Ⅰ. Theoretical Foundations & AI-Control Fusion Frameworks (理论基础与AI-控制融合架构)

• Physics-informed machine learning for modeling and system identification (物理信息驱动的机器学习建模与系统辨识)
• Novel architectures integrating deep learning with modern control theory (融合深度学习与现代控制理论的新型架构)
• Stability, convergence, and robustness analysis of neural control systems (神经控制系统的稳定性、收敛性与鲁棒性分析)
• Data-driven approximation methods for complex dynamical systems (基于数据驱动的复杂动力学系统近似方法)

Ⅱ. Intelligent Autonomous Decision-Making, Planning, and Optimization (智能自主决策、规划与优化)

• Online mission planning and path optimization in uncertain environments (动态不确定环境下的在线任务规划与路径优化)
• Logic reasoning and task decomposition for engineering systems using LLMs (基于大语言模型（LLM）的工程系统逻辑推理与任务分解)
• End-to-end learning algorithms for autonomous control (端到端（End-to-End）自主控制学习算法)
• Real-time optimization and predictive control for high-dimensional nonlinear systems (高维非线性系统的实时优化与预测控制)

Ⅲ. Safety-Critical Control and Explainability (安全关键型控制与可解释性)

• Safe Reinforcement Learning (Safe RL) with stability guarantees (保障稳定性的安全强化学习（Safe RL）方法)
• Formal verification, testing, and safety assessment of AI controllers (AI控制器的形式化验证、测试与安全性评估)
• Explainability and transparency of AI decision-making in engineering (工程领域AI决策过程的可解释性与透明度研究)
• Fault diagnosis, fault-tolerant control, and self-healing under extreme conditions (极端工况下的故障诊断、容错控制与系统自修复)

Ⅳ. Multi-Agent Coordination and Distributed Autonomy (多智能体协同与分布式自主)

• Collaborative perception and control of large-scale heterogeneous swarms (大规模异构机器人集群的协同感知与控制)
• Edge computing-based communication and control for distributed systems (基于边缘计算的分布式自主系统通信与控制)
• Multi-agent reinforcement learning for complex engineering networks (多智能体强化学习在复杂工程网络中的应用)
• Autonomous coordination under communication constraints and adversarial settings (通信受限与对抗环境下的自主协同技术)

Ⅴ. Applications in Key Engineering Domains (典型工程领域应用研究)

• Autonomous control for aerospace vehicles, UAVs, and satellites (航空航天飞行器、无人机与卫星的自主控制)
• Intelligent scheduling and operation of smart grids and energy internet (智能电网、能源互联网的智能化调度与运行)
• Autonomous navigation for self-driving cars, ITS, and underwater vehicles (自动驾驶、智能交通与水下航行器的自主导航)
• AI implementation in smart manufacturing, bio-inspired robots, and industrial processes (智能制造、仿生机器人及工业过程控制的AI实践)

• Modern engineering systems—such as aerospace vehicles, smart grids, and robotic swarms—are becoming increasingly high-dimensional, nonlinear, and uncertain. Traditional control methods often reach their limits in these scenarios. AI-enabled autonomous control is vital because it overcomes modeling bottlenecks and significantly enhances a system's adaptability, robustness, and efficiency in extreme environments, serving as a core engine for industrial intelligence.

• 现代工程系统（如空天飞行器、智能电网、机器人集群）日益呈现高维、非线性和强不确定性，传统控制方法难以应对。AI赋能的自主控制能突破建模瓶颈，显著提升系统在极端动态环境下的自适应能力、稳健性与运行效率，是实现工业智能化转型的核心引擎。