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Design Of Vehicle Suspention System Based On Sky-Hook Controlalgorithm

Posted on:2017-02-03Degree:MasterType:Thesis
Country:ChinaCandidate:Y ZhangFull Text:PDF
GTID:2322330503478316Subject:Control Engineering
Abstract/Summary:PDF Full Text Request
People care about two important aspects of vehicle running safety and ride comfort driving when driving a car, and the two vehicles performance is mainly influenced by vehicle driving stability, in the process of how to improve or improve vehicle stability is the current hot issue in auto industry.Vehicle suspension system is generally divided into passive suspension, semi-active suspension and active suspension form of three kinds of suspension, active suspension system and semi-active suspension system is on the basis of the passive suspension system adds a control actuators.Passive suspension system need set the design of the parameters of the system in advance.It can only rely on their own in the process of vehicles running on the adjusted damping, the adjusting range is limited,It can only meet in the relatively flat road.The semi-active suspension and active suspension system can be in the process of vehicle traffic on the system parameters are adjusted in real-time. The semi-active suspension system compared to the active suspension system has simple structure, low cost advantages, but in the process of vehicle real-time adjustment ability are relatively weak in active suspension system.This system combine the advantages of using both the appeal is based on the semi-active suspension control is given priority to, in the form of active suspension is complementary to meet the requirement of vehicle stability.With his internship programs in the company, this paper aimed at improving in the process of vehicle driving stability and driving safety indicators, based on the theory of ceiling control method,established the magneto-rheological damper semi-active suspension and active suspension hardware platform of DC servo motor.And through RBF neural network method is used for servo motor temperature prediction research, finally on the real vehicle experiment platform experiment analyzed the passive suspension, semi-active suspension and active suspension system of ride comfort.The specific research contents and main innovations include the following aspects:1. Summarizes the suspension system control methods commonly used at home and abroad,and introduces in detail the principle of sky-hook control method and magneto-rheological fluid rheological properties are analyzed.On the bench test platform on damping properties of magneto-rheological damper and mechanical properties were studied. Established a four degrees of freedom a half vehicle model and two degree of freedom quarter car model of test vehicle, finally combining ceiling control algorithm in the application of two degree of freedom quarter vehicle model to study the passive damping coefficient and damping coefficient of the sky-hook effect the performance of the vehicle suspension system control.2. The magneto-rheological damper and servo motor is applied to the suspension, and then in view of the car body mass center position and the driver seat position change of the acceleration and displacement to carry on the real vehicle road test, check whether the vehicle safety and driving comfort the run time performance is improved.For active control of servo electrical opportunity frequent start and stop, may lead to motor burn down and active control part of losing this problem,in this paper, a RBF neural network to predict the motor temperature rise parameter method, and a simulation experiment was carried out.3. Through the magneto-rheological damper and permanent magnet servo motor applied in based on the semi-active control algorithm ceiling/active suspension system, the hardware platform and software design, software after written by industrial PC software interface display vehicles on the road in the process of the change of various operation parameters.
Keywords/Search Tags:sky-hook control, magneto-rheological damper, two degrees of freedom, RBF neural network
PDF Full Text Request
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