Research on Offshore Wind Turbine Blade Repair Based on Particle Swarm Optimization‐Backpropagation Neural Network and Improved Active Disturbance Rejection Control

Abstract

Offshore wind turbine blade repair requires stable material removal and precise force regulation under curved‐surface contact and environmental disturbance. To address these challenges, this paper proposes an integrated constant‐force grinding method that combines a passive compliant end‐effector, a particle swarm optimization‐backpropagation neural network (PSO‐BP), and an improved active disturbance rejection control (ADRC) strategy. First, a passive compliant end‐effector with variable stiffness is designed to improve contact adaptability and reduce grinding impact on curved blade surfaces. Second, a PSO‐BP model is established to predict the material removal rate (MRR) and surface roughness (Ra) under different grinding conditions, thereby providing data‐driven support for process‐state evaluation and parameter scheduling. Third, based on a controller‐oriented force‐dynamics model, an improved ADRC framework integrating a tracking differentiator, nonlinear extended state observer, nonlinear state error feedback, and PSO‐BP‐assisted gain scheduling is developed for constant‐force grinding. A Lyapunov‐based analysis shows that the closed‐loop system is uniformly ultimately bounded under bounded disturbances and bounded scheduling error. Simulation and experimental results demonstrate that, compared with PID and standard ADRC, the proposed method achieves higher force‐tracking accuracy, stronger disturbance rejection, and better grinding quality. Under equivalent initial damage conditions, it produces the lowest post‐grinding surface roughness, indicating that the proposed method provides an effective solution for offshore blade grinding repair.