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System identification for high-performance UAV control in wind
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DOI：10.1002/rnc.6935.png)
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Abstract
En 中文
This 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.
Keywords:
adaptation models
estimation
Kalman filter
parameter estimation
system identification
uncertain systems
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