关于PLC控制系统设计的外文翻译

关于PLC控制系统 设计 领导形象设计圆作业设计ao工艺污水处理厂设计附属工程施工组织设计清扫机器人结构设计 的外文翻译 本科毕业设计(论文)外文翻译 ( 2010 届 ) 题 目: 关于xxx的PLC控制系统设计 电气工程与自动化学院 分 院: 电气工程及其自动化 专 业: 班 级: 姓 名: 学 号: 指导老师: 2009年12月 完成日期: 原文: Introductions to PLC and Intelligent Control Katsuhiko Ogata A PLC (i.e. Programmable Logic Controller) is a device that was invented to replace the necessary sequential relay circuits for machine control. The PLC works by looking at its inputs and depending upon their state, turning on/off its outputs. The user enters a program, usually via software or programmer, that gives the desired results. PLCs are used in many “real world” applications. If there is industry present, chances are good that there is a PLC present. If you are involved in machining, packaging, material handling, automated assembly or countless other industries, you are probably already using them. If you are not, you are wasting money and time. Almost any application that needs some type of electrical control has a need for a PLC. For example, let’s assume that when a switch turns on we want to turn a solenoid on for 5 seconds and then turn it off regardless of how long the switch is on for. We can do this with a simple external timer. But what if the process included 10 switches and solenoids? We would need 10 external timers. What if the process also needed to count how many times the switch individually turned on? We need a lot of external counters. As you can see, the bigger the process the more of a need we have for a PLC. We can simply program the PLC to count its inputs and turn the solenoids on for the specified time. We will take a look at what is considered to be the “top 20” PLC instructions. It can be safely estimated that with a firm understanding of these instructions one can solve more than 80% of the applications in existence. That’s right, more than 80%! Of course we’ll learn more than just these instructions to help you solve almost ALL your potential PLC applications. The PLC mainly consists of a CPU, memory areas, and appropriate circuits to receive input/output data, as shown in Fig.1. We can actually consider the PLC to be a box full of hundreds or thousands of separate relays, counters, timers and data storage locations. Do these counters, timers, etc. really exist? No, they don’t “physically” exist but rather they are simulated and can be considered software counters, timers, etc. These internal relays are simulated through bit locations in registers. 1 Fig.1 The structure of PLC What does each part do? INPUT RELAYS-(contacts) These are connected to the outside world. They physically exist and receive signals from switches, sensors, etc.. Typically they are not relays but rather they are transistors. INTERNAL UTILITY RELAYS-(contacts) These do not receive signals from the outside world nor do they physically exist. They are simulated relays and are what enables a PLC to eliminate external relays. There are also some special relays that are dedicated to performing only one task. Some are always on while some are always off. Some are on only once during power-on and are typically used for initializing data that was stored. COUNTERS-These again do not physically exist. They are simulated counters and they can be programmed to count pulses. Typically these counters can count up, down or both up and down. Since they are simulated, they are limited in their counting speed. Some manufacturers also include high-speed counters that are hardware based. We can think of these as physically existing. Most times these counters can count up, down or up and down. TIMERS-These also do not physically exist. They come in many varieties and increments. The most common type is an on-delay type. Others include off-delay and both retentive and non-retentive types. Increments vary from 1ms through 1s. OUTPUT RELAYS-(coils) These are connected to the outside world. They physically exist and send on/off signals to solenoids, lights, etc.. They can be transistors, relays, or triacs depending upon the model chosen. DATA STORAGE-Typically there are registers assigned to simply store data. They are usually used as temporary storage for math or data manipulation. They can also typically be used to store data when power is removed from the PLC. Upon power-up they will still have the same contents as before power was removed. Very convenient and necessary! A PLC works by continually scanning a program. We can think of this scan cycle as consisting of 3 important steps, as shown in Fig.2. There are typically more than 3 but we can focus on the important parts and 2 not worry about the others. Typically the others are checking the system and updating the current internal counter and timer values. Fig.2 The work process of PLC Step 1-CHECK INPUT STATUS-First the PLC takes a look at each input to determine if it is on or off. In other words, is the sensor connected to the first input on? How about the second input? How about the third… It records this data into its memory to be used during the next step. Step 2-EXECUTE PROGRAM-Next the PLC executes your program one instruction at a time. Maybe your program said that if the first input was on then it should turn on the first output. Since it already knows which inputs are on/off from the previous step, it will be able to decide whether the first output should be turned on based on the state of the first input. [3] It will store the execution results for use later during the next step. Step 3-UPDATE OUTPUT STATUS-Finally the PLC updates the status of the outputs. It updates the outputs based on which inputs were on during the first step and the results of executing your program during the second step. Based on the example in step 2 it would now turn on the first output because the first input was on and your program said to turn on the first output when this condition is true. After the third step the PLC goes back to step one and repeats the steps continuously. One scan time is defined as the time it takes to execute the 3 steps listed above. Thus a practical system is controlled to perform specified operations as desired. Intelligence and intelligent systems can be characterized in a number of ways and along a number of dimensions. There are certain attributes of intelligent systems, common in many definitions, which are of particular interest to the control community. In the following, several alternative definitions and certain essential characteristics of intelligent systems are first discussed. A brief working definition of intelligent systems that captures their common characteristics is then presented. In more detail, we start with a rather general definition of intelligent systems, we discuss 3 levels of intelligence, and we explain the role of control in intelligent systems and outline several alternative definitions. We then discuss adaptation and learning, autonomy and the necessity for efficient computational structures in intelligent systems, to deal with complexity. We conclude with a brief working characterization of intelligent (control) systems. We start with a general characterization of intelligent systems: An intelligent system has the ability to act appropriately in an uncertain environment, where an appropriate action is that which increases the probability of success, and success is the achievement of behavioral subgoals that support the system’s ultimate goal. In order for a man-made intelligent system to act appropriately, it may emulate functions of living creatures and ultimately human mental faculties. An intelligent system can be characterized along a number of dimensions. There are degrees or levels of intelligence that can be measured along the various dimensions of intelligence. At a minimum, intelligence requires the ability to sense the environment, to make decisions and to control action. Higher levels of intelligence may include the ability to recognize objects and events, to represent knowledge in a world model, and to reason about and plan for the future. In advanced forms, intelligence provides the capacity to perceive and understand, to choose wisely, and to act successfully under a large variety of circumstances so as to survive and prosper in a complex and often hostile environment. Intelligence can be observed to grow and evolve, both through growth in computational power and through accumulation of knowledge of how to sense, decide and act in a complex and changing world. The above characterization of an intelligent system is rather general. According to this, a great number of systems can be considered intelligent. In fact, according to this definition, even a thermostat may be considered to be an intelligent system, although of low level of intelligence. It is common, however, to call a system intelligent when in fact it has a rather high level of intelligence. There exist a number of alternative but related definitions of intelligent systems and in the following we mention several. They provide alternative, but related characterizations of intelligent systems with emphasis on systems with high degrees of intelligence. The following definition emphasizes the fact that the system in question processes information, and it focuses on man-made systems and intelligent machines: A. Machine intelligence is the process of analyzing, organizing and converting data into knowledge; where (machine) knowledge is defined to be the structured information acquired and applied to remove ignorance or uncertainty about a specific task pertaining to the intelligent machine. This definition leads to the 4 principle of increasing precision with decreasing intelligence, which claims that: applying machine intelligence to a database generates a flow of knowledge, lending an analytic form to facilitate modeling of the process. Next, an intelligent system is characterized by its ability to dynamically assign subgoals and control actions in an internal or autonomous fashion: B. Many adaptive or learning control systems can be thought of as designing a control law to meet well-defined control objectives. This activity represents the system’s attempt to organize or order its “knowledge” of its own dynamical behavior, so to meet a control objective. The organization of knowledge can be seen as one important attribute of intelligence. If this organization is done autonomously by the system, then intelligence becomes a property of the system, rather than of the system’s designer. This implies that systems which autonomously(self) -organize controllers with respect to an internally realized organizational principle are intelligent control systems. [5] A procedural characterization of intelligent systems is given next: C. Intelligence is a property of the system that emerges when the procedures of focusing attention, combinatorial search, and generalization are applied to the input information in order to produce the output. One can easily deduce that once a string of the above procedures is defined, the other levels of resolution of the structure of intelligence are growing as a result of the recursion. Having only one level structure leads to a rudimentary intelligence that is implicit in the thermostat, or to a variable-structure sliding mode controller. The concepts of intelligence and control are closely related and the term “Intelligent Control” has a unique and distinguishable meaning. An intelligent system must define and use goals. Control is then required to move the system to these goals and to define such goals. Consequently, any intelligent system will be a control system. Conversely, intelligence is necessary to provide desirable functioning of systems under changing conditions, and it is necessary to achieve a high degree of autonomous behavior in a control system. Since control is an essential part of any intelligent system, the term “Intelligent Control Systems” is sometimes used in engineering literature instead of “Intelligent Systems” or “Intelligent Machines”. The term “Intelligent Control System” simply stresses the control aspect of the intelligent system. Below, one more alternative characterization of intelligent (control) systems is included. According to this view, a control system consists of data structures or objects (the plant models and the control goals) and processing units or methods (the control laws) : D. An intelligent control system is designed so that it can autonomously achieve a high level goal, while 5 its components, control goals, plant models and control laws are not completely defined, either because they were not known at the design time or because they changed unexpectedly. There are several essential properties present in different degrees in intelligent systems. One can perceive them as intelligent system characteristics or dimensions along which different degrees or levels of intelligence can be measured. Below we discuss three such characteristics that appear to be rather fundamental in intelligent control systems. Adaptation and Learning. The ability to adapt to changing conditions is necessary in an intelligent system. Although adaptation does not necessarily require the ability to learn, for systems to be able to adapt to a wide variety of unexpected changes learning is essential. So the ability to learn is an important characteristic of (highly) intelligent systems. Autonomy and Intelligence. Autonomy in setting and achieving goals is an important characteristic of intelligent control systems. When a system has the ability to act appropriately in an uncertain environment for extended periods of time without external intervention, it is considered to be highly autonomous. There are degrees of autonomy; an adaptive control system can be considered as a system of higher autonomy than a control system with fixed controllers, as it can cope with greater uncertainty than a fixed feedback controller. Although for low autonomy no intelligence (or “low” intelligence) is necessary, for high degrees of autonomy, intelligence in the system (or “high” degrees of intelligence) is essential. Structures and Hierarchies. In order to cope with complexity, an intelligent system must have an appropriate functional architecture or structure for efficient analysis and evaluation of control strategies. This structure should be “sparse” and it should provide a mechanism to build levels of abstraction (resolution, granularity) or at least some form of partial ordering so to reduce complexity. [7] An approach to study intelligent machines involving entropy emphasizes such efficient computational structures. Hierarchies (that may be approximate, localized or combined in heterarchies) that are able to adapt, may serve as primary vehicles for such structures to cope with complexity. The term “hierarchies” refers to functional hierarchies, or hierarchies of range and resolution along spatial or temporal dimensions, and it does not necessarily imply hierarchical hardware. Some of these structures may be hardwired in part. To cope with changing circumstances, the ability to learn is essential, so these structures can adapt to significant, unanticipated changes. In view of the above, a working characterization of intelligent systems (or of (highly) intelligent (control) systems or machines) that captures the essential characteristics present in any such system is: 6 An intelligent system must be highly adaptable to significant unanticipated changes, and so learning is essential. It must exhibit high degree of autonomy in dealing with changes. It must be able to deal with significant complexity, and this leads to certain sparse types of functional architectures such as hierarchies. Nationality:USA Source:Modern Control Engineering .Prentice Hall 7 译文: PLC和智能控制简介 尾形克彦 即可编程逻辑控制器)是机械控制中为替代必要的继电器时序电路而发明的一种PLC( 设备。PLC工作时通过查询输入端并根据其状态打开或关闭输出。用户通常用软件或编程器输入程序，从而获得期望的结果。 很多实际应用都采用PLC。工业生产中应用PLC的可能性很高。如果你正在进行机械制造、产品包装、材料处理、自动化装配及无数其他工业生产，你可能已经用到了PLC。如果没有用到，那就是在浪费金钱和时间。几乎所有需要电气控制的地方都需要PLC。 例如，假定在开关闭合时我们需要一个线圈接通5秒，然后不管开关接通多长时间都将线圈断开。我们可以通过一个简单的外部定时器来实现。但是假如该过程有十个开关和线圈呢,我们就需要十个外部定时器。如果这个过程需要分别记录每个开关开启的次数呢,我们又需要很多外部计数器。 由此可见，系统越大，我们就越需要PLC。我们可以简单地用PLC编程来对输入信号进行计数，并在规定的时间接通线圈。 我们考察一下哪些是PLC中最常用的20条指令。保守地估计一下，如果真正地掌握了这些指令，就能解决80%以上现存的应用问题。 是的，80%以上～当然，我们要学习的指令比这些更多，以帮助你解决几乎所有潜在的PLC应用问题。 PLC 主要由中央处理器(CPU)、存储器和输入、输出电路构成，如图1所示。我们可以将PLC看成是一个装满了成百上千个独立的继电器、计数器、定时器以及数据存储器的盒子。这些计数器、定时器等是不是真的存在呢,不，它们都是模拟的，物理上并不存在，但可以将它们看成是软计数器、软定时器等。这些内部继电器是用寄存器中的位单元模拟出来的。 输入电路 输入继电器 计数器 输出继电器 中央处理器 内部通用继电器 计时器 数据存储器 存储器 输出电路 8 图1 PLC的结构 各个部分是如何工作的呢, 输入继电器(触点)这些继电器连接外部电路。它们是实际存在的，并接收来自开关、传感器等的信号，通常是晶体管而非继电器。 内部通用继电器(触点)它们不从外部设备接收信号，也非物理上存在的。它们是模拟的继电器，用以消除PLC的外部继电器。此外还有一些特殊继电器，专门执行一项任务。其中一些是常开的，一些是常闭的。有一些仅在电源上电时导通一次，通常用来初始化存储的数据。 计数器 它们也非物理上存在的，而是模拟的计数器，可通过编程来对脉冲进行计数。通常它们可进行加计数、减计数或同时进行加减计数。因为它们是用软件模拟的，计数速度就有限。一些制造商提供了基于硬件的高速计数器。这样的计数器可以认为是物理上存在的。这些计数器多数情况下可以进行加计数、减计数或同时进行加减计数。 定时器 它们也非物理上存在的，分为多种类型和定时单位。最常用的一种类型是延时导通型。其他类型还有延时断开型、记忆和非记忆型。定时单位的范围是1ms 到1s。 输出继电器(线圈)该部分连接到外围电路。它们是物理上存在的，并给线圈、灯等发送开关信号。输出继电器可以是晶体管、继电器或可控硅，取决于选择的型号。 数据存储器 它们通常是用来存储数据的寄存器，一般作为运算或数据处理的暂存器。在PLC断电时通常还可用来存储数据。再次接通电源后，其内容与断电前相同，非常方便且必要。 PLC是通过连续扫描一个程序来工作的。我们可以认为扫描周期是由3个主要阶段组成的，如图2所示。当然有多于三个阶段的情况，但我们可关注重要的环节，忽略其他环节。其他阶段通常正在检查系统及更新内部计数器和定时器的当前值。 检查输入状态 执行程序 更新输出状态 图2 PLC的工作过程 第一步——检查输入状态——首先PLC检查每一个输入是否接通。换句话说就是，与 9 第一个输入端连接的传感器接通了吗,第二个输入呢,第三个输入呢,„PLC将这些数据记录到存储器中，以便在下一阶段使用。 第二步——执行程序——然后PLC一次一条指令地执行程序。你的程序可能要求第一个输入接通时，就接通第一个输出。因为在上一步已经知道输入端的开关状态，根据上一步中第一个输入端的状态，就可以确定是否应接通第一个输出。PLC将执行结果存储起来，以供下一步使用。 第三步——更新输出状态——最后PLC更新输出状态。PLC根据第一步中接通的输入和第二步中程序执行的结果更新输出状态。由于第一个输入接通了，程序要求在该条件满足时就接通第一个输出，根据第二步的情况，PLC就接通第一个输出。 PLC在执行完第三步后就返回到第一步，并反复循环。一次扫描时间定义为执行上面的三步所花的时间。因此，一个实际的系统应根据要求去执行特定的操作。 智能与智能系统能用许多方式和从许多方面来描述。通常包含智能系统的特征，这些也是控制领域所关心的特征。 下面，首先讨论智能系统的几种定义和某些基本特性。接着陈述一下具有共同特性的智能系统的一个简洁的工程定义。更详细地，我们从智能系统的一个非常一般的定义开始，讨论智能程度，解释控制在智能系统中的作用，并概括出几种定义。然后讨论智能系统中的自适应和自学习、自主性和必要的高效计算结构，来处理智能系统的复杂性。最后归结出智能(控制)系统的基本特征。 我们从智能系统的一般特征开始: 一个智能系统应具备在不可预测的环境下适当工作的能力，在这个环境中一个适当的反应能够增加成功的可能性，从而达到系统最终的目的。 为了能让人造智能系统适当的工作，它应能模拟生物的功能和基本的人的智能。一个智能系统能从多个方面来描述。智能程度能从智能的各个方面测得。智能至少要具有感受环境、进而做出决定来进行控制的能力。智能化程度比较高的智能系统具有识别目标和事件、描述世界模型中的知识、思考并 计划 项目进度计划表范例计划下载计划下载计划下载课程教学计划下载 未来的能力。在智能化程度更高级的形式中，智能具有感知和理解、理智地做出选择、在各种各样的环境下成功地运行以便能在复杂的、不利的环境下生存和发展的能力。通过计算能力的发展和在复杂多变的环境中怎样感知、决定并做出响应的知识的积累，我们可以观察到智能也在更新与发展。 智能系统的以上特征是非常普遍的。据上所述，很多系统都可以被认为是智能的。事实上，根据这种定义，恒温器尽管只是低水平的智能，但是也可以被认为是智能系统。然 10 而，习惯上当一个系统具有高水平的智能时，我们才称它为智能系统。 智能系统存在许多相关的定义，下面我们提到了几种。它们提供可选择但相关的智能系统的特性，这种智能系统着重强调系统的高程度智能。 下面的定义强调这样一个事实，处理信息的系统集中在人造系统和智能机器上: A. 机器智能是 分析 定性数据统计分析pdf销售业绩分析模板建筑结构震害分析销售进度分析表京东商城竞争战略分析 、组织和转换数据成知识的过程，在这里，知识被定义为结构化的信息，这种信息被用来消除无知或相对于智能机器来说某些特殊任务的不确定性。这种定义导致了增加精度而相对减少智能的原则，这种原则表示:应用机器智能到数据库能生成一系列的知识，通过分析形式进行过程建模。 其次，智能系统具有自动分配任务和在内部自主地控制执行机构的特性: B. 许多自适应或自学习控制系统被认为是作为一种控制法则来满足明确的控制目标。这种行为代表着系统试图组织或排列自己动态行为的知识，来满足控制目标。这些知识的组织是组成智能的一个重要特征。如果系统能够自主地实现这种组织，那么智能就成为系统的一种性能，而非系统设计者的。这意味着能内部实现自组织原则的自组织控制器是智能控制系统。 下面给出智能系统的过程特性: C. 智能是系统的一种特性，当集中注意、联合搜索和概括等过程被应用在输入信息，从而产生输出时，这种特性就会出现。你可以很容易的推断出:一旦以上过程被定义，具有智能结构的结果规则生成并作为新的结果。只有一种 标准 excel标准偏差excel标准偏差函数exl标准差函数国标检验抽样标准表免费下载红头文件格式标准下载 结构将导致在恒温器中固有的一个不成熟的智能或导致成为一个可变结构的变化模型的控制器。 智能和控制的概念紧密相关，并且术语“智能控制”有着独特的、可区别的意思。一个智能系统必须定义和利用目标。控制被要求用来驱动系统达到这些目标并定义这些目标。因此，任何智能系统都是控制系统。相反地，智能必须在条件变化的情况下提供合适的系统运行过程，也必须在控制系统中具有高度的自主行为。因为控制是任何智能系统的一个重要部分，因此在工程文献中“智能控制系统”有时被用来代替“智能系统”或“智能机器”。“智能控制系统”强调智能系统中的控制方面。 下面，介绍一下智能(控制)系统的另外一些特性。根据观察，一个控制系统包括数据结构或对象(设备模型和控制目标)和处理单元或 方法 快递客服问题件处理详细方法山木方法pdf计算方法pdf华与华方法下载八字理论方法下载 (控制规则): D. 由于组件、控制目标、加工模型和控制法则并没有完全被定义，没被定义的原因或是因为在设计时不了解，或是因为它们在不可预测地变化着。所以设计一个智能控制系统以便能自动获得高标准目标。 11 在具有不同智能化程度的系统中呈现出几种基本特性。你可以认为它们是智能系统的特征或是衡量智能程度的方面。下面我们讨论在智能控制系统中三种非常基本的特性。 自适应与自学习 在智能系统中适应变化多端的条件的能力是必需的。尽管自适应不一定要具备自学习能力，但一个系统要适应不可预测的各种变化，学习是最必要的。因此学习能力是高智能系统的一种重要特性。 自主性和智能 在设置和获取目标过程中自主性是智能控制系统的一个重要特性。当一个系统在一个没有外界干预的不确定的环境中能正常运行时，它就被认为是一个高自主性系统。不同系统的自主性程度是不一样的，自适应控制系统被认为比安装了控制器的控制系统具有更高自主能力，因为它比一个固定的反馈控制器更能处理不可预测的问题。尽管对低自主性来说，无智能(或“低”智能)是必然的，但是对高自主性来说，系统的智能(或“高”智能度)是很必要的。 结构和层次 为了应对复杂情况，一个智能系统必须具备一个合适的功能结构来进行有效的分析和控制决策的估计。这种结构是“稀少的”并且它应该提供一种机制来建立提 决议)或者至少提供某种形式的部分规则来减少复杂性。一种研究智能机器的熵方取标准( 法能加强这种有效的计算结构。能够适应的层次(或许是大致的、局部的或是组合的层次)，可以作为一种主要的工具来处理复杂性。这里的“层次”术语是指功能性层次，或者是指时空的范围和决议，而且它并不意味着是一种层次硬件。这里面的某些结构可能是硬件的。为了应付多变的环境，自学习能力是必要的，因此这种结构应能适应重要的、不可预料的变化。 鉴于以上所述，具有其他任何系统都存在的基本特性的智能系统(高智能控制系统或机器)的一个工作特征是: 一个智能系统必须对重要的、不可预料的变化具有很高的适应性，而且自学习也是必要的。在应对变化因素时它必须呈现出高度自主性。它必须能够处理非常复杂的问题，而且这将导致某些稀少的、例如层次这样的功能机构。 国籍:美国 出处:《现代控制工程》普伦蒂斯霍尔出版社 12