Robust Quadrotor Position Tracking via Fuzzy Adaptive with Outer-Loop Sliding Mode Control

The present research introduces an integrated and computationally efficient control and optimization framework aimed at enhancing the robustness, adaptability, and real-time efficiency of unmanned aerial vehicle (UAV) operations in complex, uncertain, and dynamically changing environments. The proposed hybrid system combines an Adaptive Fuzzy Feedback Linearization Controller (AFFLC) with an outer-loop Sliding-Mode Controller (SMC) and a Late Acceptance Hill-Climbing (LAHC) path-planning optimizer, building a two-layer intelligent structure capable of handling both nonlinear flight dynamics and global trajectory optimization. The innovation of the research lies in the formulation of a fuzzy adaptive law that modulates internal control gains according to instantaneous tracking error and its derivative, ensuring smooth and rapid responses while effectively mitigating chattering without relying on disturbance observers or heavy parameter tuning. The importance of this study stems from the growing demand for lightweight, low-complexity algorithms that can sustain stable performance under severe operational conditions—such as payload uncertainty, wind disturbance, and limited onboard computational power—where conventional control and meta-heuristic methods often fail. The objectives include designing a dual-loop AFFLC–SMC system for robust stabilization, deriving Lyapunov-based guarantees of global boundedness and convergence, and integrating a LAHC-based global route optimizer minimizing the combined cost of distance, energy consumption, and threat exposure. Quantitative and qualitative analyses confirm that the proposed approach achieves the smallest tracking error, negligible steady-state deviation, and fastest dynamic recovery. Specifically, the AFFLC suppresses rotor chattering by over 70% and reduces total convergence time by ≈ 35% compared with conventional adaptive and sliding-mode controllers, while the LAHC algorithm yields the lowest path cost and execution time across all benchmark scenarios, stabilizing within ≈ 100 iterations—far faster than GWO, SOS, SA, and HGWO-MSOS optimizers. The associated tables and figures demonstrate that the adaptive-gain surface produces nonlinear stabilization behavior, effectively balancing transient acceleration and steady-state smoothness, while the 3D simulation results highlight the planner’s capability in maintaining safe, energy-efficient navigation even in densely constrained, multi-obstacle environments. Collectively, the integrated AFFLC–SMC + LAHC framework offers a lightweight, high-precision, and computationally scalable solution readily applicable to real-time UAV flight missions, emphasizing a practical step toward intelligent autonomous aerial systems for industrial, security, and environmental applications.