UAV Disturbance Estimation Based on RBF Neural Network Optimized by Two-Stage Particle Swarm Optimization

Radial Basis Function Neural Networks (RBFNNs) are frequently used in UAV disturbance estimation. However, the number of center points and the width values of the activation function significantly affect estimation accuracy, and selecting appropriate values empirically is challenging. Therefore, evolutionary optimization algorithms such as Particle Swarm Optimization (PSO) are often employed to determine the optimal parameters. Nevertheless, these evolutionary algorithms may suffer from premature convergence or entrapment in local optima, causing the fitness function value to stagnate. To address this issue, this study proposes a K-Means initialization-based two-stage particle swarm optimization algorithm (KTS-PSO-RBFNN) for RBFNN parameter optimization. The proposed method initializes the basis function centers and widths via K-Means clustering and optimizes them in two stages: first, jointly optimizing the centers and widths to locate the global optimal region; then, fine-tuning the widths while fixing the centers to eliminate parameter coupling interference. To comprehensively evaluate the proposed method, synthetic data comparative experiments, Dryden wind disturbance experiments, cross-validation experiments, and real-world dataset experiments were conducted. The results demonstrate that the KTS-PSO-RBFNN consistently outperforms traditional baseline algorithms across all test scenarios. The proposed method achieves lower fitness function values, demonstrating superior prediction accuracy in UAV disturbance estimation.