Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

Abstract

For characteristics of nonlinearity and time-varying volatility of landing gear based on MR damper, a BP neural network PID controller with a momentum was designed on basis of established dynamic mathematical model. BP neural network would adjust three parameters of PID online in time. The controller was inputted the energy which was combined by the feedback of acceleration and displacement of the control system, which greatly reduced the computation time of controller and the control effect was more obvious. After compared with PID, the simulation and experiment have showed that BP neural network PID has a better effect. The arithmetic can be put into practice through experimental testing.

Intelligent Control Based on a Neural Network for Aircraft Landing Gear with a Magnetorheological Damper in Different Landing Scenarios

“…However, applications for landing gear are relatively rare. Shixing Zhu et al [17] applied a back-propagation neural network to adjust three parameters of the PID controller for landing gear. Geoffrey Holmes et al [18] used machine learning algorithms to predict the landing gear loads during touchdown based on drop test experimental data.…”