Mathematics and Machine Learning for Intelligent Perception, Decision-Making, and Control

Dear Colleagues,

This special issue, titled “Mathematics and Machine Learning for Intelligent Perception, Decision-Making, and Control,” aims to explore the intersection and cutting-edge advances of mathematical methods and machine learning techniques in intelligent perception, decision-making, and control systems. The focus lies in how mathematical modeling, intelligent algorithms, and data-driven approaches can enhance the perception accuracy, decision-making efficiency, and control robustness of intelligent systems.

In the context of rapid advancements in intelligent systems, perception, decision-making, and control constitute the core link from information acquisition to task execution. This issue emphasizes how advanced mathematical theories—such as optimization, nonlinear systems analysis, game theory, and probabilistic reasoning—can be integrated with machine learning models, including deep learning, reinforcement learning, and generative models, to promote adaptive and autonomous evolution in intelligent systems.

Intelligent perception provides the foundation for environmental understanding and information fusion. By introducing multimodal data processing, pattern recognition, and visual computing techniques, intelligent systems can achieve high-precision situational awareness in complex and uncertain environments. In the decision-making and control phases, frameworks that combine machine learning with optimal control enable closed-loop optimization from data to action, ensuring stability and efficiency under dynamic missions and disturbance conditions.

Moreover, this issue also focuses on integrated research based on mathematical optimization, game-theoretic modeling, and learning-driven control, highlighting collaborative decision-making in multi-agent systems, strategic interactions in complex environments, and the safety and resilience control of intelligent systems. Through the deep integration of mathematical theory and artificial intelligence algorithms, this special issue seeks to advance the dual breakthroughs of performance enhancement and intelligence evolution in next-generation intelligent systems—covering unmanned surface vessels, underwater vehicles, unmanned aerial vehicles, as well as robotics, autonomous transportation, and industrial intelligence.

Overall, this special issue aims to showcase innovative achievements in mathematics and machine learning for intelligent perception, decision-making, and control, fostering interdisciplinary integration and theoretical innovation. We sincerely invite researchers from mathematics, artificial intelligence, control, and systems science to join us in exploring frontier challenges and laying the theoretical and methodological foundation for building smarter, safer, and more efficient autonomous systems of the future.

Dr. Changdong Yu
Prof. Dr. Xiao Liang
Dr. Xiaojie Sun
Guest Editors

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