Maximization of Rotary Wing Aircraft Landing Envelope Using Model Predictive Control
Abstract
Develop automated control algorithms for helicopter ship deck landing to maximize the landing envelope. Use complex control-oriented helicopter models in conjunction with ship motion data and simulation and airwake models. Employ model predictive control as a control strategy. Perform optimization and validation studies for landing maneuvers in nonquiescent conditions. The overall objective of this fundamental research project is to develop automated control algorithms for helicopter ship deck landing to achieve maximization of the landing envelope. The project will provide answers to key questions such as to what extent ship quiescence prediction can be eliminated in helicopter ship landing? Complex control oriented helicopter models will be used in conjunction with ship motion data and simulation and airwake models. The control strategy will be model predictive control, selected due to its success in handling highly constrained control problems. Optimization research will be performed for the maximization of the landing envelope. Validation studies for landing maneuvers in nonquiescent conditions and for resulting landing envelopes will be performed. This project addresses the requirement to improve current capabilities and lead to new fundamental understanding and knowledge regarding a rotary wing aircraft automated control system that can precisely perch the aircraft on a moving deck. The research will enable autonomous precision landing of helicopters on ship decks by maximally utilizing the flight envelope. The research will advance fundamental understanding in the capabilities of automated control in helicopter landing on ship decks in particular and in aerospace vehicle landing on moving platforms in general.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 23, 2016
- Source ID
- N000141612736
Entities
People
- Cornel Sultan
Organizations
- Office of Naval Research
- United States Navy
- Virginia Tech