基于模糊PID控制的車輛縱向優(yōu)化CACC系統(tǒng)
發(fā)布時間：2018-10-29 19:25
【摘要】：隨著智能交通系統(tǒng)ITS（Intelligent Transport System）框架的提出，汽車協(xié)同式自適應巡航控制系統(tǒng)CACC（CooperativeAdaptive Cruise Control）作為其重要的組成部分，在已經(jīng)大范圍普及的定速巡航控制CC（Cruise Control）系統(tǒng)和自適應巡航控制ACC（Adaptive Cruise Control）系統(tǒng)基礎上，借助專用短程通信技術DSRC（Dedicated ShortRange Communications），實現(xiàn)了車與車之間的數(shù)據(jù)通信。通過與其他車輛進行實時的信息共享，獲得本車和周圍車輛的各項行駛數(shù)據(jù)，感知當前行駛狀態(tài)，并針對不同的狀態(tài)選擇最適宜的駕駛行為來操縱汽車。相比于其他方法，CACC系統(tǒng)使車輛更加智能化，對車輛控制更加合理、準確，對道路突發(fā)狀況響應迅速，從而能夠大幅度提高交通系統(tǒng)的綜合通行效率，并增強車上乘客的安全性和舒適性。 本文設計了一種基于模糊PID控制的車輛縱向優(yōu)化CACC控制系統(tǒng)。該系統(tǒng)能夠?qū)崿F(xiàn)在DSRC車車通信環(huán)境下，對于本車的縱向跟隨控制。針對CACC系統(tǒng)中的兩個主要功能——車車通信和本車控制，該系統(tǒng)結構可分為兩層。第一層為感知層，主要負責接收通過DSRC端獲得的周圍車輛的行駛數(shù)據(jù)和本車雷達端獲得的與前車距離數(shù)據(jù)。考慮到CACC系統(tǒng)中對數(shù)據(jù)的精度和實時性要求較高，而通常由車上傳感器獲得的數(shù)據(jù)精度偏低，并伴有一定延時，因此通過DSRC通信得到的其他車輛數(shù)據(jù)是無法直接使用的，所以本文創(chuàng)新地提出了一種加速度補償平滑方法ACS（Acceleration CompensationSmoothing），通過設計一個2階IIR濾波器和加速度補償公式來對DSRC通信端接收到的數(shù)據(jù)進行優(yōu)化，使其能夠適應CACC系統(tǒng)要求，，并去掉DSRC通信中一些冗余數(shù)據(jù)，僅保留周圍車輛運動學參數(shù)并與雷達端的距離數(shù)據(jù)整合，生成當前時刻狀態(tài)模型。第二層為控制層，根據(jù)模糊PID控制理論，設計出一個針對車輛縱向行駛的CACC控制器，該控制器將感知層傳來的狀態(tài)模型作為輸入，將兩車速度參數(shù)作為主要計算量，其他參數(shù)作為輔助調(diào)節(jié)量，通過模糊控制動態(tài)調(diào)整PID調(diào)節(jié)器的各個參數(shù)，從而根據(jù)不同的行駛狀態(tài)得到相應時刻優(yōu)化的車輛的控制量（油門踏板或制動踏板的值），并輸出到車輛動力學模型的執(zhí)行機構對車輛進行控制，相比傳統(tǒng)控制方法，有很強的適應性和穩(wěn)定性。 在Matlab/Simulink下建立相應的仿真程序，運用Matlab Filter工具箱和Fuzzy Logic工具箱等設計出符合CACC系統(tǒng)要求的通信模塊、控制模塊和車輛動力學模塊，并借助Logitech G27方向盤搭建駕駛員模擬平臺，進行仿真試驗。試驗結果表明，本文提出的感知層ACS方法能夠提高DSRC通信接收端得到數(shù)據(jù)的精度和平滑性，該CACC模糊PID控制器可以在各種典型縱向工況中得到最優(yōu)的數(shù)據(jù)輸入，對車輛進行優(yōu)化跟隨控制，并能保證安全性和舒適性。
[Abstract]:With the development of intelligent transportation system (ITS (Intelligent Transport System) framework, CACC (CooperativeAdaptive Cruise Control) is an important part of automobile cooperative adaptive cruise control system. On the basis of CC (Cruise Control) system and adaptive cruise control ACC (Adaptive Cruise Control) system, which has been widely used, the data communication between vehicle and vehicle is realized by means of special short range communication technology (DSRC (Dedicated ShortRange Communications),). Through real-time information sharing with other vehicles, we can obtain the driving data of the vehicle and its surrounding vehicles, perceive the current driving state, and select the most suitable driving behavior to operate the vehicle according to the different states. Compared with other methods, CACC system can make vehicles more intelligent, more reasonable and accurate to control vehicles, and respond quickly to road emergencies, which can greatly improve the comprehensive traffic efficiency of traffic system. And enhance the safety and comfort of passengers on board. A vehicle longitudinal optimization CACC control system based on fuzzy PID control is designed in this paper. The system can realize the longitudinal following control of the vehicle under the DSRC vehicle communication environment. The system structure can be divided into two layers according to the two main functions of CACC system, that is, vehicle communication and local vehicle control. The first layer is the perceptual layer, which is mainly responsible for receiving the driving data of the surrounding vehicles obtained through the DSRC terminal and the distance data from the radar end of the vehicle to the front car. Considering the high requirement of precision and real time of data in CACC system, but the data precision obtained by vehicle sensor is on the low side and accompanied by certain delay, so other vehicle data obtained by DSRC communication can not be used directly. So this paper proposes an acceleration compensation smoothing method, ACS (Acceleration CompensationSmoothing), which optimizes the data received by DSRC communication terminal by designing a second-order IIR filter and acceleration compensation formula, so that it can meet the requirements of CACC system. Some redundant data in DSRC communication are removed and only the kinematics parameters of the surrounding vehicle are retained and integrated with the range data of the radar terminal to generate the current state model. The second layer is the control layer. According to the fuzzy PID control theory, a CACC controller is designed for the vehicle running longitudinally. The controller takes the state model from the perceptual layer as the input and the two vehicle speed parameters as the main calculation amount. The other parameters are used as auxiliary regulation, and the parameters of the PID regulator are dynamically adjusted by fuzzy control, so as to obtain the optimal control quantity of the vehicle (the value of the throttle pedal or brake pedal) according to the different driving conditions. The actuator output to the vehicle dynamics model controls the vehicle, which has strong adaptability and stability compared with the traditional control method. The corresponding simulation program is established under Matlab/Simulink. The communication module, control module and vehicle dynamics module are designed by using Matlab Filter toolbox and Fuzzy Logic toolbox, and the driver simulation platform is built with the help of Logitech G27 steering wheel. The simulation test was carried out. The experimental results show that the proposed perceptron layer ACS method can improve the accuracy and smoothness of the data obtained from the DSRC communication receiver, and the CACC fuzzy PID controller can obtain the optimal data input in various typical longitudinal conditions. The vehicle is optimized to follow control, and the safety and comfort can be guaranteed.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U463.6;U495;TP273.4
本文編號：2298583
[Abstract]:With the development of intelligent transportation system (ITS (Intelligent Transport System) framework, CACC (CooperativeAdaptive Cruise Control) is an important part of automobile cooperative adaptive cruise control system. On the basis of CC (Cruise Control) system and adaptive cruise control ACC (Adaptive Cruise Control) system, which has been widely used, the data communication between vehicle and vehicle is realized by means of special short range communication technology (DSRC (Dedicated ShortRange Communications),). Through real-time information sharing with other vehicles, we can obtain the driving data of the vehicle and its surrounding vehicles, perceive the current driving state, and select the most suitable driving behavior to operate the vehicle according to the different states. Compared with other methods, CACC system can make vehicles more intelligent, more reasonable and accurate to control vehicles, and respond quickly to road emergencies, which can greatly improve the comprehensive traffic efficiency of traffic system. And enhance the safety and comfort of passengers on board. A vehicle longitudinal optimization CACC control system based on fuzzy PID control is designed in this paper. The system can realize the longitudinal following control of the vehicle under the DSRC vehicle communication environment. The system structure can be divided into two layers according to the two main functions of CACC system, that is, vehicle communication and local vehicle control. The first layer is the perceptual layer, which is mainly responsible for receiving the driving data of the surrounding vehicles obtained through the DSRC terminal and the distance data from the radar end of the vehicle to the front car. Considering the high requirement of precision and real time of data in CACC system, but the data precision obtained by vehicle sensor is on the low side and accompanied by certain delay, so other vehicle data obtained by DSRC communication can not be used directly. So this paper proposes an acceleration compensation smoothing method, ACS (Acceleration CompensationSmoothing), which optimizes the data received by DSRC communication terminal by designing a second-order IIR filter and acceleration compensation formula, so that it can meet the requirements of CACC system. Some redundant data in DSRC communication are removed and only the kinematics parameters of the surrounding vehicle are retained and integrated with the range data of the radar terminal to generate the current state model. The second layer is the control layer. According to the fuzzy PID control theory, a CACC controller is designed for the vehicle running longitudinally. The controller takes the state model from the perceptual layer as the input and the two vehicle speed parameters as the main calculation amount. The other parameters are used as auxiliary regulation, and the parameters of the PID regulator are dynamically adjusted by fuzzy control, so as to obtain the optimal control quantity of the vehicle (the value of the throttle pedal or brake pedal) according to the different driving conditions. The actuator output to the vehicle dynamics model controls the vehicle, which has strong adaptability and stability compared with the traditional control method. The corresponding simulation program is established under Matlab/Simulink. The communication module, control module and vehicle dynamics module are designed by using Matlab Filter toolbox and Fuzzy Logic toolbox, and the driver simulation platform is built with the help of Logitech G27 steering wheel. The simulation test was carried out. The experimental results show that the proposed perceptron layer ACS method can improve the accuracy and smoothness of the data obtained from the DSRC communication receiver, and the CACC fuzzy PID controller can obtain the optimal data input in various typical longitudinal conditions. The vehicle is optimized to follow control, and the safety and comfort can be guaranteed.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U463.6;U495;TP273.4
【參考文獻】
相關期刊論文 前3條
1 WANG Pangwei;WANG Yunpeng;YU Guizhen;TANG Tieqiao;;An Improved Cooperative Adaptive Cruise Control(CACC) Algorithm Considering Invalid Communication[J];Chinese Journal of Mechanical Engineering;2014年03期
2 楊守衛(wèi);;FIR數(shù)字濾波器應用分析探討[J];機電信息;2011年15期
3 謝露;鞠浩然;張舵;;淺析濾波器技術與應用[J];工業(yè)設計;2011年05期
本文編號：2298583
本文鏈接：http://sikaile.net/kejilunwen/daoluqiaoliang/2298583.html
