Deep Reinforcement Learning Based Systems for Safety Critical Applications in Aerospace
This work targets aerospace engineers and researchers by proposing incremental advancements in applying existing DRL methods to enhance safety and efficiency in aerospace systems.
The paper addresses the integration of deep reinforcement learning (DRL) into aerospace control systems for safety-critical applications, highlighting potential improvements in real-time monitoring, fault detection, and autonomous or assistive control capabilities, though it does not provide specific numerical results.
Recent advancements in artificial intelligence (AI) applications within aerospace have demonstrated substantial growth, particularly in the context of control systems. As High Performance Computing (HPC) platforms continue to evolve, they are expected to replace current flight control or engine control computers, enabling increased computational capabilities. This shift will allow real-time AI applications, such as image processing and defect detection, to be seamlessly integrated into monitoring systems, providing real-time awareness and enhanced fault detection and accommodation. Furthermore, AI's potential in aerospace extends to control systems, where its application can range from full autonomy to enhancing human control through assistive features. AI, particularly deep reinforcement learning (DRL), can offer significant improvements in control systems, whether for autonomous operation or as an augmentative tool.