Explicit Uncertainty Quantification for Probabilistic Assessment of Rotorcraft Handling Qualities

Abstract

Early-stage vehicle design processes are typically subject to significant uncertainty in aerodynamic loads and control responses. In many cases, it may be necessary to consider this uncertainty in preliminary evaluations of stability, handling qualities, and performance metrics. This article introduces a novel methodology for computing these quantities in a probabilistic framework using the Koopman operator. The algorithm discretizes the uncertainty space and uses relevant transformations or simulations to map each point to the handling qualities evaluation domain, resulting in a stability, handling quality, and/or performance assessment for each discretized point. The expected value of the stability, handling quality, and/or performance requirement is then computed via quadrature integration, yielding a probabilistic assessment with respect to relevant specifications. The methodology is demonstrated through two examples involving a quadrotor unmanned aerial system and a generic utility helicopter. In the first example, the methodology is applied to evaluate the handling qualities of a quadrotor, for which a multiloop dynamic inversion controller is developed. In the second example, the method is used to quantify the impact of parametric uncertainty in a pilot model on specific pilot–vehicle system performance specifications and to predict vehicle handling qualities. The proposed explicit uncertainty quantification approach is shown to provide a convenient framework for the propagation of parametric uncertainty to stability, handling quality, and performance specifications with unique advantages over Monte Carlo techniques.