Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation

Abstract From arsenal delivery to rescue missions, unmanned aerial vehicles (UAVs) are playing a crucial role in various fields, which brings the need for continuous evolution of system identification techniques develop sophisticated mathematical models effective flight control. In this paper, novel parameter estimation technique based on filter error method (FEM) augmented with particle swarm optimisation (PSO) is developed and implemented estimate longitudinal lateral-directional aerodynamic, stability control derivatives fixed-wing UAVs. The FEM used steady-state extended Kalman filter, where maximum likelihood cost function minimised separately using randomised solution search algorithm, PSO proposed termed FEM-PSO. A sufficient number compatible data sets were generated two cropped delta wing UAVs, namely CDFP CDRW, analyse applicability method. comparison has been made between estimates obtained computationally intensive conventional FEM. It observed that most FEM-PSO consistent wind tunnel estimates. also noticed aerodynamic ${C_{{L_\alpha }}},\;{C_{{m_\alpha }}},\;{C_{{Y_\beta }}},\;{C_{{l_\beta }}}$ ${C_{{n_\beta having relative offsets 2.5%, 1.5%, 6.5%, 3.4% 7.6% w.r.t. values CDFP, 1.4%, 1.9%, 0.1%, 9.6% 7.5% CDRW. Despite slightly higher Cramer-Rao Lower Bounds estimated method, simulated responses have less than 0.10% measured data. proof-of-match exercise conducted ascertain efficacy degree effectiveness comparable