摘要
悬架是车辆的重要部件之一,车辆行驶的平顺性、操纵稳定性等都与悬架性能的好坏有着直接的关系,被动悬架不能随路面的变化而变化,因此难以在变化的路面上达到最优的性能。因此,可控电子悬架是当今汽车发展的重要方向之一。主动悬架需要靠外加作用力实现对悬架的控制。因此会提高车辆的耗能,增加车辆的自身重量。由弹性元件和阻尼力可控减振器组成的半主动悬架有能耗小,易实现等优点,可以改善汽车行驶的平顺性,所以半主动悬架已成为汽车领域研究的一个热门课题。
由于车辆行驶的平顺性关注的是低频振动,而空气弹簧本身在低频处具有良好的减振性能,所以利用空气弹簧组成的变刚度半主动悬架系统具有很好的应用前景。
变刚度半主动悬架是一个非线性系统,而常规的控制策略应用于非线性系统具有一定的局限性。为了更好地逼近实际系统,获得更好的控制效果,需要寻找更有效的控制策略。因此,本文对变刚度半主动悬架特性进行了分析。在此基础上,着重研究了神经网络自适应控制方法,并对其进行控制仿真研究和试验研究。
论文首先根据悬架的评价指标,建立了变刚度半主动悬架的二自由度1/4车辆模型,利用改变悬架系统中空气弹簧的刚度实现减振。由于空气弹簧的非线性,所以采用神经网络控制调节空气弹簧的刚度,以期使悬架系统达到理想的减振效果。研究中主要用车身垂直加速度作为主要控制目标,以提高车辆行驶的平顺性,同时在仿真控制研究中兼顾悬架动挠度和车轮动载荷的变化,以提高车辆行驶安全性和操纵稳定性。
在仿真研究中,路面激励取为白噪声、正弦波和锯齿波,悬架类型采用被动悬架、PID控制半主动悬架、神经网络控制半主动悬架三种。对比分析了三种悬架的车身加速度、悬架动挠度、车轮动载荷三项指标及其对应的均方根值,仿真研究表明,半主动悬架优于被动悬架,其中神经网络控制效果最好,较好地改善了车辆行驶平顺性和操纵稳定性,此外,此种控制策略具有很好的鲁棒性应用于变刚度半主动悬架控制是可行的和有效的。
在试验研究中,采用了实现相对容易,计算量较小的神经网络直接自适应控制策
车辆半主动悬架神经网络自适应控制的研究
略对变刚度半主动悬架模型进行控制.对不同频率正弦激励下的车身垂直加速度进行
了分析,结果表明,神经网络控制能够使车身加速度得到衰减,在一定程度上提高了
车辆的行驶平顺性.
本文针对变刚度半主动悬架这种时变的、非线性复杂系统,提出将神经网络自适
应控制策略用于该悬架的控制,并通过仿真和试验验证了其可行性和有效性。这种新
型智能控制策略为变刚度半主动悬架提供了一种新的控制方法.
Suspension is one of the important part of vehicle, which has tremendous influence on performance of ride quality and handing stability. Passive suspension is difficult to meet requirements for a car which pursuits under variable environment. So, electronic suspension ?is one of the development direction of automotive. Active suspension needs extra force to control it, but this could consume more energy and enhance the weight of vehicle. Semi-active suspension is composed of controllable spring and damper element which consume little energy and are easy to design and manufacture. Because semi-active suspension can improve ride comfort so automobile researchs are enthusiastic are about the research into the semi-active suspension.
Vibration of automobile in low frequency band has importment effect on automobile performance and the air spring has high performance in this frequency band, the semi-suspension with controllable spring stiffeness can be used widely to improve the vehicle's ride comfort.
Variable rate semi-active suspension is a nonlinear system, but groovy control methods take on states limits when it is applied to the system. So more efficient control polices are required have contain limit to control the semi-suspension more practically and more effectively. In this dissertation nonlinear control method-neural network control method in allusion to variasble rate semi-active suspension are investigated based on its characters to carry on research simulation and experiment.
In this paper, according to the ride comfort level a quarter car mathematical model of two-DOF has been set up. Considering nonlinear rigidity of spring element, this dissertation use neural network control strategy to control semi-active suspension. The performance indexes body acceleration of vehicle are determined to show the ride comfort, at the same time are concerned about the value of suspension wheel load and the value of suspension displacement which show manipulate stability.
Passive suspension, PID control semi-active suspension and neural network control semi-active suspension are researched by simulation. White noise, sine wave and sawtooth
wave are used for excitation. The root mean square value of vehicle body acceleration, suspension displacement and vehicle wheel load are analyzed. And the results are showed in tables and charts. From the results we can draw a conclusion that semi-active suspension is much better than the passive suspension in improving the ride comfort and manipulate stability. The neural network control semi-active strategy is better than PID control strategy, it is a feasible and effective strategy..
During the experiment, the direct neural network control strategy are used because it is easy to calculate. From the frequency responses of the body acceleration we can see that neural network control strategy reduces the acceleration of the body and can improve the ride comfort of vehicle.
Given the time-variability parameters and the nonlinearities in variable rate semi-active suspension, the new strategy is employed to control the nonlinear characteristics of the suspension system. From the simulation and experiment research, we konw that the semi-active suspension self-adapt neural network control is applicable and effective. It also offers a new idea to the field of research on variable rate semi-active suspension control strategies.
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