# Feedback Linearization

The work proposes a UAV control scheme that combines a Recursive Gaussian Process (RGP) with a feedback linearization controller, using a purpose-designed Kalman-filter-based tuning algorithm to compensate online for the nonlinear dynamics left uncanceled by feedback linearization, implements the entire RGP pipeline directly onboard a real quadrotor (the ANT-X Lab drone), and validates it for single-axis horizontal position control in indoor experiments: across repetitive and non-repetitive sinusoidal references, the geometric mean root mean square error improves by 38% to 75% relative to the same controller without RGP, with geometric mean tracking errors between 4.5 cm (constant-amplitude sinusoid at omega=1 rad/s) and 9.85 cm (sweep sinusoid).

The work proposes a UAV control scheme that combines a Recursive Gaussian Process (RGP) with a feedback linearization controller, using a purpose-designed Kalman-filter-based tuning algorithm to compensate online for the nonlinear dynamics left uncanceled by feedback linearization, implements the entire RGP pipeline directly onboard a real quadrotor (the ANT-X Lab drone), and validates it for single-axis horizontal position control in indoor experiments: across repetitive and non-repetitive sinusoidal references, the geometric mean root mean square error improves by 38% to 75% relative to the same controller without RGP, with geometric mean tracking errors between 4.5 cm (constant-amplitude sinusoid at omega=1 rad/s) and 9.85 cm (sweep sinusoid).