Advanced Filtering and Control Methods for Stochastic Systems

Dear Colleagues,

Stochastic systems are ubiquitous in engineering, economics, environmental sciences, and many other domains where uncertainty, randomness, and noise play a crucial role in system dynamics and decision-making processes. Over the past few decades, filtering and control theory for stochastic systems has witnessed substantial advances, enabling improved estimation, prediction, optimization, and stability under uncertain conditions. This Special Issue on "Advanced Filtering and Control Methods for Stochastic Systems" aims to provide a platform for the dissemination of the latest research findings, theoretical developments, and innovative applications in this field. It calls for research that addresses emerging challenges in modeling, filtering, robust control, optimal estimation, fault detection, learning-based approaches, and real-time implementation for systems affected by stochastic disturbances.

Topics of interest include, but are not limited to, the following:

• Kalman filtering and nonlinear filtering techniques;
• H-infinity and robust filtering under stochastic uncertainties;
• Data-driven filtering and estimation using machine learning;
• Distributed filtering and control in networked stochastic systems;
• Stochastic model predictive control and optimal control methods;
• Fault detection, isolation, and diagnosis for uncertain systems;
• Filtering and control of Markovian jump systems and time-delay systems;
• Applications in robotics, autonomous systems, aerospace, energy systems, financial engineering, and beyond.

We encourage submissions that combine rigorous mathematical analysis with practical relevance or demonstrate real-world implementations and simulations. Both theoretical and application-oriented contributions are welcome.

This Special Issue aims to provide researchers and practitioners with a comprehensive overview of state-of-the-art methods in the filtering and control of stochastic systems and to highlight future research directions in this rapidly evolving area.

Prof. Dr. Fan Wang
Dr. Qiang Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• stochastic systems
• networked control systems
• Kalman filter and variants
• robust control
• H-infinity filtering
• stochastic optimal control
• model predictive control
• data-driven estimation
• machine learning in control
• distributed filtering
• fault detection and diagnosis