%0 Journal Article
%T 基于交替迭代的车辆主动悬架LQG控制器设计<br>Design of LQG controller for vehicle active suspension system based on alternate iteration
%A 于曰伟
%A 周长城
%A 赵雷雷
%A 邢玉清
%A 石沛林&lt
%A br&gt
%A YU Yuewei
%A ZHOU Changcheng
%A ZHAO Leilei
%A XING Yuqing
%A SHI Peilin
%J 山东大学学报（工学版）
%D 2017
%R 10.6040/j.issn.1672-3961.0.2016.462
%X 摘要： 针对主动悬架线性二次高斯控制(linear-quadratic-Gaussian control, LQG)控制器,提供一种快速确定其最佳控制加权系数及最优控制力的方法。 通过车辆行驶平顺性评价指标分析,利用无量纲归一化思想建立主动悬架最优控制目标函数,给出平顺性加权系数与控制加权系数间的关系;根据主动悬架力学模型,利用Newmark-β显式积分法,建立平顺性加权系数仿真分析模型。以路面不平度作为输入激励,以轮胎动位移和悬架动挠度为约束条件,借鉴交替迭代思想建立交替迭代优化算法,建立主动悬架LQG控制加权系数及控制力的优化方法。通过与现有LQG控制器设计方法的对比分析,对本设计方法的先进性和可靠性进行仿真验证,结果表明设计的LQG控制器能够显著改善车辆的乘坐舒适性。<br>Abstract: For the active suspension LQG(linear-quadratic-Gaussian control)controller, an objective and fast method to determine the optimal control weighting coefficient and control force was established. Through analysis of riding comfort evaluation index of vehicle, using dimensionless normalized thoughts, the optimal control objective function of active suspension was established, and the relationship between ride comfort weighted coefficient and control weight coefficient was obtained. According to 1/4 vehicle active suspension mechanical model, using Newmark-β integration method, a simulation analysis model for the weighted coefficient of ride comfort was established. Using the road roughness as input, the tire dynamic displacement and suspension dynamic deflection as constraint conditions, by referring to alternative iterative thoughts, the alternating iterative optimization algorithm was established, an optimization design method of LQG control weighted coefficient and control force was presented. By comparing with the existing LQG controller design method, the optimal control weighting coefficient and control force design method were verified. The results showed that the LQG controller could significantly improve the ride comfort of vehicle
%K LQG控制器
%K 加权系数
%K 交替迭代优化算法
%K 主动悬架
%K 最优控制力
%K &lt
%K br&gt
%K active suspension
%K weighted coefficient
%K optimal control force
%K LQG controller
%K alternating iterative optimization algorithm
%U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2016.462