关键词: 空气悬架系统, 不确定性, 多项式混沌展开, 自适应采样, 高维模型

Abstract:

To address the difficulty in calculating the dynamic characteristic responses of the full-vehicle air suspension system considering high-dimensional uncertain parameters, a dynamic characteristic analysis method based on the adaptive polynomial chaos expansion is proposed. First, the seven-degree-of-freedom dynamic model of full-vehicle air suspension system was established, and the system dynamic characteristic responses were calculated based on this model. Then, to alleviate the curse of dimensionality problem encountered by the traditional polynomial chaos expansion models when handling high-dimensional uncertain parameters, the system dynamic characteristic response functions were processed in a layered manner based on high-dimensional models with cutting points. The response functions were decomposed into constant terms, First-order and second-order composition functions. Next, polynomial chaos expansion models were applied to handle each composition function to obtain their specific expressions. Further, to address the problem of sample redundancy in low-probability regions and insufficient sampling in critical sensitive regions caused by traditional uniform sampling, an adaptive rectangular partitioning method was introduced for sampling in the uncertain parameters space, and the polynomial coefficients were solved by using regression methods. Finally, the effectiveness of proposed method was verified through numerical example. Analysis results of a full-vehicle air suspension system with 24-dimensional uncertain parameters show that, the reasonable selection of the orders of polynomial expansion and the convergence thresholds can effectively ensure the fitting accuracy and efficiency of the proposed method. The comprehensive performance of the proposed method in the analysis of system dynamic characteristics responses is overall superior to the traditional polynomial chaos expansion method. The uncertainty of some parameters has a significant impact on the dynamic characteristics responses of system, and the key influencing parameters for different responses have certain differences. These should be given special attention in the optimization design.

Key words: air suspension system, uncertainty, polynomial chaos expansion, adaptive sampling, high-dimensional model representation