Enhanced Particle Swarm Sparrow Search Optimization for Aircraft Aerodynamic Parameter Identification

Accurate aerodynamic parameter identification is fundamental to achieving high-performance model‍-based aircraft control. To address the inherent challenges in this domain, this study introduces a novel hybrid optimization algorithm, the improved chaos particle swarm sparrow search algorithm (ICPSSSA), which synergistically combines the global search capabilities of the sparrow search algorithm (SSA) with the fine-tuning efficiency of particle swarm optimization (PSO). ICPSSSA enhances the optimization performance through three key innovations: the Kent chaotic map, which increases population diversity; an adaptive dynamic search mechanism, which balances exploration and exploitation, and the Lévy flight perturbation, which expands the global search radius. These components work in concert to accelerate convergence while avoiding local optima. By framing aerodynamic parameter identification as a nonlinear optimization problem, ICPSSSA was applied to both simulation scenarios and real flight data. The comparative results demonstrate that ICPSSSA consistently achieves superior accuracy, robustness, and convergence efficiency over conventional approaches, underscoring its promise as a powerful tool for high-fidelity aircraft modeling and control.