Real-time aerodynamic load estimation and aircraft prognostic control using distributed flush air data system (FADS) sensor network

The current state-of-the-art technologies available at the disposal of the aerospace industry lacks the ability to measure the aerodynamic forces and moments acting on an aircraft in real-time during it\u27s flight. Since the entire flight of an aircraft is based on the balance and controlled manipulation of these forces and moments, the appropriate real-time estimation for these parameters is of utmost interest. The work presented herein addresses the issues associated with the real-time aerodynamic load estimation problem through the use of a distributed Flush Air Data System (FADS) sensor network and the development of appropriate estimation methods. This work showcases a method to design the sensor network to capture the critical aerodynamic information in the aircraft pressure signature. It also elaborates upon a neural-network based estimation method to extract the aerodynamic load information from the pressure information captured by the sensor network. This research also focuses on the use of the real-time aerodynamic load estimations on building new aircraft applications for aircraft safety and control. This work shows that the incipient stall conditions can be detected using the real-time aerodynamic load information. The idea and implementation of a prognostic control is also presented in this work. It is shown here that the prognostic control based on the real-time estimates of aerodynamic forces and moments can anticipate the change in aircraft states and therefore employ appropriate control action before a traditional controller