Intelligent Flight Tracking for Uncrewed Aerial Vehicles with Blade Element Momentum Aerodynamic Modeling

Quadrotors attract broad military and civilian interest, yet their underactuated, highly coupled, and nonlinear dynamics make precise control difficult. While Sliding Mode Control (SMC) offers robust trajectory tracking, tuning its parameters—such as sliding surfaces and switching gains—remains a tedious manual task. To overcome this, we present an adaptive SMC framework integrated with an Actor-Critic Deep Reinforcement Learning (DRL) algorithm for automated gain synthesis. A full 6-DoF quadrotor model is derived via Euler-Lagrange mechanics, with aerodynamic coefficients estimated using Blade Element Momentum theory. The DRL agent dynamically adjusts SMC gains online, reducing chattering while ensuring accurate altitude and trajectory tracking. Lyapunov's direct method rigorously establishes asymptotic stability of the closed-loop system. Comparative simulations show that the proposed DRL-SMC approach consistently outperforms conventional fixed-gain baselines, achieving lower Root Mean Squared Error across complex flight profiles.