基于改进遗传算法的主动悬架最优控制研究

提出了遗传-粒子群优化算法对主动悬架的控制规则进行了优化.利用Matlab/Simulink对主动悬架系统进行了仿真,并与传统的被动悬架进行了性能比较和分析,仿真结果表明基于改进遗传算法的主动悬架最优控制能够有效提高车辆的平顺性和操纵稳定性.     

基于遗传算法的车辆半主动悬架控制系统参数优化

遗传算法(Genetic Algorithm,GA)是一种基于自然群体遗传演化机制的高效优化算法,它能模拟自然界生物进化过程,依据适者生存,优胜劣汰的进化规则,采用人工进化的方式对目标群体进行遗传操作,不断得到更优群体。文章根据遗传算法基本思路,在MATLAB/Simulink中搭建了基于遗传算法的车辆半主动悬架参数优化模型,利用该算法对半主动悬架控制系统参数进行了优化,并对优化结果进行了仿真分析,结果表明,优化后车辆簧载质量加速度均方根值降低31.1%、悬架动挠度均方根值降低11.2%、轮胎动载荷均方根值降低7.1%,车辆平顺性得到提升。     

汽车主动悬架与电动助力转向系统多目标优化及集成控制

建立了整车主动悬架和电动助力转向系统动力学模型,构建线性控制的主动悬架和PD控制的电动助力转向系统集成控制器,应用遗传算法对集成控制系统的结构参数和控制参数进行多目标优化。结果表明,经过多目标优化后的集成控制系统,既可保证车辆操纵轻便性,又显著提高了整车操纵稳定性、安全性和行驶平顺性。     

基于遗传算法的汽车麦弗逊悬架的优化

遗传算法是一种基于生物自然选择与遗传机理的随机搜索算法。文中利用遗传算法对麦弗逊悬架进行了优化。结果表明,该算法使悬架设计工作更为精确、有效;优化后,悬架系统的运动学特性得到了改善。     

基于遗传算法的汽车麦弗逊悬架的优化

遗传算法是一种基于生物自然选择与遗传机理的随机搜索算法。文章利用遗传算法对麦弗逊悬架进行了优化。结果表明,该算法使悬架设计工作更为精确、有效。优化后,悬架系统的运动学特性得到了改善。     

基于遗传算法的汽车麦弗逊悬架的优化

遗传算法是一种基于生物自然选择与遗传机理的随机搜索算法。利用遗传算法对麦弗逊悬架进行了优化。结果表明．该算法使悬架设计工作更为精确、有效，优化后悬架系统的运动学特性得到了改善。     

基于振动状态估计的电动静液压主动悬架切换控制研究

为减少电动静液压(EHA)主动悬架作动器失效对车辆动态特性的影响,设计了一种基于振动状态估计的EHA主动悬架切换控制策略。建立EHA作动器失效模型,基于卡尔曼滤波算法,以悬架系统状态变量残差与簧载质量加速度作为复合切换条件,设计了一种EHA主动悬架切换控制策略,并进行了仿真和台架试验。结果表明,该切换控制策略在作动器失效后能够进行模式切换,可消除切换紊乱现象,并在短暂时滞后,使EHA主动悬架性能恢复至与正常悬架相近的水平,改善了作动器失效造成的车辆动态特性恶化问题。     

车用汽油机过渡工况进气流量预测研究

提出了一种基于混合遗传算法的径向基神经网络(HGARBF)的车用汽油机过渡工况进气流量预测模型。首先设计了一种新的混合遗传算法,利用梯度算法每次迭代得到的结果来改进遗传算法的群体,将遗传算法的最优个体与梯度算法的迭代解相比较,选择其中的最优点作为梯度算法下一步迭代的起始点,运用该混合遗传算法进行径向基神经网络参数的优化,改善径向基神经网络不同初始参数对其性能的影响;然后建立了基于HGARBF网络的过渡工况进气流量的预测模型。仿真结果表明,该预测模型优于经典的进气流量平均值模型,为精确及时测试汽油机进气流量提供了新的方法。     

摇臂推杆式电磁主动悬架的鲁棒控制与优化

针对被动悬架和半主动悬架在抑制车辆振动方面存在的不足,提出一种摇臂推杆式电磁主动悬架并试制样机,它具有结构新颖、加工容易和模块化设计等特点。对该悬架系统非线性特性进行分析,得到等效刚度和等效簧下质量等参数的摄动区间。在保证系统鲁棒稳定性的前提下,以车身加速度、悬架动行程、轮胎动位移和主动力最小作为优化目标,设计鲁棒H∞控制器。为降低控制器保守性,将车身质量参数摄动范围分段,优化设计自适应鲁棒H∞控制器,通过静态查表方式离线控制,保证实时性,且无需切换控制器,避免对乘员产生的冲击感。     

车辆主动油气悬架系统分层控制策略的研究

基于虚拟样机技术构建了工程车主动油气悬架控制系统的数字开发平台.针对工程车行驶路况的不确定性、部分参数的时变性和油气悬架系统的强非线性,提出了有限带宽主动油气悬架系统分层控制策略,并设计了基于遗传算法的模糊PID上层力控制器和基于模型的下层电压控制器.将该控制算法集成到悬架控制系统数字开发平台中进行联合仿真.结果表明,所研制的有限带宽主动悬架分层控制器可显著改善车辆的行驶平顺性.     

大跨悬索桥MTMD抖振控制的参数优化

针对悬索桥抖振控制问题,建立有限元模型,应用神经网络和遗传算法对多重调频质量阻尼器(MTMD)进行双参数优化。以某大跨悬索桥为例,利用神经网络改进的谐波合成模拟方法(RBF-WAWS法)对脉动风速进行模拟,并换算成抖振力作用主梁上,通过时程分析及后处理获取主跨跨中横桥向响应值。将响应值的均方差作为优化目标函数,以MTMD总质量、个数及阻尼比作为优化变量和约束条件,采用神经网络拟合目标函数并应用改进的自适应遗传算法进行寻优。结果表明,优化后的MTMD能有效控制悬索桥在脉动风作用下的抖振响应,减振率达48%。提出的理论与计算方法对悬索桥中MTMD的设置及参数选取具有实际工程意义。     

悬架衬套安装方向优化设计

基于ADAMS和iSIGHT软件,提出了对悬架衬套安装方向进行优化设计的方法及流程.通过调整悬架衬套安装方向来改变悬架导向杆系的受力,从而使悬架弹性运动学特性满足特定要求.以某一轿车麦弗逊前悬架为例,通过优化前、后结果的对比分析可知,采用优化后的衬套安装方向,悬架弹性运动学特性参数得到改善,验证了 .该优化方法的有效性.     

Development of Preview Active Suspension Control System and Performance Limit Analysis by Trajectory Optimization

The main role of the suspension system is to achieve ride comfort by reducing vibrations generated by the road roughness. The active damper is getting much attention due to its reduced cost and ability to enhance ride comfort especially when the road ahead is measurable by an environment sensor. In this study a preview active suspension control system was developed in order to improve ride comfort when the vehicle is passing over a speed bump. The control system consists of a feedback controller based on the skyhook logic and a feedforward controller for canceling out the road disturbance. The performance limit for the active suspension control system was computed via trajectory optimization to provide a measure against which to compare and validate the performance of the developed controller. The simulation results indicated that the controller of this study could enhance ride comfort significantly over the active suspension control system employing only the skyhook feedback control logic. Also the developed controller, by displaying similar control pattern as the trajectory optimization during significant time portions, proved that its control policy is legitimate.     

基于遗传算法和LQG控制的汽车半主动悬架结构和控制参数的集成优化研究

传统半主动悬架的设计是先设计其结构参数后设计其控制器参数，这样易造成系统失去全局最优性能。针对这一问题，本文提出一种基于遗传算法和LQG控制的集成优化半主动悬架结构参数和控制参数的方法，通过理论分析和仿真结果表明此方法与传统优化方法相比，对改善汽车行驶平顺性和提高汽车行驶安全性具有较优的效果。     

Optimization of the Lateral Stability of Rail Vehicles

Summary The lateral stability of a rail vehicle is optimized using a combination of multibody dynamics, sequential quadratic programming, and a genetic algorithm. Several steps are taken to validate this integrated approach and to show its effectiveness. First, a hand-derived solution to a 17 degree of freedom linear rail vehicle model is compared to the simulation results from the A'GEM multibody dynamics software. Second, the calculation of the ‘critical speed’ (above which a rail vehicle response becomes unstable) using sequential quadratic programming is validated for a specific example. In the process, the existence of sharply-discontinuous ‘cliffs’ in the plots of critical speed versus suspension stiffnesses are identified. These cliffs, which are due to switching of the least-damped mode in the system, greatly hinder the application of gradient-based optimization methods. Two methods that do not require gradient information, a genetic algorithm and the Nelder-Mead's Simplex algorithm, are used to optimize the critical speed. The two algorithms and their results are compared. In recognition of the cliff phenomenon, the definition of critical speed is generalized to make it a more practical measure of lateral stability.     

Optimization of the Lateral Stability of Rail Vehicles

Summary The lateral stability of a rail vehicle is optimized using a combination of multibody dynamics, sequential quadratic programming, and a genetic algorithm. Several steps are taken to validate this integrated approach and to show its effectiveness. First, a hand-derived solution to a 17 degree of freedom linear rail vehicle model is compared to the simulation results from the A'GEM multibody dynamics software. Second, the calculation of the 'critical speed' (above which a rail vehicle response becomes unstable) using sequential quadratic programming is validated for a specific example. In the process, the existence of sharply-discontinuous 'cliffs' in the plots of critical speed versus suspension stiffnesses are identified. These cliffs, which are due to switching of the least-damped mode in the system, greatly hinder the application of gradient-based optimization methods. Two methods that do not require gradient information, a genetic algorithm and the Nelder-Mead's Simplex algorithm, are used to optimize the critical speed. The two algorithms and their results are compared. In recognition of the cliff phenomenon, the definition of critical speed is generalized to make it a more practical measure of lateral stability.     

A new active variable stiffness suspension system using a nonlinear energy sink-based controller

This paper presents the active case of a variable stiffness suspension system. The central concept is based on a recently designed variable stiffness mechanism which consists of a horizontal control strut and a vertical strut. The horizontal strut is used to vary the load transfer ratio by actively controlling the location of the point of attachment of the vertical strut to the car body. The control algorithm, effected by a hydraulic actuator, uses the concept of nonlinear energy sink (NES) to effectively transfer the vibrational energy in the sprung mass to a control mass, thereby reducing the transfer of energy from road disturbance to the car body at a relatively lower cost compared to the traditional active suspension using the skyhook concept. The analyses and simulation results show that a better performance can be achieved by subjecting the point of attachment of a suspension system, to the chassis, to the influence of a horizontal NES system.