基于时间响应误差与灰色关联度的拖拉机动力学模型评价方法

Evaluation Method of Tractor Dynamic Models Based on Time Response Error and Grey Relational Analysis

摘要:

针对拖拉机动力学模型准确性评价环节缺失以及系统化、标准化评价手段缺乏等问题，基于模型仿真系统的校核、验证与验收理论，提出了一种基于时间响应误差与灰色关联度的模型准确性评价方法，构建了包含相位误差、幅值误差和形状误差的分层验证指标体系，并采用灰色关联度法对动态响应曲线进行分段评估。设计了4组包含明确误差成分的理想算例，采用本文方法与传统数值误差分析方法进行对比，结果表明，本文方法能够对各算例多重误差特征进行有效解耦和准确识别，精确反映误差来源与分布的同时，其综合评价结果准确性优于传统方法。在此基础上以自主构建的动力换挡拖拉机纵向动力学模型为应用实例，采用所提方法评价不同工况下的模型准确性。结果显示，模型准确性综合结果均值为90.95，在高速及复杂路面等条件下，各误差特征评分明显降低，部分极端工况综合结果小于88，反映出所建模型针对不同工况存在一定局限，符合客观事实，验证了所提评价方法的有效性和全面性。

Abstract:

Aiming to address the issues of missing accuracy evaluation for tractor dynamic models and the lack of systematic, standardized assessment methods, an accuracy evaluation approach was proposed based on time-response error and grey relational analysis, grounded in the verification, validation, and accreditation (VV&A) theory of model simulation systems. A hierarchical validation index system comprising phase error, amplitude error, and shape error was constructed, and the grey relational degree method was applied for segmental evaluation of dynamic response curves. Four sets of idealized test cases with defined error components were designed and analyzed by using both the proposed method and traditional numerical error analysis techniques. The results demonstrated that the proposed method effectively decoupled and accurately identified multiple error characteristics in each test case, precisely reflecting the sources and distribution of errors, while its comprehensive evaluation accuracy outperformed traditional methods. Furthermore, taking a self-developed longitudinal dynamic model of a power-shift tractor as an application case, real vehicle data were collected under five road conditions: tilled field, untilled field, stubble field, concrete surface, and mixed road surface. The proposed method was used to compare and analyze simulation results against real vehicle data under various working conditions. The results indicated that the mean comprehensive accuracy score of the model was 90.95. Under high-speed and complex road conditions, scores for various error characteristics decreased significantly, with comprehensive results in some extreme conditions falling below 88, reflecting certain limitations of the model under different working conditions, which aligned with objective reality and verified the effectiveness and comprehensiveness of the proposed evaluation method.