Compensation control of belt polishing force for aero-engine blade based on disturbance observer

To improve the dimensional accuracy and surface quality of blade, a Back Propagation (BP) neural network Proportional Integral Derivative (PID) control method based on disturbance observer was proposed aiming at the influence of gas compressibility, valve dead-time effect, valve nonlinear flow, cylinder friction, measurement noise and other interference factors on the control precision of blade polishing force. This method predicted nonlinear interference in the polishing force pneumatic control system by constructing a disturbance observer, and the equivalent compensation was introduced to suppress the interference. Simultaneously, BP neural network control algorithm was used to adjust PID control parameters online adaptively. Simulation analysis and experimental results showed that BP neural network PID controller based on disturbance observer had high control precision, strong robustness, strong interference suppression ability and other advantages compared with the traditional PID controller, and it could improve the blade profile dimensional precision and surface consistence, reduce the surface roughness, decrease the residual stress and improve the polishing efficiency.