Abstract
This paper presents a comparative framework for robust trajectory tracking of a Skywalker X8 flying-wing unmanned aerial vehicle (UAV) using optimized smooth sliding mode control (SSMC), cascaded proportional–integral–derivative (PID) control, and model predictive control (MPC). The controllers are evaluated on a nonlinear six-degree-of-freedom UAV model incorporating a high-fidelity aerodynamic representation derived from wind-tunnel measurements and flight-test-informed refinements. The SSMC employs a cascaded outer–inner-loop architecture with smooth nonlinear switching functions, while PID and SSMC parameters are optimized using derivative-free pattern search, and the constrained linear MPC benchmark is tuned using Bayesian optimization. Performance is assessed under nominal, stochastic-wind, and sensor-noise conditions at multiple climb rates, with additional Monte Carlo and empirical disturbance-scaling analyses. MPC provides the highest tracking precision and the lowest actuator effort under nominal and wind-disturbed conditions, but its tracking error and control effort increase most strongly under sensor noise. SSMC consistently improves upon optimized PID, particularly in tracking quality, transient behavior, and saturation, and yields the lowest tracking error and control effort under sensor noise. Empirical disturbance-scaling tests further show that PID and SSMC tolerate substantially larger wind amplification than MPC within the simulated horizon. Overall, SSMC provides a favorable compromise between tracking performance, actuator demand, and disturbance robustness.