【導(dǎo)讀】構(gòu)做了分析設(shè)計(jì)；對(duì)轎廂面積進(jìn)行計(jì)算；并對(duì)轎壁進(jìn)行了介紹。件的選擇，以及這些部件的定位方式和定位尺寸。接進(jìn)行了設(shè)計(jì)，通過(guò)鋼絲繩把曳引系統(tǒng)的動(dòng)力傳輸給轎廂，從而實(shí)現(xiàn)轎廂的運(yùn)動(dòng)。此外還對(duì)緩沖器進(jìn)行了選擇。在1949年至1979年的30年中，我國(guó)總共生產(chǎn)安裝電梯約1萬(wàn)臺(tái)。梯產(chǎn)品以低速直流梯和雙速梯為主，生產(chǎn)廠(chǎng)不超過(guò)10家。PLC和微電腦控制技術(shù)，最高梯速已達(dá)到4m/s；

　　

【正文】 限速器 的設(shè)計(jì) 限速器按動(dòng)作原理可分為擺錘式和離心式兩種。如圖 53 所示為 為本設(shè)計(jì)所用的 擺錘式限速器。轎廂在運(yùn)行時(shí)，通過(guò)限速器繩頭拉動(dòng)限速器繩，使限速器繩天津工業(yè)學(xué) 2021 屆本科畢業(yè)生畢業(yè)設(shè)計(jì) 28 輪和連在一起的 轉(zhuǎn)軸 同步轉(zhuǎn)動(dòng)。 拋球 由調(diào)節(jié)彈簧拉住。轉(zhuǎn)動(dòng)速度大，擺錘的擺動(dòng)幅度也大。當(dāng)轎廂運(yùn)行超速時(shí)，由于擺錘擺動(dòng)幅度加大， 拋球會(huì)拉動(dòng)鉛塊拉桿 ，鉛塊會(huì)加緊超速繩 ，由限速器繩頭和聯(lián)動(dòng)機(jī)構(gòu)將安全鉗拉動(dòng)，轎廂制停。擺錘 式限速器一般用于速度較低的電梯。 限速器工作原理：當(dāng)轎廂超速下降時(shí)，限速器輪 3 在限速器繩與其繩槽間的摩擦力的作用下，轉(zhuǎn)速加快。因而離心錘所受到的離心力相應(yīng)地也隨之增加，并使離心錘繞著銷(xiāo)軸轉(zhuǎn)動(dòng)，中心外移。當(dāng)離心力增大到一定值時(shí)離心錘上的內(nèi)凸子將和錘罩上的外凸子相嚙合，使錘罩帶動(dòng)偏心叉一起相著 轎廂下降方向轉(zhuǎn)動(dòng)。當(dāng)轉(zhuǎn)動(dòng)到偏心叉中的壓繩舌與限速器繩接觸時(shí)，根據(jù)自鎖原理， 鉗塊 4 將限速器摯停，進(jìn)而帶動(dòng)安全鉗動(dòng)作將轎廂加持于導(dǎo)軌上。 圖 53 為限速器工作原理 簡(jiǎn) 圖 轉(zhuǎn)軸彈簧 拋球 限速器輪 鉗塊 天津工業(yè)學(xué) 2021 屆本科畢業(yè)生畢業(yè)設(shè)計(jì) 29 第六章 結(jié) 論 此次畢業(yè)論文主要對(duì)一下幾個(gè)方面做了設(shè)計(jì)： （ 1） 對(duì)轎廂面積做了設(shè)計(jì)、計(jì)算 ；對(duì)導(dǎo)軌 進(jìn)行了選型；對(duì)補(bǔ)償裝置、對(duì)重進(jìn)行了計(jì)算；對(duì)門(mén)機(jī)進(jìn)行了設(shè)計(jì)，包括電機(jī)、同步帶輪、同步帶的選型。 （ 2）對(duì)曳引系統(tǒng)進(jìn)行了設(shè)計(jì)，包括曳引機(jī)功率計(jì)算、選型；聯(lián)軸器的選型；減速器的選型；制動(dòng)器的計(jì)算、選型；曳引輪和導(dǎo)向輪的設(shè)計(jì)，鋼絲繩的選擇及校核。 （ 3）對(duì)安全鉗、限速器、緩沖器進(jìn)行了選型； 天津工業(yè)學(xué) 2021 屆本科畢業(yè)生畢業(yè)設(shè)計(jì) 30 參考文獻(xiàn) [1] 夏 國(guó)柱 .電梯工程使用手冊(cè) [M].北京： 機(jī)械工業(yè)出版社 ， . [2] 陳家盛 .電梯 結(jié)構(gòu)原理及安裝維修 [M].北京： 機(jī)械工業(yè)出版社 ， 2021， 6. [3] 朱德文 .電梯使用保養(yǎng)和維修技術(shù) [M].北京： 中國(guó)電力出版社 ， 2021. [4] 朱德文 .電梯選型、配置與量化 [M].北京 :中國(guó)電力出版社 ,2021. [5] 李秧耕 .電梯基本原理及安裝維修全書(shū) [M]. 北京 :機(jī)械工業(yè)出版社 ,. [6] 楊蘭春 .電梯曳引機(jī)設(shè)計(jì)、安裝、維修 [M].北京：機(jī)械工業(yè)出版社， 2021： 6364. [7] 鄧星鐘 .機(jī)電傳動(dòng)控制 [M].武漢：華中理工大學(xué)出版社 ,1997. [8] 朱昌明，洪致育，張惠僑 .電梯與自動(dòng)扶梯 [M].上海：上海交通大學(xué)出版社， 1995. [9] 陳延偉 .王良順 .一種可靠的 VFFF電梯門(mén)機(jī)控制系統(tǒng) [J].中國(guó)電梯 ： 2021， 13（ 4） . [10] 蔣春玉 .張?jiān)?.電梯安裝與使用維修使用手冊(cè) [M].北京：機(jī)械工業(yè)出版社， 2021. [11] “Collective Computation in Neuronlike Circuit”。Scientific American。,。 .,by et al. [12] “Chips for the Niies and Beyond?。BYTE。.,by . 天津工業(yè)學(xué) 2021 屆本科畢業(yè)生畢業(yè)設(shè)計(jì) 31 附件 中英文翻譯 1. Introduction Interest in developing capable learning systems is increasing within the multiagent and AI research munities (., Weiss amp。 Sen, 1996). Learning enables systems to be more exible and robust, and it makes them better able to handle uncertainty and changing circumstances. This is especially important in multiagent systems, where the designers of such systems have often faced the extremely difficult task of trying to anticipate all possible contingencies and interactions among the agents ahead of time. Much the same could be said concerning the e nd of decentralized control, where policies for the control stations are developed from a global vantage point, and learning does not play a role. Even though executing the policies depends only on the information available at each control station, the policies are designed in a centralized way, with access to a plete description of the problem. Research has focused on what constitutes an optimal policy under a given information pattern but not on how such policies might be learned under the same constraints Reinforcement learning (RL) (Sutton amp。 Barto, 1998。 Bertsekas amp。 Tsitsiklis, 1996) applies naturally to the case of autonomous agents, which receive sensations as inputs and take actions that affect their environments in order to achieve their own goals. RL is based on the idea that the tendency to produce an action should be strengthened (reinforced) if it produces favorable results, and weak end if it produces favorable framework appealing from a biological point of view, since an animal has builtin preferences but does not always have a teacher to tell it exactly what action it should take in every situation. If the members of a group of agents each employ an RL algorithm, the resulting collective algorithm allows control policies to be learned in a decentralized way. Even in situations where centralized information is available, it may be advantageous to develop control polisci in a decentralized way in order to simplify the search through policy space. Although it may be possible to synthesize a system whose goals can be achieved by agents with conic objectives, this paper focuses on teams of agents that share identical objectives corresponding directly to the goals of the system as a whole. To demonstrate the power of multiagent RL, we focus on the difference problem of elevator group supervisory control. Elevator systems operate in highdimensional continuous state spaces and in continuous time as discrete event dynamic systems. Their states are not fully observable, and they are nonstationary due to changing 天津工業(yè)學(xué) 2021 屆本科畢業(yè)生畢業(yè)設(shè)計(jì) 32 passenger arrival rates. We use a team of RL agents, each of which is responsible for controlling one elevator car. Each agent uses article neural works to store its actionvalue estimates. We pare a parallel architecture, in which the agents share the same works, with a decentralized architecture, in which the agents have their own independent works. In either case, the team receives a global reward signal that is noisy from the perspective of each agent due in part to the effects of the actions of the other agents. Despite these difficulties, our system outperforms all of the heuristic elevator control algorithms known to us. We also analyze the policies learned by the agents, and show that learning is relatively robust even in the face of increasingly inplete state information. These results suggest that approaches to decentralized control using multiagent RL have considerable promise. In the following sections, we give some additional background on RL, introduce the elevator domain, describe in more detail the multiagent RL algorithm and work architecture we used, present and discuss our results, and finally draw some conclusions. 2. Reinforcement Learning Machine learning researchers have focused primarily on supervised learning, where a “ teacher” provides the learning system with a set of training examples in the form of inputoutput pairs. Supervised learning algorithms are useful in a wide variety of problems involving pattern classic and function approximation. However, there are many problems in which training examples are costly or even imposs