E nhanced UAV Fault Diagnosis and Compensation via Nonlinear Disturbance Observer and Adaptive Control

Abstract

This study highlights the pivotal role of a nonlinear disturbance observer (NDO) in enhancing fault-tolerant control for fixed-wing unmanned aerial vehicles (UAVs) operating under adverse conditions such as icing. The proposed control framework integrates the NDO with nonlinear dynamic inversion and radial basis function neural networks to address complex nonlinearities introduced by simultaneous actuator and sensor faults, environmental disturbances, and model uncertainties. Central to this approach, the NDO enables accurate detection and estimation of sensor faults, significantly improving overall system robustness and reliability. To further optimize performance, the observer is designed to minimize deviations of the closed-loop eigenvalues from their desired locations and to achieve near-unity steady-state gain.