System identification for high‐performance UAV control in wind

Abstract

SummaryThis article describes and experimentally evaluates a comprehensive system identification framework for high‐performance UAV control in wind. The framework incorporates both linear offline and nonlinear online methods to estimate model parameters in support of a nonlinear model‐based control implementation. Inertial parameters of the UAV are estimated using a frequency‐domain linear system identification program by incorporating control data obtained from motor‐speed sensing along with state estimates from an automated frequency sweep maneuver. The drag‐force coefficients and external wind are estimated recursively in flight with a square‐root unscented Kalman filter. A custom flight controller is developed to handle the computational demand of the online estimation and control. Flight experiments illustrate the nonlinear controller's tracking performance and enhanced gust rejection capability.