A dynamic control decision approach for fixed-wing aircraft games via hybrid action reinforcement learning

作     者：Xing ZHUANG Dongguang LI Hanyu LI Yue WANG Jihong ZHU 

作者机构：Science and Technology on Electromechanical Dynamic Control LaboratoryBeijing Institute of Technology Department of Precision Instrument Tsinghua University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2025年第68卷第3期

页      面：197-219页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081105[工学-导航、制导与控制] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 080201[工学-机械制造及其自动化] 

基　　金：supported by China National Defense Basic Research Programs (Grant No. JCKY2021204B104) 

主　　题：intelligent air combat unmanned aerial vehicle game dynamic control reinforcement learning 

摘      要：Autonomous decision-making is crucial for aircraft to achieve quick victories in diverse scenarios. Based on a 6-degree-of-freedom aircraft model, this paper proposes a decoupled guidance and control theory for autonomous aircraft maneuvering, distinguishing between close and long-range engagements. We introduce a method for heading attitude control to enhance stability during close-range interactions and a speed-based adaptive grid model for precise waypoint control in mid-to-long-range engagements. The paper transforms dynamic aircraft interactions into a Markov decision process and presents a hybrid discrete and continuous action reinforcement learning approach. This unified learning framework offers enhanced generalization and learning speed for dynamic aircraft adversarial processes. Experimental results indicate that in a symmetric environment, our approach rapidly achieves Nash equilibrium, securing over a 10% advantage. In unmanned aerial aircraft game control with higher maneuverability, the probability of gaining a situational advantage increases by more than 40%. Compared to similar methods, our approach demonstrates superior effectiveness in decision optimization and adversarial success ***, we validate the algorithm s robustness and adaptability in an asymmetric environment, showcasing its promising application potential in collaborative control of aircraft clusters.