Intelligent Perception and Control for Complex Systems

Dear Colleagues,

Modern complex systems are increasingly characterized by nonlinearity, uncertainty, multi-agent interactions, and stringent real-time requirements. These intrinsic features pose significant challenges to traditional control methodologies, which often struggle to maintain performance under dynamic evolution and environmental disturbances. In response, a new generation of intelligent perception and control frameworks has emerged, deeply integrating sensing, prediction, learning, and decision making to enhance system robustness, adaptability, and coordination capabilities.

This Special Issue on "Intelligent Perception and Control for Complex Systems" aims to explore state-of-the-art intelligent methodologies that address the challenges of complexity across diverse application domains. We particularly focus on two representative complex system domains: modern energy systems and intelligent robotics. In energy systems, the integration of renewable sources, coordination of multi-microgrids, vehicle-to-grid interactions, and building energy management present unprecedented control complexity. Similarly, in robotics and autonomous systems, bionic robots, underwater/aerial vehicles, multi-robot collaboration, and sensor fusion demand advanced perception-control co-design strategies. These domains share fundamental challenges in achieving efficient, reliable, and autonomous operation under uncertainty—constituting the core scientific questions this Special Issue seeks to address.

We welcome original research articles and comprehensive reviews that advance the theoretical foundations and practical applications of intelligent perception and control. By bridging energy systems and robotics through the common lens of complexity, this collection will usefully supplement the literature with cross-domain insights, integrated perception-control frameworks, and novel solutions to shared challenges in uncertainty management, distributed coordination, and real-time decision making.

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

• Intelligent control for complex energy networks;
• Intelligent control of robots in complex environments;
• Perception-control co-design and integrated architectures;
• Robust and adaptive control under uncertainty and disturbances;
• AI-driven predictive control and learning-based optimization;
• Digital twins and model-based control for complex systems;
• Edge intelligence and real-time perception for autonomous systems.

Dr. Yu Wang
Dr. Huijie Dong
Guest Editors

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