Research on nonlinear helicopter model identification base on neural network
Aiming at the problem of helicopter flight mechanics modeling,a nonlinear identification method based on the BP neural network model was proposed.In the structure design of neural net-works,the topology of the hidden layer was optimized based on the analysis of the linear acceleration and angular acceleration mechanisms in helicopters.Furthermore,integrating the six degrees of free-dom Euler motion equations,a complete non-linear neural network model for helicopters was con-structed.In the aspect of neural network training method,a two-step training method based on L-M algorithm was established,and the open loop neural network and closed loop neural network were trained respectively.The neural network was trained and validated using a dataset of sweep excita-tion responses generated by the UH-60 helicopter's nonlinear model.Finally,the neural network model was trimmed and linearized at various forward flight speeds,and the trim quantity and aerody-namic derivative of the neural network model were obtained.The research results show that the es-tablished neural network model has high accuracy,strong non-linear approximation capabilities,and good generalization abilities.
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