Aircraft Longitudinal Aerodynamic Parameter Identification of Kernel Extreme Learning Machine Based on Improved Northern Goshawk Algorithm

Accurately obtaining aircraft aerodynamic parameters is essential for improving flight performance, optimizing design and control strategies, and ensuring flight safety. In this study, the improved Northern Goshawk Optimization (SPNGO) algorithm is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). To address the defects of the original NGO algorithm, such as insufficient global optimization ability and being prone to falling into local optimums, two improvement strategies are proposed. The enhanced SPNGO algorithm is verified by 14 benchmark test functions, and the proposed SPNGO-KELM model is evaluated using open-source F-16 nonlinear simulation data for longitudinal aerodynamic parameter identification. The results demonstrate its effectiveness under the considered simulation conditions, while further validation with real flight-test data is required before application to actual flight environments. Comparative analysis with KELM, NGO-KELM, SSA-KELM, and WOA-KELM models shows that a single KELM is difficult to achieve high-precision aerodynamic parameter identification, and other comparison models have obvious fitting deviations in non-steady-state and strong nonlinear regions. Notably, the SPNGO-KELM model achieves the best identification performance, with a determination coefficient (R2) of 0.96537 and a mean absolute percentage error (MAPE) as low as 3.1574%. Its comprehensive identification accuracy is 1.81% to 37.98% higher than that of the comparison models, and it can effectively suppress error oscillations in nonlinear regions. Experimental results show that the proposed algorithm has excellent identification accuracy, generalization ability, and anti-interference performance.