Handling Quality Analysis and Control Design using Polynomial Chaos Model-based Uncertainty Methods

This article demonstrates application of a probabilistic analysis facility called AURA to rotary-wing control and handling-qualities problems. It is based on a Generalized Polynomial Chaos approach to compute and reason over probabilistic quantities. The AURA software has interfaces to both SIMULINK and MATLAB and bindings to other programming languages. It can model arbitrary random distributions and has the potential to fully represent the effects of the uncertainty with a single simulation run. Modeling and analyzing rotorcraft flight dynamics, handling-qualities, performance, structural loads, and design requires sophisticated tools to capture the complex physical phenomena germane to their operation. Present and future rotorcraft operational requirements demand the vehicles perform in new and increasingly austere environments. In the design and maintenance of such complex systems, there are associated uncertainties from either internal processes or environmental factors that affect the performance and stability of the system. Traditionally, uncertainties are treated as Gaussian random variables and their impact assessed using Monte Carlo techniques. This approach has proven successful but requires numerous simulation runs and approximations regarding the true distribution of the uncertainty, and there is no guarantee that Monte Carlo techniques provide comprehensive coverage of the uncertainty space. AURA is designed to help bridge these gaps in an environment that treats arbitrary random sources in an intuitive manner. This article briefly outlines the approach and demonstrates applications to rotorcraft control and handling-qualities analysis.