Realising data-centric UAV autonomy through learning-based prediction and feedback integration

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>

Author dc:creator

• Shuyan Dong (21052235)

• Hybrid data-driven control
• Closed-loop control
• Data-driven UAV model
• Tiime series analysis
• Autonomy control
• PID control
• Motion prediction

Statement dc:rights

• CC BY
• Open Access after 2027-09-23

Identifier

10779/exe.31832809.v1

OAI identifier oai:identifier

oai:figshare.com:article/31832809