our theoretical findings are substantiated through four illustrative examples demonstrating that solutions remain consistently close to approximate trajectories under random fluctuations, thereby significantly advancing the stability theory of stochastic fractional systems and providing a robust analytical framework for applications in control theory, financial modeling, and engineering systems governed by memory effects and stochastic noise.