This study advances flight control systems by integrating deep reinforcement learning to enhance fault tolerance in fixed-wing aircraft. We assess the efficiency of Cross-Entropy Method Reinforcement Learning (CEM-RL) and Proximal Policy Optimization (PPO) algorithms in developing an adaptive stable attitude controller. Our proposed frameworks, focusing on smooth actuator control, showcase improved robustness across standard and fault-induced scenarios. The algorithms demonstrate unique traits in terms of trade-offs between trajectory tracking and control smoothness. Our approach that results in state-of-the-art performance with respect to benchmarks, presents a leap forward in autonomous aviation safety.
Towards Intelligent Fault-Tolerant Attitude Control of Fixed-Wing Aircraft
Lect. Notes Electrical Eng.
International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024
2025-03-05
10 pages
Article/Chapter (Book)
Electronic Resource
English
COURSE FAULT-TOLERANT CONTROL FOR FIXED-WING UAV
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