Towards Intelligent Fault-Tolerant Attitude Flight Control of Fixed-Wing Aircraft
Published in Lecture Notes in Electrical Engineering, Springer Nature, International Conference in Navigation Guidance and Control (ICGNC), 2024
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.
keywords: flight control, fault-tolerance, robustness, reinforcement learning, evolutionary strategies, stability, control smoothness
Recommended citation: Zongo, A.B., Qing, L. (2025). "Towards Intelligent Fault-Tolerant Attitude Control of Fixed-Wing Aircraft." In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2024. Lecture Notes in Electrical Engineering, vol 1353. Springer, Singapore.