Model Reference Adaptive Control For Gust Load Allevation of Nonlinear Aeroelastic
This addresses gust load alleviation for flexible aircraft, offering an effective framework for both deterministic and stochastic disturbances, though it appears incremental as an adaptation of existing MRAC methods to this domain.
The paper tackled gust load alleviation for nonlinear aeroelastic systems by developing a Model Reference Adaptive Control based on Lyapunov stability theory, achieving significant wing-tip deflection reductions that outperformed an H infinity robust control benchmark with comparable control effort.
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.