[EN-A-028] Aircraft Safety Analysis using Generalized Polynomial Chaos
نویسندگان
چکیده
In this paper we investigate the application of generalized polynomial chaos (gPC) for optimal control based aircraft safety assessment with parameter uncertainties. The approach is based on the formulation of an appropriate optimal control problem to obtain worst case inputs. The criterion to be assessed is introduced in the cost function and the numerical solution is obtained using direct optimal control methods. In this context, we consider the case where the parameter distribution is unknown and thus assume a truncated uniform distribution with truncation values to be determined. The approach can be summarized as follows: First an optimization assisted bisection search algorithm is performed. This algorithm yields regions of a user-defined maximum size in which a violation of the criterion occurs. In order to obtain a local explicit representation of the worst case solution, we approximate this solution in the parameter space using a spectral representation based on gPC. This representation is then used to determine the worst case truncation limits of the uniform distribution and to estimate the exceedance probability for the criterion under investigation. The application is illustrated using an F-16 short period model with a model reference adaptive controller. For this example, we estimate the exceedance probability of the maximum tracking error in the angle of attack for worst case reference command inputs and plant uncertainties in pitch damping, pitch stiffness, and control effectiveness.
منابع مشابه
Adaptive Numerical Solutions of Stochastic Differential Equations
In this paper we present an adaptive multi-element generalized polynomial chaos (ME-gPC) method, which can achieve hp-convergence in random space. ME-gPC is based on the decomposition of random space and generalized polynomial chaos (gPC). Using proper numerical schemes to maintain the local orthogonality on-the-fly, we perform gPC locally and adaptively. The key idea is to combine the polynomi...
متن کاملModeling Multibody Dynamic Systems With Uncertainties. Part II: Numerical Applications
This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects”. In this paper we illustr...
متن کاملLong-term behavior of polynomial chaos in stochastic flow simulations
In this paper we focus on the long-term behavior of generalized polynomial chaos (gPC) and multi-element generalized polynomial chaos (ME-gPC) for partial differential equations with stochastic coefficients. First, we consider the one-dimensional advection equation with a uniform random transport velocity and derive error estimates for gPC and ME-gPC discretizations. Subsequently, we extend the...
متن کاملEvaluation of Non-Intrusive Approaches for Wiener-Askey Generalized Polynomial Chaos
Polynomial chaos expansions (PCE) are an attractive technique for uncertainty quantification (UQ) due to their strong mathematical basis and ability to produce functional representations of stochastic variability. When tailoring the orthogonal polynomial bases to match the forms of the input uncertainties in a Wiener-Askey scheme, excellent convergence properties can be achieved for general pro...
متن کاملPolynomial-Chaos-Based Bayesian Approach for State and Parameter Estimations
Two new recursive approaches have been developed to provide accurate estimates for posterior moments of both parameters and system states while making use of the generalized polynomial-chaos framework for uncertainty propagation. The main idea of the generalized polynomial-chaos method is to expand random state and input parameter variables involved in a stochastic differential/difference equat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017