【摘要】：論文題目：多自主車輛系統(tǒng)安全控制與分布式優(yōu)化策略研究 多自主車輛系統(tǒng)因其能夠釋放繁雜交通工作對人的束縛，為交通路網(wǎng)提供及時(shí)的服務(wù)，成為近年來智能交通領(lǐng)域研究的熱點(diǎn)之一。本文以多自主車輛系統(tǒng)控制與優(yōu)化策略為切入點(diǎn)，通過對多自主車輛系統(tǒng)建模分析，著重研究了自主車輛在典型路況的安全控制策略、分布式環(huán)境中的優(yōu)化配置以及多自主車輛系統(tǒng)的任務(wù)分配方法。具體如下： 1)建立了多自主車輛系統(tǒng)模型，設(shè)計(jì)濾波器估計(jì)自主車輛運(yùn)動(dòng)狀態(tài)。分別對自主車輛縱向動(dòng)力學(xué)、操縱動(dòng)力學(xué)和多自主車輛系統(tǒng)進(jìn)行建模，分析了風(fēng)阻對自主車縱向車速的影響，從操縱穩(wěn)定性角度出發(fā)，分析自主車設(shè)計(jì)合理性，不同頻率響應(yīng)下的穩(wěn)定性等，通過混合自動(dòng)機(jī)模型描述多自主車輛系統(tǒng)協(xié)作模式。設(shè)計(jì)卡爾曼濾波器估計(jì)自主車運(yùn)動(dòng)狀態(tài)。仿真結(jié)果驗(yàn)證了所建模型的合理性。 2)研究了針對典型交通路況的柔性跟馳、路口高效協(xié)作安全控制策略。考慮急剎給車輛帶來的損傷，通過引入以自然指數(shù)函數(shù)為原型的安全控制策略模型，解決了跟馳安全控制問題。針對T形路口安全問題，提出安全系數(shù)估計(jì)方法，結(jié)合人工勢場思想，，估計(jì)車輛期望加速度、速度，利用改進(jìn)的增量型數(shù)字PI控制器實(shí)現(xiàn)自主車縱向車速的準(zhǔn)確控制，實(shí)現(xiàn)自主車輛協(xié)作避碰。仿真結(jié)果驗(yàn)證了跟馳安全控制策略和采用安全系數(shù)估計(jì)自主車輛T形路口協(xié)作避碰規(guī)劃的可行性與有效性。 3)解決了交通路網(wǎng)中多自主車輛系統(tǒng)通過優(yōu)化配置、任務(wù)規(guī)劃滿足指定覆蓋率、減少平均時(shí)間代價(jià)的問題。利用先“靜”后“動(dòng)”的思想，通過模擬退火算法求解數(shù)量不同的自主車能夠達(dá)到的靜態(tài)優(yōu)化覆蓋率，進(jìn)而求得滿足指定覆蓋率的自主車配置數(shù)量。以此為依據(jù)，進(jìn)一步求解動(dòng)態(tài)情況下滿足指定覆蓋率的自主車配置數(shù)量。對任務(wù)分配問題，給出基于BDI模型的決策模塊結(jié)構(gòu)，利用Hungary算法給出任務(wù)分配方案。仿真結(jié)果通過覆蓋率統(tǒng)計(jì)值證明了優(yōu)化配置的可行性，通過平均到達(dá)比例、完成率估計(jì)值、平均時(shí)間代價(jià)證明任務(wù)分配的有效性。 4)設(shè)計(jì)多自主車輛仿真實(shí)驗(yàn)平臺(tái)，分析系統(tǒng)模塊性能。搭建多自主車輛系統(tǒng)仿真平臺(tái)，給出多自主車輛系統(tǒng)結(jié)構(gòu)。對平臺(tái)上的室內(nèi)定位子系統(tǒng)模塊進(jìn)行了準(zhǔn)確性測試。在一組合理的參數(shù)配置下，對定位系統(tǒng)性能進(jìn)行了分析。通過軟件實(shí)驗(yàn)平臺(tái)對通信子系統(tǒng)進(jìn)行了組網(wǎng)，實(shí)現(xiàn)對自主車輛位置信息的發(fā)送與控制。 綜上所述，本文就多自主車輛系統(tǒng)的安全控制和分布式優(yōu)化策略進(jìn)行了相對完整的理論研究，主要目的是實(shí)現(xiàn)自主車輛的安全控制與多自主車輛系統(tǒng)的分布式優(yōu)化，通過仿真實(shí)驗(yàn)進(jìn)行了相應(yīng)的驗(yàn)證與分析，并搭建仿真實(shí)驗(yàn)平臺(tái)，分析系統(tǒng)性能。
[Abstract]:Topic: multi autonomous vehicle system security control and distributed optimization strategy research
The multi autonomous vehicle system has become one of the hotspots in the field of Intelligent Transportation Research in recent years because of its ability to release the bondage of complex traffic work and provide timely service to the traffic network. This paper focuses on the multi autonomous vehicle system control and optimization strategy, and focuses on the research of autonomous vehicles through the modeling and analysis of the multi autonomous vehicle system. In the typical road safety control strategy, the optimal allocation in the distributed environment and the task allocation method of the multi autonomous vehicle system are as follows:
1) the multi autonomous vehicle system model is established and the filter is designed to estimate the motion state of the autonomous vehicle. The longitudinal dynamics, the control dynamics and the multi autonomous vehicle system are modeled respectively. The influence of the wind resistance on the longitudinal vehicle speed is analyzed. From the angle of control stability, the design rationality and the different frequency of the autonomous vehicle are analyzed. In response to the stability, a hybrid automaton model is used to describe the cooperative model of the multi autonomous vehicle system. The Calman filter is designed to estimate the motion state of the autonomous vehicle. The simulation results verify the reasonableness of the model.
2) the efficient cooperative safety control strategy for the intersection of typical traffic conditions is studied. Considering the damage caused by emergency brake, the safety control problem is solved by introducing the model of natural exponential function as the prototype of safety control strategy, and the safety factor estimation method is proposed for the safety problem of the T intersection. The idea of the potential field is used to estimate the desired acceleration and speed of the vehicle. The improved incremental digital PI controller is used to realize the accurate control of the longitudinal speed of the autonomous vehicle and realize the cooperative collision avoidance of the autonomous vehicle. The simulation results verify the feasibility and effectiveness of the following safety control strategy and the use of the safety factor to estimate the cooperative collision avoidance planning for the T intersection of the autonomous vehicle. Sex.
3) to solve the problem that the multi autonomous vehicle system in the traffic network is optimized through the optimization configuration, the task planning satisfies the specified coverage rate and reduces the average time cost. By using the idea of "static" after the "motion", the simulated annealing algorithm is used to solve the static optimal coverage rate that the number of different autonomous vehicles can reach, and then to meet the specified coverage rate. On the basis of this, we further solve the number of autonomous vehicles that meet the specified coverage in the dynamic situation. The decision module structure based on the BDI model is given to the task allocation problem, and the task allocation scheme is given by the Hungary algorithm. The simulation results prove the feasibility of the optimized configuration through the coverage rate statistics. Over average arrival ratio, completion rate estimate and average time cost prove the effectiveness of task allocation.
4) design the multi autonomous vehicle simulation experiment platform, analyze the performance of the system module, build the multi autonomous vehicle system simulation platform, give the multi autonomous vehicle system structure. The accuracy test of the indoor positioning subsystem module on the platform is carried out. The performance of the positioning system is analyzed under a set of reasonable parameters configuration. The software experiment is carried out through the software experiment. The communication network is organized by the platform to realize the transmission and control of the location information of the autonomous vehicle.
To sum up, this paper makes a relatively complete theoretical study on the security control and distributed optimization strategy of the multi autonomous vehicle system. The main purpose is to realize the safety control of the autonomous vehicle and the distributed optimization of the multi autonomous vehicle system. The corresponding verification and analysis are carried out through the simulation experiment, and the simulation experiment platform and the analysis system are set up. Unified performance.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495

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