Stochastic Maximum Principles and Linear-Quadratic Optimal Control Problems for Fractional Backward Stochastic Evolution Equations in Hilbert Spaces

TL;DR

This paper develops a stochastic maximum principle for fractional backward stochastic evolution equations in Hilbert spaces and applies it to explicitly solve linear-quadratic optimal control problems, bridging fractional calculus and stochastic control.

Contribution

It introduces a necessary optimality condition for fractional backward stochastic systems and derives explicit solutions for linear-quadratic control problems in infinite-dimensional spaces.

Findings

Established a stochastic maximum principle for FBSEEs.

Derived explicit solutions for LQ control problems.

Connected fractional calculus with stochastic control theory.

Abstract

This paper develops a comprehensive framework for optimal control of systems governed by fractional backward stochastic evolution equations (FBSEEs) in Hilbert spaces. We first establish a stochastic maximum principle (SMP) as a necessary condition for optimality. This is achieved by introducing spike variations, deriving precise estimates for the associated variational equations, and constructing an adjoint process tailored to the fractional dynamics. Subsequently, we apply this general principle to solve the linear-quadratic (LQ) optimal control problem explicitly. The resulting optimal control is characterized in closed form via the adjoint process and is shown to be governed by a system of coupled fractional forward-backward stochastic equations. Our work bridges fractional calculus with stochastic control theory, providing a rigorous foundation for controlling infinite-dimensional systems with memory and long-range dependencies.