Real-Time Adaptive Control for Quadrotor UAV Trajectory Tracking: Hardware-in-the-Loop Validation and Performance Evaluation

Abstract

Accurate trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) remains a very challenging problem because of their inherent nonlinear, strongly coupled and underactuated dynamics. In order to overcome these limitations, a real-time Model Reference Adaptive Control (MRAC) strategy is proposed in this paper for better tracking performance in the presence of parametric uncertainties and external disturbances. The controller is cascaded, and adaptive laws based on Lyapunov stability theory are used to control the translational and rotational motions separately and guarantee closed-loop stability. The proposed approach is benchmarked against a tuned Particle Swarm Optimisation (PSO) PID controller under the same operating conditions to evaluate its efficacy. The validation is performed via extensive numerical simulations and real-time Hardware-in-the-Loop (HIL) experiments on an OPAL-RT platform, confirming enhanced disturbance rejection and transient response in the studied deterministic HIL conditions. The results show that the MRAC controller converges faster and has higher tracking accuracy than the PSO-based PID controller. Settling times are reduced from 9–12 s to 5–7 s with negligible steady-state error in setpoint tracking tests. The tracking errors for the multi-axis trajectory-tracking experiments, including the square and three-dimensional trajectories, are kept within 0.1–0.3 m; larger tracking deviations are observed with the benchmark controller. The quantitative performance evaluation demonstrates approximately 60–70% reduction in RMSE together with lower MAE, IAE, and ITAE values compared with the optimised PSO-based PID controller. Also, disturbance experiments under 1 N external force demonstrate the improved disturbance-rejection performance of the adaptive controller with performance degradation of about 25–30% compared to 38–45% for the PSO-based PID controller. The overall results obtained under deterministic real-time HIL conditions indicate that the proposed MRAC strategy provides improved trajectory-tracking performance compared to the benchmark PSO-based PID controller. Further statistical validation and physical flight experiments will be considered in future works.