Trajectory Tracking of Fixed-Wing UAV Using ANFIS-Based Sliding Mode Controller

This paper proposes a robust Adaptive Neuro-Fuzzy Inference System-based Sliding Mode Controller (ANFIS-SMC) for trajectory tracking in fixed-wing unmanned aerial vehicles (FWUAVs). FWUAVs are autonomous and versatile, finding applications in many fields such as defense, surveillance, and logistics. However, their dynamic model is highly complex due to nonlinearity and coupling effects. In this paper the decoupling is held based on extracting dominant inputs, and all other inputs are considered uncertainties, for designing controllers. The proposed ANFIS-SMC integrates the adaptability of the ANFIS with the robustness of SMC to overcome issues such as the chattering effect and shortcomings of traditional fuzzy logic controllers. ANFIS is trained with the sliding surface as an input data and SMC’s discontinuous control effort as an output data. Through such training, ANFIS can effectively approximate the discontinuous control action without losing smoothness and adaptability of control while keeping the robustness of the traditional SMC strategy. The Lyapunov theory ensures the finite time convergence of both reaching and sliding phases of sliding mode controller. The proposed ANFIS-SMC controller is stronger and more adaptive compared to FSMC and SMC. During mass variation along the x-axis, ITAE increased from 10.94 to 63.52 using ANFIS-SMC, from 17.39 to 128.3 using FSMC, and from 38.28 to 830.1 using SMC. This is an improvement of 50% over FSMC and 90% over SMC, which demonstrates improved trajectory tracking performance for fixed-wing UAVs in dynamic environments. Simulations on MATLAB®/Simulink® confirm that the proposed ANFIS-SMC ensures stable flight and accurate trajectory tracking, even in challenging flight conditions.