A New 3D Sliding Pursuit Guidance Law for Fixed Wing Combat Drone Piloting: Application to El-Djazaïr 54

Abstract

This paper introduces a new finite-time neural adaptive nonlinear 3D sliding pursuit guidance law designed for autonomous control of fixed-wing Unmanned Aerial Vehicles (UAVs) targeting a maneuvering object. The central innovation in the control strategy is the incorporation of sliding control in pure pursuit, which significantly enhances robustness against uncertainties and variations. Simulations were conducted using a specific combat drone model (El-Djazaïr 54), within a real-time virtual Simulation Platform for Aircraft Control System (SP-ACS). The control approach is model-based, with an initial identification phase before testing and validation. To identify unknown, variable, and classified aerodynamic parameters, the Total Least Squares Estimation (TLSE) method was employed. The mean values of aerodynamic coefficients were calculated, with any deviations treated as modeling uncertainties to be managed by the robust control law. Simulation results demonstrate that the El-Djazaïr 54 drone exhibits excellent performance in tracking the moving target and maintaining robustness despite modeling uncertainties