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。
Abstract
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.
关键词
兵器科学与技术 /
高速履带车辆 /
参数修正 /
多目标遗传算法 /
径向基神经网络
{{custom_keyword}} /
Key words
ordnance science and technology /
high mobility tracked vehicle /
parameter updating /
multi-objective genetic algorithm /
radial basis function neural network
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
［1］ 韩宝坤, 李晓雷, 孙逢春. 履带车辆动力学仿真技术的发展与展望[J]. 兵工学报, 2003, 24(2): 246-249.
HAN Bao-kun, LI Xiao-lei, SUN Feng-chun. Present state and future outlook of the simulation of tracked vehicles[J]. Acta Armamentarii, 2003, 24(2): 246-249. (in Chinese)
[2] FerrettiG, Girelli R. Modelling and simulation of an agricultural tracked vehicle[J]. Journal of Terramechanics, 1999, 36(3):139-158.
[3] Rubinstein D, Hitron R. A detailed multi-body model for dynamic simulation of off-road tracked vehicles[J]. Journal of Terramechanics, 2004, 41(2/3):163-173.
[4] 马伟标, 王红岩, 芮强. 基于广义简约梯度算法的履带车辆模型参数修正[J]. 系统仿真学报, 2012, 24(4): 774-779.
MA Wei-biao, WANG Hong-yan, RUI Qiang. Researchon model updating for tracked vehicle dynamic model based on generalized reduced gradient method[J]. Journal of System Simulation, 2012, 24(4): 774-779. (in Chinese)
[5] 居乃鵕. 装甲车辆动力学分析与仿真[M]. 北京: 国防工业出版社, 2002.
JU Nai-jun. Dynamics analysis and simulation forarmored vehicle[M]. Beijing: National Defense Industry Press, 2002. (in Chinese)
[6] 丁法乾. 履带式装甲车辆悬挂系统动力学[M]. 北京: 国防工业出版社, 2004.
DING Fa-qian. Dynamics of tracked armored vehicle suspension system[M]. Beijing: National DefenseIndustry Press, 2004. (in Chinese)
[7] 王钦龙, 王红岩, 芮强. 轮式越野车动力学建模及行驶动力学特性仿真分析[J]. 装甲兵工程学院学报, 2012, 26(5): 34-38.
WANG Qin-long, WANG Hong-yan, RUI Qiang.Dynamic modeling and simulation analysis on ride dynamic of wheeled off-road vehicle[J]. Journal ofAcademy of Armored Force Engineering, 2012, 26(5): 34-38. (in Chinese)
[8] 陈立平, 张云清, 任卫群, 等. 机械系统动力学分析及 ADAMS应用教程[M]. 北京: 清华大学出版社, 2005.
CHEN Li-ping, ZHANG Yun-qing, REN Wei-qun,et al. Dynamic analysis of mechanical system and application course of ADAMS[M]. Beijing: Tsinghua University Press, 2005. (in Chinese)
[9] Mitschke M, Wallentowitz H.汽车动力学[M]. 陈萌三, 余强, 译. 北京: 清华大学出版社, 2009.
Mitschke M, Wallentowitz H. Dynamic of vehicle[M]. CHEN Meng-san, YU Qiang, translated. Beijing:Tsinghua University Press, 2009. (in Chinese)
[10] BuD X, Sun W, Yu H S, et al. Adaptive robust control based on RBF neural networks for duct cleaning robot[J]. International Journal of Control Automation and Systems, 2015, 13(2):475-487.
[11] 高隽. 人工神经网络原理及仿真实例[M]. 北京: 机械工业出版社, 2003.
GAO Jun. Theory and simulation example ofartificial neural network[M]. Beijing: China Machine Press, 2003. (in Chinese)
[12] David V,Sánchez A. Searching for a solution to the automatic RBF network design problem[J]. Neurocomputing, 2002, 42(1):147-170.
[13] 李伟平, 王世东, 周兵, 等. 基于响应面法和NSGA-Ⅱ算法的麦弗逊悬架优化[J]. 湖南大学学报:自然科学版, 2011, 38(6): 28-32.
LI Wei-ping, WANG Shi-dong, ZHOU Bing,et al. Macpherson suspension parameter optimization based on response surface method and NSGA-Ⅱ algorithm[J]. Journal of Hunan University: Natural Science, 2011, 38(6): 28-32. (in Chinese)
[14] Chen S M, Shi T Z, Wang D F, et al. Multi-objective optimization of the vehicle ride comfort based on Kriging approximate model and NSGA-Ⅱ[J]. Journal of Mechanical Science and Technology, 2015, 29(3):1007-1018.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
