Towards Intelligent Fault-Tolerant Attitude Flight Control of Fixed-Wing Aircraft

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.

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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. https://doi.org/10.1007/978-981-96-2264-1_15