Genetic Algorithm-Guided, Adaptive Model Order Reduction of Flexible Aircrafts

Abstract

This paper presents a methodology for automated model order reduction (MOR) of flexible aircrafts to construct linear parameter-varying (LPV) reduced order models (ROM)for aeroservoelasticity (ASE) analysis and control synthesis in broad flight parameter space. The novelty includes utilization of genetic algorithms (GAs) to automatically determine the states for reduction while minimizing the trial-and-error process and heuristics requirement to perform MOR; balanced truncation for unstable systems to achieve locally optimal realization of the full model; congruence transformation for "weak" fulfillment of state consistency across the entire flight parameter space; and ROM interpolation based on adaptive grid refinement to generate a globally functional LPV ASE ROM. The methodology is applied to the X-56A MUTT model currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that X-56A ROM with less than one-seventh the number of states relative to the original model is able to accurately predict system response among all input-output channels for pitch, roll, and ASE control at various flight conditions. The GA-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The adaptive refinement allows selective addition of the grid points in the parameter space where flight dynamics varies dramatically to enhance interpolation accuracy without over-burdening controller synthesis and onboard memory efforts downstream. The present MOR framework can be used by control engineers for robust ASE controller synthesis and novel vehicle design. I. Introduction ith the fast paced technological advances in this new era of science, modern aerospace designs are able to incorporate new flexible structures and lighter materials to achieve better maneuverability, endurance, and performance. As a result they are also more susceptible to issues such as complex dynamics and interactions between the controller and the aerodynamic and structural systems, which may lead to catastrophic events such as flutter, limit cycle oscillation, and gust loading. In order to design a modern flexible aircraft that can attain a safe and acceptable flight envelope, detailed modeling and high fidelity simulations of aeroservoelastic (ASE) systems must be performed prior to flight tests to be able to prevent aeroelastic (AE) failures. While full-order models coupling the nonlinear aerodynamics with structural models are capable of accurate prediction of underlying AE phenomena and onset, their prohibitive computational cost, low speed, nonlinear nature, as well as difficulty to deploy controllers with high-state-order models render it impractical for integration in the design environment involving concurrent ASE analysis and control synthesis and design. NomenclatureTo combat these challenges various model order reduction (MOR) techniques have been developed in the context of linear parameter varying (LPV) formulation. In LPV, the fully coupled nonlinear aircraft m...