Research on soft sensing of aircraft nose landing gear shimmy damp based on neural network

The mechanism of the nose wheel shimmy is complicated and influenced by many factors, and it shows nonlinear characteristics. The damping characteristics of the nose wheel steering system of a certain aircraft are measured by the damping test. The relationships between the damping characteristics of the nose wheel steering system and the damping aperture diameter, the excitation frequency and the excitation amplitude of the system are analyzed. Rely on the powerful nonlinear mapping ability and generalization function of BP neural network, built up the soft sensing model of BP neural network structure, whose inputs are shimmy damping aperture diameter, excitation frequency and excitation amplitude and output is shimmy damping values. Learned and predicted the model use the neural network. The prediction results proved the feasibility and practicability of this method.