Project: System Identification for Unmanned Air Systems
Sponsor: National Science Foundation (NSF) Center for Autonomous Air Mobility & Sensing (CAAMS)
Purpose: System Identification is a process to develop a mathematical representation of the dynamics of a physical system from measured data. Accurate models enable prediction of performance and dynamics of a system.
Challenges: Models for sUAS are generally not available as manufacturers do not have models for commercial sUAS and models for military sUAS are not typically available. Modeling and control systems are often vehicle dependent and not easily portable across sUAS. Many commercial autopilots do not provide data needed for online system identification
Our Approach: Utilizing the Observer Kalman Filter Identification algorithm with the Developmental Flight Test Instrumentation 2 framework, full state space models can be identified in near-real time onboard the vehicle utilizing data from a variety of sensors.