Model Predictive Control for Autonomous Shipboard Landing
Abstract
This project seeks to develop a model predictive control (MPC) framework for designing algorithms that can guarantee fast, safe, and precise landing of helicopters onto moving ship decks. The effort will use a model predictive control approach to develop control algorithms for autonomous flight that can guarantee performance and safety constraints, while being robust to uncertainty in input disturbances (e.g., ship wake interactions) and model parameters. The proposed work will seek to establish feasibility of using MPC algorithms for safe and time optimal landing maneuvers onto a moving platform from a computational and analytic standpoint. After designing cost functions and constraints that capture impact avoidance during landing while minimizing the time of descent, the control approach will be demonstrated on the Black Hawk GenHel model using the SCONE database for ship motion. The effort will also compare the performance of linear and non-linear models and will assess the impact of sensor noise and accuracy on the performance bounds of the control algorithms.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N000141612705
Entities
People
- Sandipan Mishra
Organizations
- Office of Naval Research
- Rensselaer Polytechnic Institute
- United States Navy