基于多目标遗传算法的高速履带车辆动力学模型参数修正研究

为了提高高速履带车辆多体动力学模型仿真结果的准确度，对模型参数修正方法进行了研究。建立了高速履带车辆多体动力学模型，根据其行驶工况统计规律，选择水泥路和砂石路作为参数修正的行驶路面条件。对比分析了模型参数修正前的仿真结果与实车测试结果，并给出了修正目标函数的表达式。通过正交实验设计筛选出对目标函数影响较大的待修正模型参数。为了解决修正效率低、计算量大的问题，建立了修正参数与目标函数之间关系的径向基神经网络近似模型。通过分析目标函数随修正参数的变化规律，采用多目标遗传算法NSGA-Ⅱ对两种工况条件下的模型参数同时进行修正，并确定了最终解。研究结果表明，动力学模型仿真结果的准确度得到了提高，证明该修正方法的有效性。

Research on Parameter Updating of High Mobility Tracked Vehicle Dynamic Model Based on Multi-objective Genetic Algorithm

A method of model parameter updating is researched to improve the accuracy of simulation results of high mobility tracked vehicle dynamic model. A dynamic model of high mobility tracked vehicle is established, and the cement road and the gravel road are selected for updating the model parameters according to the statistical regularity of the driving conditions. The simulation results of dynamic model without parameter updating and the corresponding real vehicle test results under the same driving conditions are compared and analyzed, and the expression of objective function for model parameter updating is given. The updating parameters which influence objective function strongly are screened by using orthogonal experiment design method. The radial basis function neural network approximation models about the relation among updating parameters and objective functions are established to solve the issues of large calculation quantity and inefficiency of parameter updating. By analyzing the change rule of objective functions with updating parameters, the dynamic model parameters are updated simultaneously by using the second non-dominated sorting genetic algorithm (NSGA-Ⅱ) for two driving conditions. The final results of parameter updating are obtained. The research results show that the simulation accuracy of high mobility tracked vehicle dynamic model is effectively improved, and the availability of the proposed method is validated.