System identification of a general aviation aircraft using a personal electronic device
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System Identification (SysID) is the process of obtaining a model of system dynamics by analyzing measurement data. SysID is often used in flight testing to obtain or refine estimates for aircraft stability and control derivatives and performance. Recent applications have shown that SysID can also be used to monitor and update models of dynamics and performance during routine operations. General Aviation (GA) continues to see higher accident rates than other aviation sectors. To combat this, research into accident mitigation strategies, especially loss of control (LOC) accidents, has led to the development of energy-based or envelope-based safety metrics that can be used to monitor and improve the safety and efficiency of GA operations. However, these methods depend on the existence of an accurate aircraft model to predict the performance and dynamics of the aircraft. The diversity of the aging GA fleet has established the need to calibrate existing models using flight data. SysID therefore has the potential to improve these methods by monitoring and updating aircraft models for each individual GA aircraft. Any SysID process depends on the type and quality of measurement data available as well as the nature of the aircraft model (what parameters are being identified) and the method of SysID being used. As opposed to flight test SysID, availability of flight data can be limited in GA. However, flight data recording using Personal Electronic Devices (PEDs) or low-cost Flight Data Recorders (FDRs) is becoming common. The capabilities of SysID methods using data from these devices has yet to be explored. This work demonstrates a process for evaluating SysID techniques for GA aircraft using data from a PED. A simulator environment was created that allowed testing of a variety of SysID and estimation methods. An observability condition was developed and used to inform decisions regarding model parameters and necessary assumptions. The results of this process provide a proof for existence and uniqueness of a solution to the minimization problem that SysID aims to solve. Local observability and global identifiability were also used to divide the “blind” SysID process into two estimations: an online estimation of aircraft states and unknown controls, and an offline identification of model parameters. Two SysID methods were then compared: Output Error Method (OEM), and Filter Method using an Extended Kalman Filter (EKF). It was shown that OEM outperformed EKF at the expense of increased computational burden. Potential improvements to both OEM and EKF SysID in this context are discussed. However, using OEM resulted in improved estimates of performance and dynamics over an assumed a priori model. These improvements were robust to both sensor quality and assumptions in the model, therefore demonstrating the potential of SysID using PED data to improve GA safety and efficiency.
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