本文關(guān)鍵詞： 車輛工程 底盤集成控制 駕駛?cè)耸褂媚Ｊ?仿生智能算法 多變量頻域控制　出處：《吉林大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：汽車底盤集成控制以整車性能最優(yōu)為目標(biāo),能夠綜合協(xié)調(diào)汽車底盤各動力學(xué)子系統(tǒng),已經(jīng)成為現(xiàn)今車輛動力學(xué)研究領(lǐng)域的熱點(diǎn)問題。然而,當(dāng)前底盤集成控制系統(tǒng)大多基于車輛動力學(xué)特性進(jìn)行均一設(shè)計(jì),忽略了駕駛?cè)藢刂葡到y(tǒng)的作用。事實(shí)上,駕駛?cè)伺c主動控制系統(tǒng)對車輛的作用具有強(qiáng)烈的耦合關(guān)系,因此,汽車底盤集成控制系統(tǒng)設(shè)計(jì)過程中應(yīng)考慮駕駛?cè)颂匦缘挠绊�。本文依托國家自然科學(xué)基金(項(xiàng)目編號51105169、51475206)等項(xiàng)目,開展了基于駕駛?cè)耸褂媚Ｊ降钠嚨妆P集成控制研究,設(shè)計(jì)并搭建了駕駛?cè)耸褂媚Ｊ綌?shù)據(jù)采集系統(tǒng),進(jìn)行了典型工況下的駕駛?cè)耸褂媚Ｊ綌?shù)據(jù)采集;引入仿生智能算法對駕駛?cè)耸褂媚Ｊ竭M(jìn)行了辨識;在此基礎(chǔ)上,基于改進(jìn)多變量頻域控制算法建立了基于駕駛?cè)耸褂媚Ｊ降钠嚨妆P集成控制策略,并選取典型工況利用MATLAB/Simulink與Car Sim聯(lián)合仿真平臺進(jìn)行了驗(yàn)證分析。本文的主要研究內(nèi)容包括:(1)駕駛?cè)耸褂媚Ｊ綌?shù)據(jù)采集為了進(jìn)行駕駛?cè)耸褂媚Ｊ降姆治�、識別和量化,首先基于d SPACE DS1006實(shí)時(shí)仿真平臺設(shè)計(jì)搭建了駕駛?cè)耸褂媚Ｊ綌?shù)據(jù)采集系統(tǒng)。利用所搭建的系統(tǒng)對典型工況下的駕駛?cè)耸褂媚Ｊ綌?shù)據(jù)進(jìn)行了采集,所采集數(shù)據(jù)包括駕駛?cè)朔较虮P轉(zhuǎn)角、油門踏板和制動踏板開度以及車輛狀態(tài)信息等。(2)基于仿生智能算法的駕駛?cè)耸褂媚Ｊ奖孀R引入駕駛?cè)祟A(yù)瞄最優(yōu)曲率模型來描述駕駛?cè)耸褂媚Ｊ?通過預(yù)瞄時(shí)間、神經(jīng)延遲時(shí)間和肌肉慣性延遲時(shí)間來表征不同類型駕駛?cè)耸褂媚Ｊ降牟町?在此基礎(chǔ)上,分別采用改進(jìn)粒子群算法和BP神經(jīng)網(wǎng)絡(luò)算法設(shè)計(jì)了駕駛?cè)耸褂媚Ｊ诫x線和快速在線辨識策略,并對辨識結(jié)果進(jìn)行了分析驗(yàn)證。(3)基于駕駛?cè)耸褂媚Ｊ降钠嚨妆P集成控制策略設(shè)計(jì)提出了基于駕駛?cè)耸褂媚Ｊ降钠嚨妆P集成控制策略架構(gòu)。通過對駕駛?cè)耸褂媚Ｊ降姆治觥⒆R別、量化和建模,得到預(yù)測的駕駛?cè)朔较虮P轉(zhuǎn)角,將預(yù)測方向盤轉(zhuǎn)角輸入給二自由度車輛參考模型,從而建立基于駕駛?cè)耸褂媚Ｊ降目刂葡到y(tǒng)參考模型。分析了主動轉(zhuǎn)向和基于主動制動的電子穩(wěn)定性控制之間的干涉和耦合,采用考慮了車速變化和輪胎側(cè)偏剛度非線性的改進(jìn)逆奈奎斯特陣列法(Inverse Nyquist Array method,INA)進(jìn)行了集成控制器的設(shè)計(jì),有效消除了兩種主動控制系統(tǒng)之間的耦合。(4)控制策略驗(yàn)證搭建了MATLAB/Simulink與Car Sim聯(lián)合仿真平臺,選取雙移線和蛇形兩種工況,在不同車速、不同路面附著系數(shù)的情況下對控制策略進(jìn)行了仿真測試。結(jié)果表明,所提出的控制策略能夠有效提高車輛的軌跡跟隨能力,有效提高車輛的操縱穩(wěn)定性能,有效減輕駕駛?cè)说牟僮髫?fù)荷。
[Abstract]:The vehicle chassis integrated control, which aims at the optimal performance of the whole vehicle, can comprehensively coordinate the dynamic subsystems of the vehicle chassis, which has become a hot issue in the field of vehicle dynamics research. At present, the chassis integrated control system is mostly based on the vehicle dynamics characteristics for uniform design, ignoring the role of the driver on the control system. The driver and the active control system have the strong coupling relation to the vehicle function, therefore. The influence of driver's characteristics should be considered in the design of automobile chassis integrated control system. This paper relies on the National Natural Science Foundation of China (project number 51105169 / 51475206) and so on. The integrated control research of automobile chassis based on driver usage mode is carried out, and the data acquisition system of driver usage mode is designed and built, and the data collection of driver usage mode is carried out under typical operating conditions. The bionic intelligent algorithm is introduced to identify the driving mode. On this basis, a vehicle chassis integrated control strategy based on driving mode is established based on the improved multivariable frequency domain control algorithm. At the same time, the typical working conditions are selected and verified by the joint simulation platform of MATLAB/Simulink and Car Sim. The main research contents of this paper include: 1). The driver uses the pattern data collection to carry on the driver to use the pattern analysis. Identification and quantification. First based on d SPACE. DS1006 real-time simulation platform designed and built the driver use mode data acquisition system, using the built system to the typical operating conditions of the driver use mode data collection. The data collected include the steering wheel angle of the driver. Throttle pedal and brake pedal opening degree and vehicle state information etc.) based on the bionic intelligent algorithm driver use pattern identification introduces the driver preview optimal curvature model to describe the driver use mode. Preview time, nerve delay time and muscle inertia delay time were used to characterize the difference of different drivers' usage patterns. Improved particle swarm optimization algorithm and BP neural network algorithm are used to design off-line and fast on-line identification strategies for drivers. The identification results are analyzed and verified. The design of vehicle chassis integrated control strategy based on driver usage mode. The framework of vehicle chassis integrated control strategy based on driver usage mode is proposed. The predicted steering wheel angle is obtained by identifying, quantifying and modeling, and the predicted steering wheel angle is input to the two-degree-of-freedom vehicle reference model. The reference model of control system based on driving mode is established, and the interference and coupling between active steering and electronic stability control based on active braking are analyzed. An improved inverse Nyquist Array method method, which takes into account the variation of vehicle speed and the nonlinearity of tire lateral stiffness, is used to improve the inverse Nyquist array method. INA) is used to design the integrated controller. The coupling between two active control systems is eliminated effectively. The verification of control strategy builds a joint simulation platform of MATLAB/Simulink and Car Sim. The control strategy is simulated under the conditions of different speed and different road adhesion coefficient under two working conditions of double moving line and snake shape. The results show that. The proposed control strategy can effectively improve the tracking ability of the vehicle, improve the handling and stability of the vehicle, and effectively reduce the operating load of the driver.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U463.1

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