【作者】 郭大蕾；

【导师】 胡海岩；

【作者基本信息】 南京航空航天大学 ， 固体力学， 2002， 博士

【摘要】 行驶平顺性和乘坐舒适性是衡量轿车行驶品质的基本指标。本研究试图建立基于新型智能材料的车辆悬架振动控制新技术，进而改善车辆的平顺性和舒适性。 影响车辆乘坐舒适性的主要因素是车身的垂直振动。为了描述这种振动，先将车辆简化为1／4车二自由度模型，讨论并分析了悬架系的振动特性以及悬架参数对此特性的影响。然后将车辆简化为1／2车四自由度的悬架模型，根据其车身垂直振动加速度幅频特性的折算结果，验证了二自由度分析模型的正确性。由于在某一特定条件下经参数优化得到的被动悬架，不能适应路面及车辆行驶状况等条件的变化，因而在提高悬架行驶平顺性的研究过程中，提出了悬架半主动控制的要求。 在分析新型智能材料用做作动器的磁流变阻尼器特性的基础上，本文给出阻尼器线圈的前置电流放大电路，并讨论了因磁流变液体固液态之间的可逆变化而产生的悬架系附加非线性刚度。通过分析车身振动的变化，可知这一附加的非线性刚度为一弱非线性项，对悬架的侧倾刚度和侧倾角的影响不大。因此，基于磁流变阻尼器的半主动悬架在改善行驶平顺性的同时，能够保证车辆的操纵稳定性。 由于磁流变液体的滞回特性，采用磁流变阻尼器的车辆悬架系成为一个典型的非线性系统。本研究采用神经网络控制来解决这类非线性系统的控制问题。本文提出一种带二次动量项的多层前向网络误差反传算法，提高了神经网络的误差收敛速度。针对悬架系统的辨识和控制过程，本文提出一种神经网络间接自适应控制方法，优化了控制结构，提高了控制精度。半主动悬架的数值仿真结果表明，磁流变阻尼器半主动悬架的减振效果明显优于被动悬架以及其他控制方式和阻尼调节方式。根据悬架的仿真控制结果及轿车悬架的具体情况，本文建议对RD1005型磁流变阻尼器进行部分参数修改，以期使阻尼作动器更适用、有效。 本文最后对含有磁流变阻尼减振器的半主动悬架进行了实验研究。路面低频输入和磁流变液体毫秒级快响应的特点，为神经网络的在线控制提供了条件。实验控制过程采用计数器／定时器，实现了计算机的多任务工作方式。同时，利用TurboC语言实现对硬件的访问。通过比较神经网络参数的影响，可知单隐含层的神经网络能够实现半主动悬架的精确控制，太多的隐含结点数对控制效果没有很大提高，反而增加了计算时间。在半主动悬架控制中，解决了神经网络控制的实时问题。实验结果表明，基于磁流变阻尼器半主动悬架的减振效果明显优于被动悬架及其它控制策略。同时证实了磁流变阻尼器附加非线性刚度的存在及其弱非线性特性。这些结果对车辆行驶平顺性的改善具有理论和实践意义。更多还原

【Abstract】 Traveling and ride comfort is the basic evaluation criterion for a ground vehicle. This dissertation presents an effective method based on the new-type smart material for vibration isolation of vehicle suspensions, and further to improve the performance of suspensions. The paper reports a systematical investigation on semi-active vehicle suspension, including dynamic characteristic of vehicle suspension, the properties and functions of a magnetoreological (MR) damper, the adaptive neural network control and the experiment on a test rig of quarter car model. The four topics are studied from Chapters 2 to 5, respectively.The ride comfort of a vehicle mainly depends on the vertical vibration of vehicle body. Chapter 2 presents the study on a two-degree-of-freedom model of quarter car. The vibration characteristic and the effect of the suspension parameters on vertical vibration are mainly analyzed. The amplitudes of vehicle body acceleration of half car model of four degrees of freedom verify the efficacy of quarter car model. The passive suspension via the specified parameters can optimize the suspension sometimes. However, the global optimization is difficult to achieve under disturbance. This paper presents an adaptive control for the semi-active suspension using the combination of MR damper and feedback neural networks.Analyzing the MR damper, a smart actuator, Chapter 3 gives the design of pre-amplifier of current, which provides the external magnetic field. And it discusses the additional nonlinear stiffness resulted from the transition of MR fluid from liquid to semi-solid or solid. According to the study of the vibration of car body, it can be concluded that this additional stiffness, a weakly nonlinear term, would not influence the purpose of vibration isolation. At the same time, the nonlinear stiffness hardly affects the rolling stiffness and roll angle. Consequently, the suspension with MR damper not only increases ride comfort, but also guarantees the controlling stability of vehicle.As the MR damper features nonlinearity, the vehicle suspension equipped with MR damper is a nonlinear system. Nonlinear neural network (NN) control strategy, which was certified the high capacity of approximation, is adopted to control this typical nonlinear system. Chapter 4 presents an error back propagation algorithm with quadratic momentum of the multilayer forward neural networks that will speed up the error convergence velocity. And it proposes an indirect adaptive neural network law. This law optimizes the control structure and improves the quality of control. The numerical simulation results show that the semi-active suspension with MR damper using NN strategy is superior to those with traditional control or without any control. To narrow the hysteretic loops and improve saturation, the author suggests partially modifying the parameters of the RD1005 MR damper hi terms of the exclusive utilization, such as the vehicle suspension.Chapter 5 presents an experiment on a test rig of quarter car model equipped with MR damper. The low frequency of road-induced vibration of a vehicle and quick response of MR damper make NN real-time control of possible. In the experiment the timer and counter are adopted to realize computer multi-work environment, and the Turbo-C computer language is used to call on the hardware. The effects of various neural networkparameters on the vibration of car body are compared and the results demonstrate that only single implicit layer structure can considerably improve the ride comfort. However, excessive implicit nodes can not increase the performance but lengthen the time consuming. The real-time control of neural network is adopted successively for the semi-active suspension with MR damper. The experiments indicate that the suspension with MR damper and NN control is superior to the passive one in the frequency band of concern. The isolation performance is particularly pronounced at the resonance of car body. The existence of additional nonlinear stiffness and its weak affecti更多还原