本文選題：輪轂電機電動汽車　切入點：駕駛員轉(zhuǎn)向特性　出處：《遼寧工業(yè)大學》2016年碩士論文　論文類型：學位論文

【摘要】：現(xiàn)如今,汽車正日益向電動化、智能化方向發(fā)展,四輪獨立驅(qū)動輪毅電機電動汽車作為汽車發(fā)展的重要方向,其控制已成為研究的熱點,但目前研究工作大多只針對汽車本身的底盤系統(tǒng),沒有考慮駕駛員特性。論文基于“智能四輪獨立驅(qū)動輪轂電機電動汽車自適應(yīng)轉(zhuǎn)向研究”這一國家自然科學基金青年基金項目(51305190),對考慮駕駛員轉(zhuǎn)向特性的四輪獨立驅(qū)動輪轂電動汽車控制進行研究。首先對駕駛員轉(zhuǎn)向特性分類方法進行研究,基于駕駛模擬器實驗,設(shè)計了具有多個直角轉(zhuǎn)彎實驗路況,選取具有一定駕駛經(jīng)驗的駕駛員,采集能夠反映駕駛員轉(zhuǎn)向特性的數(shù)據(jù),采用模糊C均值聚類方法將駕駛員轉(zhuǎn)向特征數(shù)據(jù)分為謹慎型、一般型和激進型3類。其次,在對駕駛員轉(zhuǎn)向特性合理分類的基礎(chǔ)上,利用BP神經(jīng)網(wǎng)絡(luò)建立駕駛員轉(zhuǎn)向特性辨識模型,選取駕駛員在轉(zhuǎn)彎時的特征值作為模型的輸入量,駕駛員轉(zhuǎn)向特性類型作為模型的輸出量,設(shè)計網(wǎng)絡(luò)結(jié)構(gòu),訓(xùn)練特性辨識模型,并對模型辨識準確度進行驗證。在此基礎(chǔ)上,通過駕駛模擬器實驗采集三類人的實驗數(shù)據(jù),采用RBF神經(jīng)網(wǎng)絡(luò)方法建立不同轉(zhuǎn)向特性的駕駛員轉(zhuǎn)向特性參考模型,設(shè)計網(wǎng)絡(luò)結(jié)構(gòu),訓(xùn)練參考模型,并對參考模型的預(yù)測精度和可行性進行驗證。然后研究通過四輪驅(qū)動力控制產(chǎn)生橫擺力矩滿足駕駛員喜好轉(zhuǎn)向特性的四輪驅(qū)動力控制方法�；谧赃m應(yīng)模糊控制理論,設(shè)計了橫擺力矩參數(shù)自調(diào)整模糊控制器,控制得出的附加橫擺力矩采用四輪驅(qū)動力規(guī)則分配方法。最后基于駕駛模擬器,將離線訓(xùn)練出的駕駛員轉(zhuǎn)向特性辨識模型和參考模型嵌入到駕駛模擬器整車模型系統(tǒng)中,選取不同類型的駕駛員在駕駛模擬器上進行實驗,對駕駛員轉(zhuǎn)向特性辨識的準確性、匹配參考模型后的四輪驅(qū)動力控制效果進行在線驗證。驗證結(jié)果表明：所研究的方法能夠?qū)崿F(xiàn)對駕駛員轉(zhuǎn)向特性的準確辨識、自動匹配轉(zhuǎn)向特性參考模型,通過四輪驅(qū)動力矩控制實現(xiàn)了滿足駕駛員喜好轉(zhuǎn)向特性的電動汽車自適應(yīng)轉(zhuǎn)向控制。研究成果可為電動汽車智能控制提供理論基礎(chǔ)和技術(shù)支持。
[Abstract]:Nowadays, the automobile is becoming more and more electric and intelligent. As an important direction of automobile development, the control of four-wheel independent drive wheel motor electric vehicle has become a hot spot. But at present, most of the research work only focuses on the chassis system of the car itself. Based on the research of adaptive steering of intelligent four-wheel independent drive hub motor electric vehicle, a project of National Natural Science Foundation Youth Foundation No. 51305190, this paper is concerned with the four-wheel one-wheel system considering the steering characteristics of the driver. The control of vertical drive hub electric vehicle is studied. Firstly, the classification method of driver steering characteristics is studied. Based on the driving simulator experiment, the road conditions with several right-angle turning experiments are designed, and the drivers with certain driving experience are selected to collect the data which can reflect the driver's steering characteristics. Fuzzy C-means clustering method is used to classify driver steering characteristic data into three categories: cautious type, general type and radical type. Secondly, on the basis of reasonable classification of driver's steering characteristics, BP neural network is used to establish the driver steering characteristic identification model. The driver's characteristic value is selected as the input of the model, the driver's steering characteristic type is taken as the output quantity of the model, the network structure is designed, and the training characteristic identification model is designed. The accuracy of model identification is verified. On the basis of this, the experimental data of three kinds of people are collected by driving simulator experiment, and the reference model of driver steering characteristic with different steering characteristics is established by using RBF neural network method, and the network structure is designed. Training reference model, The prediction accuracy and feasibility of the reference model are verified. Then the four-wheel driving force control method, which can produce yaw torque to satisfy the driver's preference steering characteristic, is studied based on the adaptive fuzzy control theory. The self-adjusting fuzzy controller of yaw torque parameters is designed. The additional yaw torque obtained by the control is assigned by four-wheel driving force rule. Finally, based on the driving simulator, The off-line driver steering characteristic identification model and reference model are embedded in the whole vehicle model system of driving simulator. Different types of drivers are selected to carry out experiments on the driving simulator, and the accuracy of driver steering characteristic identification is obtained. The control effect of four-wheel driving force after matching reference model is verified online. The results show that the proposed method can accurately identify the steering characteristics of the driver and automatically match the steering characteristic reference model. The adaptive steering control of electric vehicle can be realized by four-wheel drive torque control, which meets the steering characteristics of driver preference. The research results can provide theoretical basis and technical support for intelligent control of electric vehicle.
【學位授予單位】：遼寧工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：U469.72

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