Model Reference Adaptive Control For Gust Load Allevation of Nonlinear Aeroelastic

TL;DR

This paper develops a Model Reference Adaptive Control framework based on Lyapunov stability theory for gust load alleviation in nonlinear aeroelastic systems, demonstrating effectiveness in reducing wing-tip deflections.

Contribution

It introduces a novel MRAC approach using a reduced-order nonlinear model, with real-time gain adaptation and proven stability guarantees for aeroelastic gust load control.

Findings

MRAC outperforms H infinity control in reducing wing-tip deflections for UAVs.

The adaptation rate influences convergence speed and load reduction.

Effective in both deterministic gusts and stochastic turbulence environments.

Abstract

Model Reference Adaptive Control based on Lyapunov stability theory is developed for gust load alleviation of nonlinear aeroelastic systems. The controller operates on a nonlinear reduced-order model derived from Taylor series expansion and eigenvector projection of the coupled fluid-structure-flight dynamic equations. The complete MRAC formulation is presented, including the reference model design that encodes desired closed-loop damping characteristics, the adaptive control law with real-time gain adjustment, and the Lyapunov derivation of the adaptation law that guarantees asymptotic tracking in the linear case and bounded tracking under a Lipschitz condition on the nonlinear residual. The adaptation rate matrix is identified as the single most important design parameter, governing the trade-off between convergence speed, peak load reduction, and actuator demand. Two test cases are considered, a 3DOF aerofoil with cubic stiffness nonlinearities, and a Global Hawk type unmanned aerial vehicle. For the UAV under a discrete gusts, MRAC achieves significant wing-tip deflection reductions, outperforming the H infinity robust control benchmark with comparable control effort. Under Von Karman stochastic turbulence, meaningful reductions are also obtained, with performance scaling with the adaptation rate. The results demonstrate that MRAC provides an effective framework for GLA of flexible aircraft operating in both deterministic and stochastic disturbance environments.