【文章內(nèi)容簡(jiǎn)介】 車長(zhǎng)計(jì)算的算法；（3）利用松下FPWINGR PLC軟件編制控制程序，實(shí)現(xiàn)算法。由于時(shí)間有限，筆者尚未深入探討智能交通控制系統(tǒng)的進(jìn)一步的智能控制，但本設(shè)計(jì)對(duì)于緩解目前緊張的交通次序具有一定的實(shí)踐價(jià)值。四、致謝詞 轉(zhuǎn)瞬之間三個(gè)月的畢業(yè)設(shè)計(jì)即將結(jié)束，在這段日子里不但使我度過(guò)了最后一段大學(xué)生活的美好時(shí)光，領(lǐng)會(huì)到搞研究和開發(fā)不是件容易的事，不僅要很深的基本功，而且需要有全新的思維方式，從生活的點(diǎn)點(diǎn)滴滴中都可以給人得以啟發(fā)，就像在編程當(dāng)中遇到問題時(shí)編不下去，功能實(shí)現(xiàn)不了，但在偶然的情況下就明白了，就像阿基米德一樣在洗澡時(shí)往滿水的盆坐盆里的水都往外溢出，他突然明白溢出的水體積就是他在水里那部分的體積。這時(shí)心里有一種非常的成就感，想把畢業(yè)設(shè)計(jì)弄好，希望通過(guò)這次設(shè)計(jì)提高自己的能力，這是五年大學(xué)以來(lái)的學(xué)習(xí)作了一個(gè)最后的總結(jié)和歸納。在三個(gè)月的畢業(yè)設(shè)計(jì)里，通過(guò)自己對(duì)PLC及智能交通控制系統(tǒng)設(shè)計(jì)的學(xué)習(xí)和崔世鋼導(dǎo)師的悉心指導(dǎo)和耐心解答使得我對(duì)交通智能控制及PLC有了更深刻的了解，遇到疑難問題不是一味的逃避而是要追根問底。更使我感到在大學(xué)的學(xué)習(xí)專業(yè)基礎(chǔ)很重要，但在具體的實(shí)踐還要通過(guò)自己的不懈努力和探索。感謝崔世鋼老師的指導(dǎo)和幫助, 是老師的智慧和汗水，幫助我完成了整個(gè)畢業(yè)設(shè)計(jì)的工作。雖然他工作非常忙，但還是抽出很多時(shí)間來(lái)指導(dǎo)我的畢業(yè)設(shè)計(jì)，并為我解答問題。在此，對(duì)崔世鋼老師在設(shè)計(jì)的整個(gè)過(guò)程中的精心指導(dǎo)和幫助，致以衷心的感謝！參 考 文 獻(xiàn)[1]、王文貴 編 深圳智能交通技術(shù)應(yīng)用與研究文集 [M]北京 人民交通出版社 2000 [2]、 (意) 王武宏等編譯 智能車輛 智能交通系統(tǒng)的關(guān)鍵技術(shù) [M]北京 人民交通出版社 2002[3]、中國(guó)公路學(xué)會(huì)《交通工程手冊(cè)》編委會(huì) 交通工程手冊(cè) [M] 北京 人民交通出版社 1996[4]、劉智勇. 智能交通控制理論及其應(yīng)用 [M]. 北京: 科學(xué)出版社 2003[5]、 常斗南 李全利 張學(xué)武 可編程序控制器原理、應(yīng)用、實(shí)驗(yàn)[M]機(jī)械工業(yè)出版社 1998年[6]、朱善君　可編程序控制系統(tǒng)原理．應(yīng)用．維護(hù)[M]　清華大學(xué)出版社　1992年[7]、 劉敏 可編程序控制器技術(shù)[M]機(jī)械工業(yè)出版社 2000年[8]、 李乃夫 可編程序控制器原理、應(yīng)用、實(shí)驗(yàn)[M]中國(guó)輕工業(yè)出版社 1998年[9]、 萬(wàn)太福 可編程序控制器原理及應(yīng)用[M]重慶大學(xué)出版社 1994年[10]、 陳春雨 可編程序控制器應(yīng)用軟件設(shè)計(jì)方法與技巧[M]電子工業(yè)出版社 1992年附錄1：英文資料翻譯 Improving performanceAs it can be easily recognized, the algorithm outlined above consists of jumps from equilibrium point to equilibrium point, and as such is unlikely to provide satisfactory performance, in terms of the cost (2).However, performance may be restored by realizing that the available guidance policy may not only steer the vehicle from equilibrium state to equilibrium state, but from any state to an equilibrium state. This suggests introducing the following step: consider the tree at some point in time and a newly added milestone to the tree. A secondary milestone is defined to be any state of the system (continuous or hybrid) along the path leading from the parent node in the tree to the newly added milestone. Pick n ≥ such secondary milestones at random along that path. Because the vehicle is in motion along the path, these secondary milestones are likely to be at points in the state space that are far from the equilibrium manifold.These secondary milestones are added to the tree, and, as for all newly generated milestones, feasibility is checked for the resulting trajectory to the destination xf Moreover secondary milestones can be selected as the tree node to be expanded in later iterations. Note that all secondary milestones, by construction, have a primary milestone in a child subtree. Data structureThe roadmap is constructed as a tree, consisting of nudes and edges. At the tree nodes all the information concerning each milestone is stored, including: 1) The propagated state of the vehicle (. state , time ). 2) The cumulative cost and upper and lower bounds on the costtogo. The lower bound on the costtogo coincides with the value of the cost function , that is, it corresponds to the cost to go to the target state assuming the presence of no obstacle. The upper bound on the costtogo is initialized to +∞, meaning that a feasible path from the particular node has not been found yet. 3) A counter of the total number of milestones in the children trees. The (state time) couple is initialized through propagation of the system dynamics, and the cumulative cost is updated, according to eq.(2).At the tree edges, the following data are stored: 1)Information regarding the transitions between single milestones (namely, the parameters identifying the control law implemented in the transition, or the target equilibrium point)。 2) The incremental cost incurred during the transition, mainly for bookkeeping purposes。 again, this is done using the cost functional expressed in eq.(2). Realtime considerationsA significant issue arising from the usage of randomized algorithms for path planning is the distinct possibility of driving the system towards a deadend due to finite putation times. The notion of safety is introduced to prevent such situations to develop:Definition I (safety) A milestone is said to be safe if (x, t) is feasible for all.Primary milestones are added to the tree only if safe. If possible, and practical, primary milestones can be checked for the absence of collisions over an infinite horizon(). This is possible in many cases of interest (including all static environments), and results in hard safety guarantees on the resulting motion plan if the initial condition is itself safe. In cases in which safety cannot be ensured over an infinite time horizon,safety only ensures that the algorithm will always have at least r seconds to pute a new solution.Thesafety of the generated plan derives from the fact that all the primary milestones are by construction safe, and all secondary milestones have at least one primary milestones in their subtree. Maintaining safety guarantees in the face of finite putation times is particularly important, since the algorithm itself has no deterministic guarantees of success. In the sense outlined above the algorithm will always produce safe motion plans, even in the case in which a feasible trajectory to the target set has not been found. The time available for putation is bounded by either 0, or by the duration of the current trajectory segment. When the time is up, a new tree must be selected from the children of the current root. If there are none, since every primary milestone is τsafe, the system has at least τ seconds of guaranteed safety, available for puting a new tree (secondary milestones always have at least one child). If the current root has children, then two cases arise: At least one of the children leads to the destination through an already puted feasible solution. If there is more than one such feasible solution, the solution with the least, upper bound on the costtogo is chosen。 No feasible solutions have been puted yet. In this case, there is no clear indication of the best child to expl