AI in Hypersonic Flight Control (Adaptive RL for Stability & Safety)

To provide a safety-first, non-weaponized framework for using AI and adaptive RL in hypersonic flight control, focusing on governance, assurance, and human oversight rather than controller design.

• Frame AI/adaptive RL for hypersonic flight within a safety-first, non-weaponized context.

• Build and document hazards, oversight roles, abort criteria, and geofencing for high-speed test articles.

• Specify perception and state-awareness requirements without exposing sensitive control algorithms.

• Draft a V&V plan, test envelopes, and safety monitors for AI-enabled high-speed systems.

• Outline a safety case and operator SOPs aligned with governance and certification thinking.

• Engineers & researchers in aerospace, mechanical, electrical, controls, or related fields

• AI/ML & RL practitioners interested in safety-critical aerospace applications

• Flight test, safety, governance, and certification professionals

• Senior students, faculty, and industry R&D teams working on hypersonic or high-speed systems

• Apply a safety-first, non-weaponized framework for AI/RL in hypersonic flight.

• Create core assurance artifacts: requirements matrix, hazard log, V&V plan, and safety case outline.

• Define safe envelopes, stay-out zones, geofencing, and abort criteria for high-speed tests.

• Design human-on-the-loop oversight with clear alerts, intervention thresholds, and shutdown workflows.

• Navigate governance and certification thinking for AI in sensitive aerospace systems.