An IQC analysis framework for small fixed-wing UAS

Abstract

A robust analysis framework for mathematical validation of flight control systems is presented. The framework was specifically developed for the complete uncertainty characterization, quantification, and analysis of small fixed-wing unmanned aircraft systems (UAS). The analytical approach presented within is based on integral quadratic constraint (IQC) analysis methods and uses linear fractional transformations (LFTs) on uncertainties to represent system models. The IQC approach can handle a wide range of uncertainties, including static and dynamic, linear time-invariant and linear time-varying perturbations. While IQC-based uncertainty analysis has a sound theoretical foundation, it has thus far mostly been applied to academic examples, and there are major challenges when it comes to applying this approach to complex engineering systems, such as UAS. The difficulty mainly lies in appropriately characterizing and quantifying the uncertainties such that the resulting uncertain model is representative of the physical system without being overly conservative, and the associated computational problem is tractable. This paper addresses these challenges by applying IQC-based analysis tools to analyze the robustness of a particular UAS flight control system. Specifically, uncertainties are characterized and quantified based on mathematical models and system identification flight test data obtained in house for a small commercial off-the-shelf platform with a custom autopilot. Analysis is performed on three uncertainty groups, aimed at providing valuable information for use in controller synthesis. The proposed framework is also transferable to other fixed-wing UAS platforms, effectively taking IQC-based analysis beyond academic examples to practical application in UAS control design and airworthiness certification.