Genetic-Algorithm-Optimized Adaptive Backstepping MRAC for Robust Quadrotor Trajectory Tracking

Resumen

This paper presents an adaptive control strategy for quadrotor unmanned aerial vehicles (UAVs operating under parametric uncertainties and external disturbances. The proposed approach combines model reference adaptive control (MRAC) with a recursive backstepping design to ensure stable trajectory tracking for both translational and rotational dynamics. To reduce the sensitivity of the controller to manual gain tuning and to enhance tracking performance, a genetic algorithm (GA) is employed to optimize the key control and adaptation gains based on the integral of time-weighted absolute error (ITAE) criterion. The control design is developed using Lyapunov stability theory, guaranteeing the asymptotic convergence of tracking errors despite unknown system parameters. The proposed controller is evaluated on a nonlinear quadrotor model considering coupled attitude and altitude dynamics. Simulation results demonstrate accurate reference tracking, bounded control inputs, and improved robustness compared to non-optimized adaptive backstepping schemes. The obtained results indicate that the GA-optimized adaptive MRAC–backstepping framework provides an effective and systematic solution for quadrotor control in uncertain operating conditions.