2026-09-18T23:58:14Z https://api.figshare.com/v2/oai

oai:figshare.com:article/31832809 2026-03-23T17:40:08Z portal_1132 item_type_8 month_year_03_2026

Realising data-centric UAV autonomy through learning-based prediction and feedback integration Shuyan Dong (21052235) Hybrid data-driven control Closed-loop control Data-driven UAV model Tiime series analysis Autonomy control PID control Motion prediction Unmanned aerial vehicles (UAVs) operate under non-linear, time-varying dynamics and distribution shift, where classical controllers depend on accurate models and extensive gain tuning. This dissertation develops an end-to-end pipeline — from raw flight logs to a deployable closed loop — that combines data-driven prediction, evolutionary optimisation, and classical feedback to deliver reliable and interpretable autonomy. A data-driven virtual UAV is learned from real flight data using a nonlinear autoregressive model with exogenous inputs (NARX). Evaluation is horizon-sensitive and employs MSE, RMSE, R squared, and dynamic time warping (DTW) to separate transient and steady-tate behaviour and expose error structure. Under identical dataset splits and preprocessing, a companion LSTM baseline is trained for comparison; the NARX surrogate achieves higher accuracy on representative lateral and vertical manoeuvres and exhibits regular error growth with horizon length. The surrogate is first embedded in a genetic algorithm (GA) sequence designer that explores short actuator sequences and trades a small amount of fidelity for smoothness and robustness; signal conditioning — including Hodrick-Prescott filtering — suppresses high-frequency irregularities while preserving temporal structure. While effective over short horizons, this open-loop synthesis drifts as the horizon grows and is sensitive to unmodelled disturbances. Motivated by these limitations, the core contribution, NeuroFlyPID-Fusion, places the NARX predictor inside a high-rate PID feedback loop. A multi-output reconstruction maps corrections to roll, pitch, yaw, and thrust, reducing inter-axis coupling. The fused design yields smoother actuation, sharper transients, and stabilised residuals under distribution shift. Behaviour awareness is introduced via lightweight segmentation (take-off, landing, forward/backward/sideways motion) with soft transitions and short hysteresis windows, keeping identification local to each primitive. Overall, the pipeline provides a practical route from data to flight-worthy autonomy with predictable behaviour and a low tuning burden.<p></p> 2026-03-17T00:00:00Z Text Thesis 10779/exe.31832809.v1 https://figshare.com/articles/thesis/Realising_data-centric_UAV_autonomy_through_learning-based_prediction_and_feedback_integration/31832809 CC BY Open Access after 2027-09-23