关于PLC控制系统设计的外文翻译.doc

本科毕业设计（论文）外文翻译 ( 届 ) 题 目： 关于xxxPLC控制系统设计 分 院: 电气工程与自动化学院 专 业: 电气工程及其自动化 班 级: 姓 名: 学 号: 指导老师: 完成日期: 12月 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 u state, turning on/off its outputs. The user enters a program, usually via software or programmer, t 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 time. Almost any application that needs some type of electrical control has a need for a PLC. For example, lets assume that when a switch turns on we want to turn a solenoid on for 5 seconds a then turn it off regardless of how long the switch is on for. We can do this with a simple externa 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 e counters. As you can see, the bigger the process the more of a need we have for a PLC. We can simply progrPLC 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. Thats right, more than 80%! Of course well learn more than just these instructions to hel you solve almost ALL your potential PLC applications. The PLC mainly consists of a CPU, memory areas, and appropriate circuits to receive input/outpu as shown in Fig.1. We can actually consider the PLC to be a box full of hundreds or thousands of relays, counters, timers and data storage locations. Do these counters, timers, etc. really exist? No, they don “physically” exist but rather they are simulated and can be considered software counters, timers, et internal relays are simulated through bit locations in registers. 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 tran. INTERNAL UTILITY RELAYS-(contacts) These do not receive signals from the outside world nor dothey physically exist. They are simulated relays and are what enables a PLC to eliminate external reare also some special relays that are dedicated to performing only one task. Some are always on whare always off. Some are on only once during power-on and are typically used for initializing datstored. 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 tsimulated, they are limited in their counting speed. Some manufacturers also include high-speed couare 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 son/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 temporary storage for math or data manipulation. They can also typically be used to store data whenremoved from the PLC. Upon power-up they will still have the same contents as before power was remVery 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 importannot 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 oother words, is the sensor connected to the first input on? How about the second input? How about tthird 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 wheoutput should be turned on based on the state of the first input. 3 It will store the execution results for use latduring 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 progthe second step. Based on the example in step 2 it would now turn on the first output because thewas 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 scdefined as the time it takes to execute the 3 steps listed above. Thus a practical system is controlspecified 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 inteare first discussed. A brief working definition of intelligent systems that captures their common chis then presented. In more detail, we start with a rather general definition of intelligent systelevels of intelligence, and we explain the role of control in intelligent systems and outline seve definitions. We then discuss adaptation and learning, autonomy and the necessity for efficient com structures in intelligent systems, to deal with complexity. We conclude with a brief working charact 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 systems 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 dimensions. There are degrees or levels of intelligence that can be measured along the various dim intelligence. At a minimum, intelligence requires the ability to sense the environment, to make deci 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 successfu 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 t 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 grea 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, howev 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 f mention several. They provide alternative, but related characterizations of intelligent systems with 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 definitio principle of increasing precision with decreasing intelligence, which claims that: applying machine to a database generates a flow of knowledge, lending an analytic form to facilitate modeling of the 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 systems attempt to organize or order its “knowledge” of its own dynamical behavior, so to meet a control The organization of knowledge bjective.can be seen as one important attribute of intelligence. If this organization is done autonomously then intelligencbecomes a property of the system, rather than of the systems 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 One can easily deduce that once a string of the above procedures is defined, the other levels of r the structure of intelligence are growing as a result of the recursion. Having only one level struc rudimentary intelligence that is implicit in the thermostat, or to a variable-structure sliding modeThe 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 the 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 contr Since control is an essential part of any intelligent system, the term “Intelligent Control Systems” i 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 gits components, control goals, plant models and control laws are not completely defined, either be 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 them as intelligent system characteristics or dimensions along which different degrees or levels of 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 abl a wide variety of unexpected changes learning is essential. So the ability to learn is an important of (highly) intelligent systems. Autonomy and Intelligence. Autonomy in setting and achieving goals is an important characteristic intelligent control systems. When a system has the ability to act appropriately in an uncertain env extended periods of time without external interventiit is considered to be highly autonomous. There are , degrees of autonomy; an adaptive control system can be considered as a system of higher autonomy control system with fixed controllers, as it can cope with greater uncertainty than a fixed feedbac Although for low autonomyno intelligence (or “low” intelligence) is necessary, for high degrees of a intelligence in the system (or “high” degrees of intellig) is essential. ncStructures 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 s 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. Hierarc(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 hier 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, unanticip