Advances in Control of Stochastic Dynamical Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C2: Dynamical Systems".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 94
Special Issue Editors
Interests: stochastic differential equations and dynamical systems; biomathematics; delay differential equations；stochastic almost periodic theory and its applications
Special Issue Information
Dear Colleagues,
Stochasticity is an inherent feature in numerous real-world dynamical systems, arising from internal uncertainties, external disturbances, or unmodeled dynamics. Traditional control methods, often designed for deterministic systems, may fail to ensure satisfactory performance or stability when confronted with stochastic influences. The study of control strategies for stochastic dynamical systems has thus become increasingly crucial, with applications spanning robotics, aerospace, finance, and biomedicine, where reliable operation under random perturbations is paramount.
This Special Issue, "Advance in Control of Stochastic Dynamical Systems", aims to bring together cutting-edge research that advances the theory, methodology, and applications of control for stochastic systems. We seek to showcase innovative approaches to addressing challenges such as stochastic stability analysis, robust and adaptive control design, optimal control under uncertainty, and filtering and estimation for stochastic dynamics. By integrating insights from probability theory, control engineering, applied mathematics, and related disciplines, this Issue aims to foster cross-disciplinary dialogue and drive the development of practical solutions for real-world stochastic control problems.
We invite researchers, engineers, and scholars to submit original research articles, review papers, and case studies that contribute to this vibrant field. Topics of interest include, but are not limited to, stochastic stability and stabilization, stochastic optimal control, adaptive and learning-based control for stochastic systems, filtering and state estimation in stochastic environments, and applications of stochastic control in various engineering and scientific domains.
Your contributions will help shape the landscape of stochastic control research, providing valuable insights for both theoretical advancement and practical implementation. We look forward to your submissions and the opportunity to highlight the latest progress in this important area.
Dr. Xiaofeng Zhang
Prof. Dr. Jun Cheng
Guest Editors
Manuscript Submission Information
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Keywords
- stochastic control theory
- stochastic dynamical systems
- stochastic stability
- robust optimization
- adaptive control for stochastic systems
- stochastic differential equations
- machine learning in control
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