改进的输出误差法用于不稳定飞机参数辨识

Considering the problems of numerical divergence and initial value dependence of the output error method in parameter identification of unstable aircraft, a system identification method combining neural network, particle swarm optimization algorithm, and Levenberg-Marquardt (LM) algorithm was designed. First, in order to solve the numerical divergence problem of the output error method, the neural network was utilized to approximate the dynamic characteristics of the system to be identified. The flight test data at different moments were used to train the neural network. The trained network could directly predict the motion state at the next moment, so as to avoid solving the unstable motion equation. Then, the particle swarm optimization algorithm was adopted to search the best damping factor in LM algorithm, and the improved LM algorithm was used to replace the Gauss-Newton algorithm in the output error method. Next, the improved LM algorithm was combined with the trained neural network to form a new parameter identification algorithm. Finally, the proposed algorithm was verified based on the closed-loop simulation flight test data of unstable aircraft. Research results show that compared with the estimation results of the traditional least square method and output error method with artificial stabilization, the proposed algorithm had higher estimation accuracy, and it could randomly select the initial value of the parameters to be identified, which overcomes the dependence of the output error method on the initial value of the parameters. The research results of this paper can be directly used in the identification of other unstable nonlinear dynamic systems, as well as other nonlinear optimization fields after modification.