Robust Quadrotor Position Tracking Using an LAHC-Optimized Dual-Loop Adaptive Fuzzy Feedback Linearization and Sliding Mode Control Framework

Abstract

This study addresses the challenging problem of quadrotor position control arising from actuator constraints, strong nonlinear couplings, modeling errors, payload variations, and environmental disturbances such as wind. To overcome these issues, a dual-loop control architecture is proposed in which the inner loop employs a Fuzzy Adaptive Feedback Linearization Controller (AFFLC) to cancel structural nonlinearities and estimate uncertainties online, while the outer loop utilizes a Sliding Mode Controller (SMC) for precise path and position tracking. Moreover, an asymmetric fuzzy adaptation mapping is introduced so that the adaptive gain increases nonlinearly with error magnitude but remains small near the origin, preventing over-amplification and residual chattering. In addition, Late Acceptance Hill Climbing (LAHC) optimization is applied to enhance convergence speed, reduce control signal oscillations, and improve adaptability under varying payloads and dynamic conditions. Overall results demonstrate that the proposed hybrid structure preserves theoretical stability through Lyapunov analysis and achieves more accurate three-dimensional tracking, higher robustness against uncertainties and disturbances, and smoother, low-cost control signals compared with heavy deep learning or meta-heuristic approaches; thus, it provides a practical and implementable solution for real quadrotor applications.