车辆悬架控制系统的仿真研究【自动化毕业论文开题报告外文翻译说明书】.zip
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自动化毕业论文开题报告外文翻译说明书
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系统研究【说明书论文开题报告外文翻译
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车辆悬架控制系统的仿真研究【自动化毕业论文开题报告外文翻译说明书】.zip,自动化毕业论文开题报告外文翻译说明书,控制系统设计【说明书论文开题报告外文翻译】,系统研究【说明书论文开题报告外文翻译,系统研究【说明书论文开题报告外文翻译】,自动化系统设计,的设计【毕业论文
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毕 业 设 计（论 文）任 务 书1本毕业设计（论文）课题应达到的目的： 通过毕业设计，使学生受到对大学所学知识的综合训练，在不同程度上提高各种设计及应用能力，具体包括以下几方面：1.调查研究、中外文献检索与阅读的能力。2.综合运用专业理论、知识分析解决实际问题的能力。3.定性与定量相结合的独立研究与论证的能力。4.控制方法的制定、仪器设备的使用、程序调试及对仿真数据进行分析处理的能力，提高使用计算机的能力。5.逻辑思维与形象思维相结合的文字及口头表达的能力。6.撰写设计论文的能力。 2本毕业设计（论文）课题任务的内容和要求（包括原始数据、技术要求、工作要求等）： 1.本设计以车辆悬架系统为例，进行数学建模，选用非线性控制、模糊控制、神经网络等先进控制理论中的一种方法与传统的PID控制方法分别对其进行研究，得出主、被动悬架车身位移，速度及加速度等一些仿真曲线，并与PID控制进行比较，说明两种控制方法的优缺点。2.按时完成开题报告书。3.按时完成毕业设计外文参考资料，定期开展毕业设计自查。4.在工程设计过程中要有科学认真的工作态度，数据合理可靠。5.能够圆满完成指导老师布置的课题任务，技术设计方案合理，具有一定的可行性，能够体现一定的创新性。6.按时参加答辩，在答辩前各项规定的资料要齐全。 毕 业 设 计（论 文）任 务 书3对本毕业设计（论文）课题成果的要求包括图表、实物等硬件要求： 1.按期完成一篇符合金陵科技学院论文规范的毕业设计说明书（毕业论文），能详细说明设计步骤和思路； 2.能有数学建模及控制器的设计过程。3.能有仿真结果及相应的源程序。4主要参考文献： 1邵瑛.车辆主动悬架控制策略的仿真研究D.南京:南京农业大学，20032孙求理.张洪欣.主动悬架的发展和技术现状J.世界汽车.1996. 5.3王磊.汽车主动悬架控制策略的研究D.杭州:浙江工业大学，2003.4张庙康，胡海岩. 车辆悬架振动控制系统研究的进展J. 振动、测试与诊断，1997，17(1)：7155方敏，王峻，陈无畏. 汽车半主动悬架的自适应LQG控制J. 汽车工程，1997，19(3)：2002056檀润华，陈鹰，路甬祥. 路面对汽车激励的时域模型建立及计算机仿真J. 中国公路学报，1998，11(3)：961027刘金琨.先进PID控制MATLAB仿真（第2版）M.北京：电子工业出版社，2004.8寇发荣.车辆EHA主动悬架PID控制的实验研究J.西安科技大学.2009.9符永法.几种新型PID调节器参数的整定法J，化工自动化及仪表，1997, 24 (1).25-29.10程耀东，李培玉.机械振动学M.浙江:浙江大学出版社，2005, 1-240.11李克强，董坷，永井正夫.多自由度车辆模型主动悬架及鲁棒控制J，汽车工程.2003, 25 (1).7-11.12董景新，赵长德.控制工程基础M.北京:清华大学出版社，1999, 12-193.13刘豹.现代控制理论(第2版)M.北京:机械工业出版社，2004, 51-295.14李宜达.控制系统设计与仿真M.北京:清华大学出版社，2004, 26-188.15李国勇.智能控制及其MATLAB实现M.北京:电子工业出版社，2005, 216. 毕 业 设 计（论 文）任 务 书5本毕业设计（论文）课题工作进度计划：起 讫 日 期工 作 内 容2015.11.102015.12.13调研、收集相关资料、对学生进行初步辅导，拟题、选题、填写任务书。2015.12.152015.12.31学生查看任务书，为毕业设计的顺利完成，进行前期准备。12月31日前正式下发任务书。12月21日两个系提交专业选题分析总结（撰写要求详见对内通知中附件2）2016.01.092016.04.05学生在指导教师的具体指导下进行毕业设计创作；拟定论文提纲或设计说明书（下称文档）提纲；撰写及提交开题报告、外文参考资料及译文、论文大纲； 在2016年4月5日前学生要提交基本完成的毕业设计创作成果以及文档的撰写提纲，作为中期检查的依据。指导教师指导、审阅，定稿由指导教师给出评语，对论文主要工作未通过的学生下发整改通知。2016.04.062016.04.10提交中期课题完成情况报告给指导教师审阅；各专业组织中期检查（含毕业设计成果验收检查）。2016.04.112016.05.10进行毕业设计文档撰写；2016年5月8日为学生毕业设计文档定稿截止日。2016年5月9日-13日，指导教师和评阅教师通过毕业设计（论文）管理系统对学生的毕业设计以及文档进行评阅，包括打分和评语。5月1日前，做好答辩安排，通知学生回校进行答辨2016.05.142016.05.15查看答辩安排，毕业设计（论文）小组答辩2016.05.162016.05.29对未通过答辨的学生进行二次答辨完成毕业设计的成绩录入2016.05.302016.06.07根据答辩情况修改毕业设计（论文）的相关材料，并在毕业设计（论文）管理系统中上传最终稿，并且上交纸质稿。2016年6月7日为学生毕业设计文档最终稿提交截止日。2016.06.072016.06.30各系提交本届毕业设计（论文）的工作书面总结及相关材料。所在专业审查意见：通过负责人： 2015 年 12 月21 日 毕 业 设 计（论文） 开 题 报 告 1结合毕业设计（论文）课题情况，根据所查阅的文献资料，每人撰写不少于1000字左右的文献综述： 悬挂系统是一种由弹簧、减震筒和连杆所构成的车用系统,用于连接车辆与其车轮。 早在 19 世纪初期,大部分英国四轮马车都配备弹簧,木制弹簧用于轻型马车,而较大的 马车弹簧则采用铁制。这些铁制的弹簧由低碳钢制成而且通常叠成多层成为板式弹簧。 当时的这些弹簧装置就是一种结构简单的悬挂系统。 悬架系统是车辆的一个重要组成部分。车辆悬架性能是影响车辆行驶平顺性、操纵稳定性和行驶速度的重要因素。传统的被动悬架一般由具有固定参数的弹性元件和阻尼元件组成,被设计为适应某 一种路面,限制了车辆性能的进一步提高。20 世纪 70 年代工业发达国家已经开始研究基于振动主动控制的主动、半主动悬架系统。 近年来电子技术、测控技术、机械动力学等学科的快速发展,使车辆悬架系统由传统被动隔振发展到振动主动控制。特别是信息科学中对最优控制、自适应控制、模糊控制、人工神经网络等的研究,不仅使悬架系统振动控制技术在现代控制理论指导下更趋完善,同时已开始应用于车辆悬架系统的振动控制,使悬架系统振动控制技术得以快速发展。随着车辆结构和功能的不断改进和完善,研究车辆振 动,设计新型悬架系统,将振动控制到最低水平是提高现代车辆质量的重要措施。 车辆悬架控制系统是一个含有许多不确定因素的非线性的机、电、液一体化系统，基于模型的线性控制策略受到很大的限制，也即用传统控制方法难以达到其预定的性能要求。目前应用于车辆悬架控 制系统的控制方法主要有现代控制方法 (如自适应控制方法、预见控制方法、最优控制方法及鲁棒控制方法)和智能控制方法(如模糊控制、神经网络控制) 以及复合控制方法。 本设计拟以车辆悬架系统为例，进行数学建模，选用非线性控制、模糊控制、神经网络等先进控制理论中的一种方法与传统的PID控制方法分别对其进行研究，得出主、被动悬架车身位移，速度及加速度等一些仿真曲线，并与PID控制进行比较，得出结论，说明两种控制方法的优缺点。参考文献： 1邵瑛.车辆主动悬架控制策略的仿真研究D.南京:南京农业大学，2003 2孙求理.张洪欣.主动悬架的发展和技术现状J.世界汽车.1996. 5. 3王磊.汽车主动悬架控制策略的研究D.杭州:浙江工业大学，2003. 4张庙康，胡海岩. 车辆悬架振动控制系统研究的进展J. 振动、测试与诊断，1997，17(1)：715 5方敏，王峻，陈无畏. 汽车半主动悬架的自适应LQG控制J. 汽车工程，1997，19(3)：200205 6檀润华，陈鹰，路甬祥. 路面对汽车激励的时域模型建立及计算机仿真J. 中国公路学报，1998，11(3)：96102 7刘金琨.先进PID控制MATLAB仿真（第2版）M.北京：电子工业出版社，2004. 8寇发荣.车辆EHA主动悬架PID控制的实验研究J.西安科技大学.2009. 9符永法.几种新型PID调节器参数的整定法J，化工自动化及仪表，1997, 24 (1).25-29. 10程耀东，李培玉.机械振动学M.浙江:浙江大学出版社，2005, 1-240. 11李克强，董坷，永井正夫.多自由度车辆模型主动悬架及鲁棒控制J，汽车工程.2003, 25 (1).7-11. 12董景新，赵长德.控制工程基础M.北京:清华大学出版社，1999, 12-193. 13刘豹.现代控制理论(第2版)M.北京:机械工业出版社，2004, 51-295. 14李宜达.控制系统设计与仿真M.北京:清华大学出版社，2004, 26-188. 15李国勇.智能控制及其MATLAB实现M.北京:电子工业出版社，2005, 216. 毕 业 设 计（论文） 开 题 报 告 2本课题要研究或解决的问题和拟采用的研究手段（途径）： 本文主要要解决的问题主要有如下几个：首先，如何对汽车的悬挂系统进行建模，其中涉及到选取什么样的模型以及选取哪一些参数来模拟悬挂系统的性能；其次是选取何种控制方法来进行对车辆悬挂系统控制，并与PID控制方法进行比较，共有适应控制方法、预见控制方法、最优控制方法及鲁棒控制方法)和智能控制方法(如模糊控制、神经网络控制) 以及复合控制方法等控制方法可共选择；第三是怎样选取最佳的参数来获取各个控制器的最优效果；最后是选取哪些方面对两种控制器进行比较并能够正确地得出控制器的优缺点。 本文的主要工作有以下方面：首先搜索资料，研究哪些方面对于车辆悬挂系统的性能影响比较大，进而选择合适的模型和属性来对车辆悬挂系统进行建模；其次，研究和分析了控制理论一些基本的控制方法，针对汽车悬挂系统模型，结合MATLAB软件对悬挂系统进行开环系统仿真，观察系统在受到路面扰动后的响应曲线，分析曲线特征；其次根据期望的性能指标，设计所需的控制器，将其置于悬挂装置中，再一次进行MATLAB仿真，对于不满足要求的，可适当进行控制器参数的调整，直到满足要求为止；最后对设计出的多种控制器进行综合的分析与比较，得出最优控制器，并在最优控制器的基础上又作了进一步的对比分析。 毕 业 设 计（论文） 开 题 报 告 指导教师意见：1对“文献综述”的评语：能较好的查阅相关文献，具备一定的文献查阅能力和文献综述能力，可以从文献中获得相关信息,论文研究方向比较明确，能利用文献内容形成自己的观点，内容比较充实。2对本课题的深度、广度及工作量的意见和对设计（论文）结果的预测：本文研究目的明确，思路比较清晰，具有一定深度和广度，工作量适中，对论文设计合理，毕业论文进度安排合理，要求学生给出编制的控制程序，实现车辆悬架系统的位置、速度及加速度控制。3.是否同意开题： 同意 不同意 指导教师： 2016 年 02 月 19 日所在专业审查意见：同意 负责人： 2016 年 03 月 30 日Fuzzy Control of Vehicle Semi-active Suspension with MR DamperAbstractA reasonable mathematical model of semi-active suspension is established based on a quarter vehicle. A fuzzy controller is proposed and its fuzzy rules are deduced using Matlab Fuzzy logic control toolbox. Simulation model for the whole system is completed by means of Matlab/Simulink. The response curves under random road profile excitations of semiactive suspension are given, which are compared with those of passive suspension. The comparison show that the body vertical acceleration is reduced obviously by way of using fuzzy control on semi-active suspension, meanwhile, the vehicle ride comfort and handling stability is improved.Keywords: semi-active suspension; fuzzy control; Matlab simulinkI. INTRODUCTIONThere are three main types of vehicle suspensions that have been proposed, that is, passive, semi-active and active suspensions, which depend on the operation mode to improve vehicle ride comfort, vehicle safety, road damage minimization and the overall vehicle performance. Normally, conventional passive suspensions are effective only in a certain frequency range and no on-line feedback action is used. Thus, optimal design performance cannot be achieved when the system and its operating conditions are changed. On the contrary, active suspensions can improve the performance of the suspension systems over a wide range of frequency and can adapt to the system variations based on on-line changes of the actuating force. Therefore, active suspensions have been extensively studied since 1960s and various approaches have been proposed, see Ref. 1 and references therein. However, active suspensions normally require large power supply, which is the main drawback that prevents this technique from being used extensively in practice. Since 1970s, semi-active suspensions have received much attention since they can achieve desirable performance than passive suspensions and consume much less power than that of active suspensions. Especially, when some controllable dampers, such as electro-rheological (ER) dampers and magneto-rheological (MR) dampers, are available in practice recently, semi-active suspensions are more practical than ever in engineering realization.In particular, MR dampers have found considerable attraction in vibration reduction of bridges, helicopter rotors, truss structures, suspension seats, seismic reduction, and vibration isolator, etc. Semi-active control with MR dampers for vehicle suspensions have also been studied by many researchers 2,3. Many control strategies such as skyhook, groundhook and hybrid control 4, H1 control 5 and model-following sliding mode control 6 have been evaluated in terms of their applicability in practice.However, the practical use of MR dampers for control is significantly hindered by its inherently hysteretic and highly nonlinear dynamics. This makes the modeling of MR dampers very important for its application. In order to characterize the performance of MR dampers, several models have been proposed to describe their behavior. These include the phenomenological model based on a BoucWen hysteresis model proposed by Spencer et al. 7, neural network model developed by Chang and Roschke 8,9, fuzzy model 10, nonlinear blackbox model 11, NARX model 12, viscoelasticplastic model 13, polynomial model 14 and other approaches 15. Among these MR models, phenomenological model and viscoelasticplastic model can accurately describe the behavior of the MR dampers, but the corresponding models for the inverse dynamics of the MR dampers are often difficult to obtain due to their nonlinear characteristics. Neural network and fuzzy models can be used to emulate the inverse dynamics of the MR dampers, but the selection of network structure and training data are essential in order to obtain accurate results. In fact, the polynomial model is a convenient and effective choice which can realize the inverse dynamics of the MR damper in an analytical form, and is easy to achieve the desirable damper force in an open-loop control system. A shortcoming of polynomial models is that it cannot characterize the MR behavior favorably at relatively low velocity region since the model does not include variables characterizing the pre-yield property of the damper force.In addition, theoretical and experimental researches have demonstrated that the performance of a semi-active control system is also highly dependent on the choice of control strategy 16. Therefore, some semi-active control schemes have been presented and compared in Ref. 17 and many other approaches, such as neuro-fuzzy control 18, and observer-based control 19, are also incorporated into the semi-active control.It can be concluded that the success of MR dampers in semi-active vehicle suspension applications is determined by two aspects: one is the accurate modeling of the MR dampers and the other is the selection of an appropriate control strategy. The latter is often related to the selection of a model for an MR damper. Based on previous research results, this paper is mainly concerned with semi-active static output feedback H1 control with MR dampers for vehicle suspension systems. A polynomial model is used here to model the MR damper using experimental data. Using this model, the damper force is mainly dependent upon the velocity of damper motion and input current. If the desired damper force is given and the damper motion velocity is measured, the input current can be calculated according to this model. Therefore, the desired damper force can be accurately tracked in the open-loop control system. In order to utilize this model sufficiently and to meet the three main performance requirements for advanced vehicle suspensions (ride comfort, road holding, and suspension deflection), an appropriate static output feedback H1 controller, which utilizes the measurable suspension deflection and sprung mass velocity as feedback signals, is designed to provide a trade-off between these requirements. A quarter-car suspension model is used here to study the performance of a vehicle suspension system in terms of the bouncing motion, the tyre deflection, and other performance features. The research of this paper is different from the recent research results 2,3 in that (a) a polynomial model is used, (b) a static output feedback H1 controller is designed to fit to this model and, (c) no closed-loop control system is required to make the actual damper force tracks the desired damper force. The performance of the presented scheme is further evaluated by computer simulation under random excitation in time domain. It is demonstrated via simulation results that the designed semi-active vehicle suspension can achieve good performance imitating that of active suspension.The rest of this paper is organized as follows. Section 2 presents a description of the experiment and modeling of the MR damper. A quarter-car suspension model and the formulation of the static output feedback H1 controller design problem are presented in Section 3. Section 4 presents the design results and performance evaluations. Conclusions are given in Section 5.II. MATHEMATICAL MODEL OF SEMI-ACTIVE SUSPENSION WITH MR DAMPERA. Structure of mathematical model of 1/4 semi-active suspensionTo study the semi-active control of the vehicle suspension, a two-DOF vehicle dynamic model established as shown in Fig.1. It adds a force represented by d f , which can be controlled on the basis of the passive suspension (fig.2 (a). It achieves an input of this force by MR damper.B. State equation of the mathematical modelBased on the semi-active suspension dynamic model, dynamic differential equations of semi-active suspension are written according to Newton second law:Take the system state vector ，Output vector , Input vector ，, and then its state equation is established as follows:III. DESIGN OF FUZZY CONTROLLERA. Selection of input and outputFuzzy control rules are the core of fuzzy logic controller. They are also the basis for decision-making. Considering the actual situation, in order to reduce vehicle body vibration and improve vehicle ride comfort, we want to decrease the value of the acceleration as small as possible and make it to zero better 4. So formulate fuzzy control rules according to it. In this paper, the difference between the response value represented by X2 and the expected value of vehicle body vertical acceleration and the rate of change of acceleration represented by dX2/dt are the input of the fuzzy logic controller. As the expected value of the body acceleration is 0, we can see error as itself. The error represented by e is the response value of acceleration. The rate of change of the acceleration is represented by ec . The adjustable damping force represented by u of the suspension is the output of the fuzzy logic controller. Both the input and the output are described by 7 kinds of fuzzy language, they are positive big (PB), positive middle (PM), positive small (PS), zero (ZE), negative small (NS), negative middle (NM), and negative big (NB). e , ec and u are represented as follow:e = NB，NM，NS，ZE，PS，PM，PB;ec = NB，NM，NS，ZE，PS，PM，PB;u = NB，NM，NS，ZE，PS，PM，PBB. Selection of fuzzy controller parametersThe quantization factor is a transformation factor, which transforms the theory of physical domain into the theory of fuzzy domain of the accurate value 5. The scale factor is also a transformation factor, which transforms the theory of fuzzy domain into the theory of physical domain of the accurate value. Operating rules of the fuzzy controller is shown in fig.2:The principle developed by the fuzzy controller is as follow. When the error is big, the value of control is selected to eliminate the error mainly. When the error is small, the value of control maintains the stability of the system mainly. The value of the quantization factor represented by ke and kec means the different extent of weights between errors and the rate of change of errors. The value of the scaling factor represented by keu affects the gain of the entire loop directly. It is important to select ke , kec and keu reasonably. According to some references, the basic domain of e , ec and u are set to -1.2, +1.2, -1.2, +1.2 and -900, 900 respectively. ke = 2.5 , kec = 2.5 , keu = 150 . The membership function obeys triangular distribution. Random fuzzy rules use the method of bisector. Fuzzy logic uses the method of mamdani. According to expertise experiences, the fuzzy rules are shown in Table1. Three-dimensional map of inputs and outputs is shown in fig.3.IV. SIMULATION EXPERIMENTSA. Simulation model of semi-active suspensionAccording to the 1/4 semi-active suspension model mentioned previously, using fuzzy logic controller, the simulations on the passive suspension and semi-active suspension system are completed by using matlab/simulink. The selection of simulation parameters is: 1 m =36 kg, 2 m =240 kg, 1 k =10000 N/m, 2 k =100000 N/m, c =200 N/m.The input model use the vehicle which drives on the road of level B with 20m/s. The model is shown in fig.4. This model set simulation for 5 seconds and sampling time for 0.01 seconds. The solver is variable ode45 solver.B. Simulation resultsThe simulation result in fig.6 shows that: the dotted line indicates passive suspension and the solid line indicates semi-active suspension controlled by fuzzy logic controller 6.Compared with passive suspension, the body vertical acceleration is significantly reduced in semi-active suspension by fuzzy control. First, the peak acceleration is reduced. Secondly, the vibration amplitude is slow and the body acceleration RMS under fuzzy control is reduced 35.2%, which can greatly improve the ride comfort. Fig.7 show that the suspension deflection reduced obviously; therefore, it reduces the impact of the vehicles and enhances the vehicle handling stability. In regard to the slightly diminution of the peak value of vehicle dynamic load also inhibits the pulse of tires in some extent. In a word, the semiactive suspension under fuzzy control improves the ride comfort and handling stability of vehicle.V. CONCLUSIONThis system uses the fuzzy control to enhance the vehicle running smoothness with semi-active suspension, and reduce the body vibration. It is obviously superior to the traditional passive suspension system.This system which using the Matlab fuzzy logic controller is easy to realize the fuzzy control, it can make the operation convenient, easy and intuitive.We can further establish a 7-DOF model of the whole vehicle suspension. We also can use more systematic and accurately fuzzy control strategy to study the suspension system.The fuzzy control only takes the limited control level, which limits its accuracy. Meanwhile, its stability is relatively weak. If it is used with other control methods, such as intelligent PID control, there will be more ideal effect.对使用MR阻尼器的车辆半自动悬挂的模糊控制分析摘要有一种合理的针对半主动悬挂系统的数学模型是建立在四分之一的汽车上。一种模糊控制器被提出，其规则被Matlab模糊逻辑工具箱所演绎。对于整个系统的仿真模拟可以通过Matlab或者Simulation来完成。相比于被动悬挂来说，半主动悬挂更依赖于给出的随机道路激励曲线。这表明通过对半主动悬挂系统使用模糊控制能很明显地减少车体在竖直方向上的加速度，同时也提升了行驶过程中的舒适度和稳定性。关键词：半主动悬挂；模糊控制；Matlab Simulink1介绍现在主要有3种车辆悬挂，被动，主动和半主动，在改善乘坐舒适度，车辆安全，减小道路损坏和总体车辆表现的方式上有所不同。通常来说，传统的被动悬挂只在特定的频率区间和非即时反馈的情况下使用。因此，在系统和操作的条件总是改变的情况下，被动悬挂的表现很不好。相反，主动悬挂可以在很大的频率区间上改善悬挂系统的表现，并且能以应力变化为基准主动适应系统的变化。所以，主动悬挂从上世纪60年代开始被广泛眼见，一些方法已经被广泛使用了。但是，主动悬挂系统通常需要很大的能源供给，这就是主要制约这项技术在实际运用中广泛使用的因素。从上世纪70年代开始，半主动悬挂系统开始得到广泛关注，因为它比被动悬挂的表现更好，并且消耗的能源要比主动悬挂少很多。特别是当可调节的阻尼器，例如电阻尼（ER）或者磁流变阻尼（MR），在实践中更容易得到之后，半主动悬挂在工程实现中变得前所未有的受欢迎。尤其是磁流变阻尼在在桥梁，直升机旋翼，桁架结构，悬挂座椅的震动减轻和地震还原，防震器方面都很受关注。使用磁流变阻尼器的半自动悬挂同样受到很多学者的研究。但是，磁流变阻尼控制的实际运用却被其固有的磁滞和高度非线性的动力结构所阻碍。这导致了在运用磁流变阻尼的时候，其模型至关重要。为了描述磁流变阻尼器的表现，我们使用了几种已有的模型。在这些磁流变模型中，现象学模型和粘滞塑料模型就能精确描述磁流变模型的行为，但是因为高非线性特性，磁流变阻尼的一致性模型很难得到。神经网络模型和模糊模型也可以被用来描述磁流变阻尼，但是为了得到精确的结果，神经网络结构的选择和训练数据十分重要。事实上，多项式模型也是一个很方便有效的选择，可以将磁流变阻尼转变成一种可以分析的模式，很容易就能得到想要的阻尼力。而且，理论和实验的研究同时说明了半主动控制系统同样高度依赖于控制策略的选择。因此，许多其他的方法，例如神经-模糊控制和以观测为基础的控制也在半主动控制中一起使用。磁流变阻尼在半主动车辆悬挂运用上的成功主要取决于两个方面：一个是对于磁流变阻尼的精确模型，另一个是选择适当的控制策略。后者通常与选择磁流变悬挂系统的描述模型相关联。在过去研究结论的基础上，本文主要关注使用磁流变的半主动悬挂系统的静态输出反馈。我们使用多项式模型来对使用实验数据的磁
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