LPV模型的动态压缩测量辨识算法

In solving the identification problem of linear parametric variation (LPV) model, the least squares algorithm is widely used due to the advantages of simple structure and low computational complexity. However, the results of least squares algorithm are subject to computational accuracy and model approximation accuracy, which are mutually exclusive in the same system. Therefore, there is always a certain error between the identification result and the true value of the algorithm. In addition, in the case of high-order LPV model identification or high sampling cost, the general model parameters are much more than the identification data. Consequently, it is difficult for the least squares algorithm to obtain stable identification results. The dynamic compression measurement identification (DCMI) algorithm proposed in this paper improves the system identification accuracy in this case from two aspects. First, the "uniform motion" and "non-uniform motion" models are used to represent the parametric function to improve the approximate accuracy of the model. Second, the under-sampling ability of the compressed sensing theory is utilized to improve the calculation accuracy of the parameters and expand the calculation scale of the model in the case of the same amount of data. The simulation results show that the proposed DCMI algorithm based on the "uniform motion" model can accurately identify the linear parametric function. Even in the case of insufficient identification data, the algorithm can still obtain stable identification results.