关于PID控制的毕业设计外文翻译
PID中英文对照翻译
中英文互译PID Contro lIntroductionThe PID controller is the most common form of feedback 。
It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95％ of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used 。
The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly 。
PID control is an important ingredient of a distributed control system 。
The controllers are also embedded in many special purpose control systems 。
PID control is often combined with logic ， sequential functions, selectors ， and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies ， such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering。
PID控制中英文对照翻译
外文资料与翻译PID Contro lIntroductionThe PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering. It is an important component in every co ntrol engineer’s tool box.PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.6.2 The AlgorithmWe will start by summarizing the key features of the PID controller. The “textbook” version of the PID algorithm is described by:()()()()⎪⎪⎭⎫ ⎝⎛++=⎰dt t de d e t e K t u T T d t i 01ττ 6.1 where y is the measured process variable, r the reference variable, u is the control signal and e is the control error （e =sp y − y ）. The reference variable is often calledthe set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. The controller parameters are proportional gain K, integral time T i, and derivative time T d. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.Effects of Proportional, Integral and Derivative ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with T i= and T d=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing integral time T i. The figure shows that the steady state error disappears when integral action is used. Compare with the discussion of the “magic of integral action” in Sec tion 2.2. The tendency for oscillation also increases with decreasing T i. The properties of derivative action are illustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. The parameters K and T i are chosen so that the closed loop system is oscillatory. Damping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the time T d. Using this interpretation it is easy to understand that derivative action does not help if the prediction time T d is too large. In Figure 6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2Derivative actions cease to be effective when T d is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.A PerspectiveThere is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider。
PID控制简介中英文翻译word版
英语资料及译文About PID controlRecently automation technology is based on the concept of feedback. Elements of feedback theory consist of three parts: measurement, comparison and implementation. Measurement variables of concern, compared with expectations, with the control system to correct the error response.The theory and application of automatic control, the key is to make the correct measurement and comparison of how best to rectify the system.PID (proportional - integral - differential) controller as the first practical use of the controller more than 50 years of history, is still the most widely used industrial controller. Simple PID controller, the use of the system does not accurately model a prerequisite that they have become the most widely used controller.PID controller is the proportion of cells (P), integral unit (I) and the differential unit (D) component.Because of its wide range of uses, the use of flexible, has been serialized products, the use of only three parameters setting (Kp, Ti and Td) can be. In many cases, does not necessarily need all three modules, which can take 1-2 unit, but the proportion of the control unit is essential.First of all, PID broad range of applications. Although many industrial processes or time-varying non-linear, but can be simplified through their basic non-linear and dynamic characteristics of the system over time, so that you can control the PID.Secondly, PID parameter can tune easier. That is, PID parameters Kp, Ti and Td can be based on the dynamic characteristics of the process of setting a timely manner. If the dynamic characteristics of the process ofchange, for example, changes may be caused by the load dynamic characteristics of the system changes, PID parameters can be re-tuning.Third, PID controller in practice is to be improved continuously, the following are two examples of improvements.In factories, we always see a lot of loops are in manual, and because of the difficulty of the course so that the "automatic" mode, a smooth working. As a result of these deficiencies, the use of the industrial control system PID is always subject to product quality, safety, waste production and energy problems. PID parameter self-tuning PID parameters in order to deal with this problem setting generated. Now, the auto-tuning or self-tuning of PID controller is a business single-loop controllers and distributed control system of a standard.In some cases the system-specific design of PID controller to control very well, but they are there are still some problems to be solved: If self-tuning should be based on the model, in order to re-PID tuning parameters online to find and maintain a good process model is more difficult. When closed-loop works, the requirements in the process of inserting must have a test signal. This method will cause disturbance, so model-based PID parameter self-tuning is not too good in the industrial applications.If self-tuning control law based on the often difficult to load disturbance caused by the impact and dynamic characteristics of the process of the impact of changes in the distinction between the effects of so disturbed overshoot controller will have to create a self-adaptive unnecessary conversion. In addition, since the control law based on the maturity of the system is not the stability of analytical methods, the reliability of parameter tuning, there are many problems.Therefore, many self-tuning PID controller parameters work in the auto-tuning mode and not in the self-tuning mode. Auto-tuning is oftenused to describe the state of open-loop based on a simple process model to determine automatic calculation of PID parameters.PID in controlling nonlinear, time-varying, coupling and parameter uncertainty and structural complexity of the process, the work is not very good. The most important thing is, if the PID controller can not control the complexity of the process, regardless of how not to use transfer parameters.Despite these shortcomings, PID controller is sometimes the most simple is the best controller.At present, the level of industrial automation has become a measure of the level of modernization in all walks of life an important sign. At the same time, the development of control theory has also experienced a classical control theory, modern control theory and intelligent control theory of three stages. Classic example of intelligent control is ambiguous, such as full-automatic washing machine. Automatic control system can be divided into open-loop control systems and closed-loop control system.A control system, including controllers, sensors, transmitters, implementing agencies, input and output interfaces. Controller's output after the output interface, the implementing agencies, added to the system was charged with; control system charged with the amount, after the sensor, transmitter, through the input interface to the controller. Different control system, the sensor, transmitter, the executing agency is not the same. For example pressure sensors need to be used in pressure control system. Electric heating control system is the sensor temperature sensor. At present, PID control and PID controller or a smart controller (instrument) has a lot of products have been in practice in engineering is widely used, there are a wide range of PID controller products, major companies have developed with PID parameter self-tuning regulator function smart (intelligent regulator), which the PID controller parameters are automatically adjusted through the intelligent or self-tuning, adaptivealgorithms to achieve. PID control are achieved using pressure, temperature, flow, liquid level controller, PID control functions to achieve the programmable logic controller (PLC), also enables the PC system, PID control and so on. Programmable Logic Controller (PLC) is the use of its closed-loop control module to achieve PID control, and programmable logic controller (PLC) can be connected directly with the ControlNet, such as Rockwell's PLC-5 and so on. There can be the controller PID control functions, such as Rockwell's Logix product line, it can be connected directly with the ControlNet, using the Internet to achieve its long-range control functions.1, Open-loop control systemOpen-loop control system is the object of the output (volume control) on the controller does not affect the output. In this control system, do not rely on volume will be charged back to the formation of anti-any closed-loop circuit.2, Closed-loop control systemClosed-loop control system is characterized by the output of the system object (volume control) will be sent back to the impact of anti-output controller to form one or more of the closed-loop. Closed-loop control system has positive feedback and negative feedback, if the feedback signal and the system to set the value of the signal the other hand, is referred to as negative feedback, if the same polarity is called positive feedback, the general closed-loop control system using negative feedback, also known as negative feedback control system. Closed-loop control system has many examples. For example, people with negative feedback is a closed-loop control system, the eye is the sensor to act as a feedback, the human body system through the constant variety of the right to make amendments to the final action. If there are no eyes, there is no feedback loops, it became an open-loop control system. Another example, when a full-automatic washing machine with a realcontinuously check whether the washed clothing, and wash off automatically after the power supply, it is a closed-loop control system.3, Step responseStep response refers to a step input (step function) when added to the system, the system output. Steady-state error is the system response into the steady-state, the system's desired output and actual output of the difference. The performance of control system can be stable, accurate and fast three words to describe. Stability is the stability of the system, a system must be able to work, first of all must be stable, from the step response should be a convergence point of view; quasi-control system refers to the accuracy, control precision, stability is usually state error description, it said the system output steady-state value and the difference between expectations; fast control system refers to the rapid response, and usually to a quantitative description of the rise time.4, Theory and the characteristics of PID controlIn engineering practice, the most widely used control laws regulate the proportional, integral, differential control, referred to as PID control, also known as PID regulator. PID controller has been available for nearly 70 years of history, which in its simple structure, stable, reliable, easy to adjust and become the main industrial control technologies. When charged with the structure and parameters of the object can not completely grasp, or lack of accurate mathematical model, control theory it is difficult using other techniques, the system controller structure and parameters have to rely on experience and on-site testing to determine when the application PID control of the most convenient technology. That is, when we do not fully understand the system and charged with an object, or can not be an effective means of measuring system parameters to obtain the most suitable PID control technology. PID control, in practice there are PI and PD control. PID controller is the error of the system, using proportional, integral, differential calculationfor the control of the volume control.The ratio of (P) controlProportional control is one of the most simple control methods. The controller's output and input error signal proportional to the relationship. The output has the existence of steady-state error when there is only a proportional control system.Integral (I) controlIn integral control, the controller's output and input error signal is proportional to the integral relationship. For an automatic control system, if steady-state error exists after entering the steady-state, the control system is referred to as steady-state error or having a poor system. In order to eliminate steady-state error, the controller must be the introduction of the "key points." Points of error depend on the time of the points of the increase over time, will increase the integral term. In this way, even if the error is very small, integral term will increase as time increases, it increased to promote the output of the controller so that steady-state error further reduced until zero. Therefore, the proportional + integral (PI) controller, you can make the system after entering the steady-state non-steady-state error.Differential (D) controlIn the differential control, the controller's output and input of the differential error signal (the rate of change of error) is directly proportional to the relationship. Automatic control system to overcome the errors in the adjustment process may be unstable or even oscillation. The reason is because of greater inertial components (links) or there is lag components, can inhibit the role of error, the changes always lag behind changes in error. The solution is to inhibit the changes in the role of error "in advance", that is close to zero in the error and suppress the role of error should be zero. This means that the controller only the introduction of the "proportion" of often is not enough, the proportion of the role isonly to enlarge the amplitude error, the current need to increase the "differential item" that can change the trend of prediction error, In this way, with the proportion of + differential controller, will be able to advance so that the role of inhibitory control error equal to zero or even negative, thus avoiding the amount charged with a serious overshoot. Therefore, greater inertia of the charged object or lag, the proportion of + differential (PD) controller to improve the system in the regulation of the dynamic characteristics of the process.PID控制简介当今的自动控制技术都是基于反馈的概念。
PID控制的意义中英文翻译资料
The significance of PID controlThe current level of industrial automation industries to measure the level of modernization has become an important symbol. Meanwhile, the control also experienced the development of the theory of classical control theory, modern control theory and intelligent control theory of three stages. Intelligent control is a typical example of fuzzy automatic washing machine. Open-loop control system can be divided into control systems and closed-loop control system. A control system including controller, sensors, transmitters, actuators, input and output interfaces. Controller's output through the output interface, the implementing agency, added to the charged system; control system, the amount charged, through sensors, transmitters, sent to the controller through the input interface. Different control systems, sensors, transmitters, actuators are not the same. Such as pressure control system pressure sensor to be used. Electric heating control system sensor is a temperature sensor. At present, PID control and controller or intelligent PID controller (instrument) has a lot of products have been in the engineering practice has been widely applied, there is a wide range of PID controllers, the major companies have developed PID parameter self-tuning capabilities of intelligent controller (intelligent regulator), which automatically adjusts the PID controller parameters are adjusted through the intelligent or self-correction, adaptive algorithms to achieve. PID control are achieved using pressure, temperature, flow, liquid level controller, PID control can achieve programmable controller (PLC), also allows PID control of PC systems, etc.. Programmable Logic Controller (PLC) is to use the closed-loop PID control module to achieve control, programmable logic controller (PLC) can be connected directly with ControlNet, such as Rockwell's PLC-5 and so on. PID control function also allows the controller, such as Rockwell's Logix product line, which can be connected directly with ControlNet, use the network to achieve its remote control functions.1, the open-loop control systemOpen-loop control system (open-loop control system) is charged with theobject output (controlled variables) on the controller (controller) did not affect the output. In this control system, not dependent on the amount will be charged against sending it back to form any closed loops.2, closed loop control systemClosed loop control system (closed-loop control system) is characterized by the system control object output (controlled variables) will affect the controller against the output sent back to form one or more closed loop. Closed-loop control system has positive feedback and negative feedback, if the feedback signal and system for a given value of signal contrast, is known as negative feedback (Negative Feedback), if the same polarity is called positive feedback, the general closed-loop negative feedback control systems are used , also known as negative feedback control system. Many examples of closed loop control system. Such person is a negative feedback loop control system, the eye is the sensor, as feedback, the human system through constant correction to all the right moves last. If there are no eyes, no feedback loop, will become an open-loop control system. Other cases, when a truly automatic washing machines have to continuously check whether clothes washed, and cut off the power automatically after cleaning, it is a closed loop control system.3, step responseStep response is a step input (step function) added to the system, the system outputs. Steady-state error is the response of the system into steady state, the system's expected output and actual output of the difference. Control system performance can be stable, accurate, fast and three words to describe. Stability is the stability of the system (stability), a system to work properly, first of all must be stable, from the step response appears to be that convergence; quasi-control system refers to the accuracy, control precision, usually stable state error to (Steady-state error) description, it said the system output and the expected steady-state value of the difference; faster control system response is fast, usually the rise time to quantify.4, PID control principles and characteristicsIn engineering practice, the most widely used regulator control law is proportional, integral, differential control, referred to as PID control, also known as PID regulator. PID controller has been developed for nearly 70 years, it is its simple structure, stable, reliable, easy to adjust and become one of the main techniques of industrial control. When the structure and parameters of the object and can not fully grasp, or lack of accurate mathematical models, control theory is difficult to use other technologies, the system controller structure and parameters must rely on experience and on-site commissioning to determine, when applied PID control technique is more convenient. That is, when we do not fully understand a system and the controlled object, or can not be an effective means of measurement to obtain system parameters, the most suitable PID control technology. PID control, in practice there are PI and PD control. PID controller is the error according to the system, using proportional, integral, differential calculation of the volume control to control.Proportion (P) controlProportional control is the most simple control method. The controller's output and the input error signal proportional. When only a proportional control system output when there is steady-state error (Steady-state error).Integral (I) controlIn integral control, the controller's output and the input error signal proportional to the integral. An automatic control system into the steady state if there is steady-state error, claimed that this control system is called a steady-state error or poor system (System with Steady-state Error). In order to eliminate steady state error, the controller must introduce the "integral term." Integral term of the error depends on the time integral, as time increases, integral term will increase. Thus, even if the error is very small, integral term will increase over time to increase its promotion of the controller output increases to further reduce the steady-state error, until zero. Therefore, the ratio of + integral (PI) controller allows the systemto enter steady state of no steady state error.Differential (D) controlIn the differential control, the controller output and differential input error signal (ie, rate of change of error) is proportional to. Automatic control system to overcome the errors in the adjustment process of oscillation or even instability may occur. The reason is because of greater inertia components (links), or a lag (delay) component, can inhibit the role of error, the changes always lag behind changes in the error. The solution is to change the role of inhibition of error, "ahead", that is close to zero in the error and suppress the role of error should be zero. This means that the controller only the introduction of the "ratio" item is often not enough, the proportion of item only to enlarge the role of the magnitude of the error, but now need to increase the "differential item" that can change the trend of forecast errors, In this way, with the proportion of + differential controller, it can advance to the role of inhibition of the control error is zero, even negative, thus avoiding the charged amount of serious overshoot. Therefore have greater inertia or lag the controlled object, proportional + derivative (PD) controller5, PID controller tuningTuning PID controller is the core of the control system design. It is based on the characteristics of controlled process to determine the proportion of PID controller coefficients, integral time and derivative time, the size of the. PID controller tuning are many ways to sum up, there are two categories: First, tuning the theoretical calculation. It is mainly based on the mathematical model, through theoretical calculations to determine the controller parameters. This method the calculated data may not be directly used, it must adjust and revise engineering. Second, the tuning method works, it mainly relies on engineering experience, directly in the control experiments carried out, and the method is simple, easy to master, in engineering practice is widely used. PID controller parameter tuning method works, mainly the critical ratio, reaction curve and attenuation. Three methods have their own characteristics, their common points are the experiment, and then follow theempirical formula works on the controller parameter tuning. But no matter which method used by the controller parameters are needed in the actual operation of the final adjustment and improvement. Now commonly used is the critical ratio method. PID controller using the method parameter setting of the following steps: (1) first pre-select a short enough sampling period of the system to work; (2) by adding proportional control only part until the system appears critical step response input oscillation Note the amplification factor and the proportion of time critical oscillation period; (3) a certain degree of control in the formula be adopted under the PID controller parametersPID控制的意义目前工业自动化水平已成为衡量各行各业现代化水平的一个重要标志。
PID控制中英文对照翻译
外文资料与翻译PID Contro l6.1 IntroductionThe PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering. It is an important component in every control engineer’s tool box.PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.6.2 AlgorithmWe will start by summarizing the key features of the PID controller. The “textbook” version of the PID algorithm is described by:()()()()⎪⎪⎭⎫ ⎝⎛++=⎰dt t de d e t e K t u T T d t i 01ττ 6.1 where y is the measured process variable, r the reference variable, u is the control signal and e is the control error （e =sp y − y ）. The reference variable is often calledthe set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. The controller parameters are proportional gain K, integral time T i, and derivative time T d. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.Effects of Proportional, Integral and Derivative ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with T i= and T d=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing integral time T i. The figure shows that the steady state error disappears when integral action is used. Compare with the discussion of the “magic of integral action” in Section 2.2. The tendency for oscillation also increases with decreasing T i. The properties of derivative action are illustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. The parameters K and T i are chosen so that the closed loop system is oscillatory. Damping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the time T d. Using this interpretation it is easy to understand that derivative action does not help if the prediction time T d is too large. In Figure 6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2Derivative actions cease to be effective when T d is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.A PerspectiveThere is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider。
【系统】PID中英文对照翻译
【关键字】系统PID ControlIntroductionThe PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread a nd butter of control engineering. It is an important component in every control engineer’s tool box.PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.6.2 The AlgorithmWe will start by summarizing the key features of the PID controller. The “textbook” version of the PID algorithm is described by:6.1where y is the measured process variable, r the reference variable, u is the control signal and e is the control error（e = − y）. The reference variable is often called the set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. Thecontroller parameters are proportional gain K, integral time Ti, and derivative time Td. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.Effects of Proportional, Integral and Derivative ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with Ti = and Td=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing integral time Ti. The figure shows that the steady state error disappears when integral action is used. Compare with the discussion of the “magic of integral action” in Section 2.2. The tendency for oscillation also increases with decreasing Ti. The properties of derivative action are illustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. The parameters K and Ti are chosen so that the closed loop system is oscillatory. Damping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the time Td. Using this interpretation it is easy to understand that derivative action does not help if the prediction time Td is too large. In Figure 6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2Derivative actions cease to be effective when Td is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.A PerspectiveThere is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider。
外文翻译---PID控制算法
附录附件1：英文资料及中文翻译英文资料PID control algorithm1, PID is a closed loop control algorithm. Therefore, to achieve the PID algorithm, you must have the hardware loop control, that is, must have feedback. Such as controlling a motor speed, you have to have a measurement of speed sensor, and the results fed back to the control line, the following will also be speed control, for example.2, PID is the proportional (P), integral (I), Derivative (D) control algorithms. But not must have these three algorithms, it can be PD, PI, or even only the P-control algorithm. I used for closed-loop control of one of the most simple idea only P control, the current results fed back, and then subtract the target, is positive, then slow down, then it is negative acceleration. Now know that this is just the simplest closed-loop control algorithms.3, the ratio (P), integral (I), differential (D) control algorithms have effect:?????Proportion of the reaction system, the basic (current) deviation e (t), coefficient, can speed up the regulation, reduce errors, but the system is too large proportion decreased stability, or even cause system instability;?????Integral, the cumulative deviation of the reaction system, allowing the system to eliminate the steady state error, no gradient to improve, because there is an error, integral control will be carried out until no error;?????Differential, reflecting the rate of change of the system deviation signal e (t)-e (t-1), is foreseen to anticipate trends deviation generated ahead of the control action, the deviation is not formed, has been the differential regulation of remove, so you can improve the dynamic performance. However, differential interference to noise amplification role in strengthening differential interference detrimental to the system.Integral and differential can not work alone and must be proportional control cooperation.4, the controller of the P, I, D item selection.The following rules will be used to control various control features briefly summarized here: 1, the proportional control law P: P control law used to quickly overcome the disturbances, which act on the output value of the fast, but not very stable at a desired value, compared with a negative result is to effectively overcome the disturbances, but more than a difference appears. It is suitable for the control channel lag is small, little change in the load control is less demanding, controlled parameters within a certain range to allow more than poor occasions. Such as: Jin Biao under the Ministry of Public Works pump house cold, heat pool water level control; pump room intermediate tank oil level control. 2, proportional integral control law (PI): in engineering proportional integral control law is the most widely used control law. Points that can be eliminated on the basis of the proportion of residual error, which applies to the control channel lag is small, little change in load, was charged with parameters do not allow more than a poor occasions. Eg: In the main chamber kiln heavy commutation No. F1419 F1401 to heavy gun flow control system; pump room for the pipeline flow control system; annealing furnace temperature control system, etc. districts. 3, proportional and differential control law (PD): Differential effects has advanced, with capacity for hysteresis control channel, the introduction of differential involved in the control, the derivative term is set properly in the case, for improving the system's dynamic performance indicators, have a significant effect. Therefore, for the time constant of the control channel or capacity lags larger occasions, in order to improve the stability of the system, reducing the dynamic deviation proportional derivative control law can be used. Such as: heated temperature control, composition control. It should be noted that, for those pure lag larger area, the derivative term is powerless, and in the measured signal is noisy or periodic vibration system, you should not use derivative control. Such as: large kiln glass level control. 4, for example, integral derivative control laws (PID): PID control law is an ideal control law, it is based on the introduction of proportional integral, can eliminate residual error, then add the derivative action, but also improve system stability. Itis suitable for the control channel or capacity lag time constant is large, the control requirements of the occasion. Such as temperature control, composition control.????Given D role of the law, we must also understand the concept of time lag, the time lag including capacity lag and pure lag. Capacity in which they lag usually include: measurement and transmission lag lag. Measurement hysteresis is detecting element in the detection need to establish a balance, such as thermocouples, RTD, pressure, etc. resulting from a slow response lag. The transmission lag is in the sensors, transmitters, actuators and other devices produce a control delay. Pure delay is measured relative lag in industry, most of the time delay caused due to material transport, such as: large kiln glass level, feeding maneuver done in the nuclear level gauge detection requires a very long period of time.????In short, the choice of control law according to the process characteristics and process requirements to select, and never say PID control law in any case, has better control performance, regardless of the occasion are used is unwise. If you do so, it will only add complexity to the other work and give parameter tuning difficult. When using PID controller has not yet reached technological requirements, you need to consider other control scheme. If cascade control, feedforward control, large hysteresis control.5the problem. Kp, Ti, Td setting the three parameters of the PID control algorithm key issues. Generally they can only be set when programming the approximate value and the system is running through repeated testing to determine the best value. Therefore, the program must apply for the commissioning phase and memory can be modified at any of these three parameters.6 the parameter self-tuning. In some applications, such as generic instrumentation industry, systems work object is uncertain, different objects have different parameter values, can not set parameters for the user, on the introduction of the concept of self-tuning parameters . Essence is the first time, through the N measurements is looking for a new job object parameters, andremembered as a basis for future work.中文翻译PID控制算法1，PID是一个闭环控制算法。
机械类 数控 外文翻译 外文文献 英文文献 PID控制器
PID controllerA proportional–integral–derivative controller (PID controller) is a generic .control loop feedback mechanism widely used in industrial control systems. A PID controller attempts to correct the error between a measured process variable and a desired setpoint by calculating and then outputting a corrective action that can adjust the process accordingly.The PID controller calculation (algorithm) involves three separate parameters; the Proportional, the Integral and Derivative values. The Proportional value determines the reaction to the current error, the Integral determines the reaction based on the sum of recent errors and the Derivative determines the reaction to the rate at which the error has been changing. The weightedsum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element.By "tuning" the three constants in the PID controller algorithm the PID can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the setpoint and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability.Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are particularly common, since derivative action is very sensitive to measurement noise, and the absence of an integral value may prevent the system from reaching its target value due to the control action.Note: Due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in common use.1.Control loop basicsA familiar example of a control loop is the action taken to keep one's shower water at the ideal temperature, which typically involves the mixing of two process streams, cold and hot water. The person feels the water to estimate its temperature. Based on this measurement they perform a control action: use the cold water tap to adjust the process. The person would repeat this input-output control loop, adjusting the hot water flow until the process temperature stabilized at the desired value.Feeling the water temperature is taking a measurement of the process value or process variable (PV). The desired temperature is called the setpoint (SP). The output from the controller and input to the process (the tap position) is called the manipulated variable (MV). The difference between the measurement and the setpoint is the error (e), too hot or too cold and by how much.As a controller, one decides roughly how much to change the tap position (MV) after one determines the temperature (PV), and therefore the error. This first estimate is the equivalent of the proportional action of a PID controller. The integral action of a PID controller can be thought of as gradually adjusting the temperature when it is almost right. Derivative action can be thought of as noticing the water temperature is getting hotter or colder, and how fast, and taking that into account when deciding how to adjust the tap.Making a change that is too large when the error is small is equivalent to a high gain controller and will lead to overshoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, this control loop would be termed unstable and the output would oscillate around the setpoint in either a constant, growing, or decaying sinusoid. A human would not do this because we are adaptive controllers, learning from the process history, but PID controllers do not have the ability to learn and must be set up correctly. Selectingthe correct gains for effective control is known as tuning the controller.If a controller starts from a stable state at zero error (PV = SP), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that impact on the process, and hence on the PV. Variables that impact on the process other than the MV are known as disturbances and generally controllers are used to reject disturbances and/or implement setpoint changes. Changes in feed water temperature constitute a disturbance to the shower process.In theory, a controller can be used to control any process which has a measurable output (PV), a known ideal value for that output (SP) and an input to the process (MV) that will affect the relevant PV. Controllers are used in industry to regulate temperature, pressure, flow rate, chemical composition, speed and practically every other variable for which a measurement exists. Automobile cruise control is an example of a process which utilizes automated control.Due to their long history, simplicity, well grounded theory and simple setup and maintenance requirements, PID controllers are the controllers of choice for many of these applications.2.PID controller theoryNote: This section describes the ideal parallel or non-interacting form of the PID controller. For other forms please see the Section "Alternative notation and PID forms".The PID control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (MV). Hence:where Pout, Iout, and Dout are the contributions to the output from the PID controller from each of the three terms, as defined below.2.1. Proportional termThe proportional term makes a change to the output that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant Kp, called the proportional gain.The proportional term is given by:WherePout: Proportional outputKp: Proportional Gain, a tuning parametere: Error = SP − PVt: Time or instantaneous time (the present)Change of response for varying KpA high proportional gain results in a large change in the output for a given change in the error. If the proportional gain is too high, the system can become unstable (See the section on Loop Tuning). In contrast, a small gain results in a small output response to a large input error, and a less responsive (or sensitive) controller. If the proportional gain is too low, the control action may be too small when responding to system disturbances.In the absence of disturbances, pure proportional control will not settle at its target value, but will retain a steady state error that is a function of the proportional gain and the process gain. Despite the steady-state offset, both tuning theory and industrial practice indicate that it is the proportional term that should contribute the bulk of the output change.2.2.Integral termThe contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. Summing the instantaneous error over time (integrating the error) gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain and added to the controller output. The magnitude of the contribution of the integral term to the overall control action is determined by the integral gain, Ki.The integral term is given by:Iout: Integral outputKi: Integral Gain, a tuning parametere: Error = SP − PVτ: Time in the past contributing to the integral responseThe integral term (when added to the proportional term) accelerates the movement of the process towards setpoint and eliminates the residual steady-state error that occurs with a proportional only controller. However, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the setpoint value (cross over the setpoint and then create a deviation in the other direction). For further notes regarding integral gain tuning and controller stability, see the section on loop tuning.2.3 Derivative termThe rate of change of the process error is calculated by determining the slope of the error over time (i.e. its first derivative with respect to time) and multiplying this rate of change by the derivative gain Kd. The magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd.The derivative term is given by:Dout: Derivative outputKd: Derivative Gain, a tuning parametere: Error = SP − PVt: Time or instantaneous time (the present)The derivative term slows the rate of change of the controller output and this effect is most noticeable close to the controller setpoint. Hence, derivative control is used to reduce the magnitude of the overshoot produced by the integral component and improve the combined controller-process stability. However, differentiation of a signal amplifies noise and thus this term in the controller is highly sensitive to noise in the error term, and can cause a process to become unstable if the noise and the derivative gain are sufficiently large.2.4 SummaryThe output from the three terms, the proportional, the integral and the derivative terms are summed to calculate the output of the PID controller. Defining u(t) as the controller output, the final form of the PID algorithm is:and the tuning parameters areKp: Proportional Gain - Larger Kp typically means faster response since thelarger the error, the larger the Proportional term compensation. An excessively large proportional gain will lead to process instability and oscillation.Ki: Integral Gain - Larger Ki implies steady state errors are eliminated quicker. The trade-off is larger overshoot: any negative error integrated during transient response must be integrated away by positive error before we reach steady state.Kd: Derivative Gain - Larger Kd decreases overshoot, but slows down transient response and may lead to instability due to signal noise amplification in the differentiation of the error.3. Loop tuningIf the PID controller parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, i.e. its output diverges, with or without oscillation, and is limited only by saturation or mechanical breakage. Tuning a control loop is the adjustment of its control parameters (gain/proportional band, integral gain/reset, derivative gain/rate) to the optimum values for the desired control response.The optimum behavior on a process change or setpoint change varies depending on the application. Some processes must not allow an overshoot of the process variable beyond the setpoint if, for example, this would be unsafe. Other processes must minimize the energy expended in reaching a new setpoint. Generally, stability of response (the reverse of instability) is required and the process must not oscillate for any combination of process conditions and setpoints. Some processes have a degree of non-linearity and so parameters that work well at full-load conditions don't work when the process is starting up from no-load. This section describes some traditional manual methods for loop tuning.There are several methods for tuning a PID loop. The most effective methods generally involve the development of some form of process model, then choosing P, I, and D based on the dynamic model parameters. Manual tuning methods can be relatively inefficient.The choice of method will depend largely on whether or not the loop can be taken "offline" for tuning, and the response time of the system. If the system can be taken offline, the best tuning method often involves subjecting the system to a step change in input, measuring the output as a function of time, and using this response to determine the control parameters.Choosing a Tuning MethodMethodAdvantagesDisadvantagesManual TuningNo math required. Online method.Requires experiencedpersonnel.Ziegler–NicholsProven Method. Online method.Process upset, sometrial-and-error, very aggressive tuning.Software ToolsConsistent tuning. Online or offline method. May includevalve and sensor analysis. Allow simulation before downloading.Some costand training involved.Cohen-CoonGood process models.Some math. Offline method. Only good forfirst-order processes.3.1 Manual tuningIf the system must remain online, one tuning method is to first set the I and D values to zero. Increase the P until the output of the loop oscillates, then the P should be left set to be approximately half of that value for a "quarter amplitude decay" type response. Then increase D until any offset is correct in sufficienttime for the process. However, too much D will cause instability. Finally, increase I, if required, until the loop is acceptably quick to reach its reference after a load disturbance. However, too much I will cause excessive response and overshoot. A fast PID loop tuning usually overshoots slightly to reach the setpoint more quickly; however, some systems cannot accept overshoot, in which case an "over-damped" closed-loop system is required, which will require a P setting significantly less than half that of the P setting causing oscillation.3.2Ziegler–Nichols methodAnother tuning method is formally known as the Ziegler–Nichols method, introduced by John G. Ziegler and Nathaniel B. Nichols. As in the method above, the I and D gains are first set to zero. The "P" gain is increased until it reaches the "critical gain" Kc at which the output of the loop starts to oscillate. Kc and the oscillation period Pc are used to set the gains as shown:3.3 PID tuning softwareMost modern industrial facilities no longer tune loops using the manual calculation methods shown above. Instead, PID tuning and loop optimization software are used to ensure consistent results. These software packages will gather the data, develop process models, and suggest optimal tuning. Some software packages can even develop tuning by gathering data from reference changes.Mathematical PID loop tuning induces an impulse in the system, and then uses the controlled system's frequency response to design the PID loop values. In loops with response times of several minutes, mathematical loop tuning is recommended, because trial and error can literally take days just to find a stable set of loop values. Optimal values are harder to find. Some digital loop controllers offer a self-tuning feature in which very small setpoint changes are sent to the process, allowing the controller itself to calculate optimal tuning values.Other formulas are available to tune the loop according to different performance criteria.4 Modifications to the PID algorithmThe basic PID algorithm presents some challenges in control applications that have been addressed by minor modifications to the PID form.One common problem resulting from the ideal PID implementations is integralwindup. This can be addressed by:Initializing the controller integral to a desired valueDisabling the integral function until the PV has entered the controllable regionLimiting the time period over which the integral error is calculatedPreventing the integral term from accumulating above or below pre-determined boundsMany PID loops control a mechanical device (for example, a valve). Mechanical maintenance can be a major cost and wear leads to control degradation in the form of either stiction or a deadband in the mechanical response to an input signal. The rate of mechanical wear is mainly a function of how often a device is activated to make a change. Where wear is a significant concern, the PID loop may have an output deadband to reduce the frequency of activation of the output (valve). This is accomplished by modifying the controller to hold its output steady if the change would be small (within the defined deadband range). The calculated output must leave the deadband before the actual output will change.The proportional and derivative terms can produce excessive movement in the output when a system is subjected to an instantaneous "step" increase in the error, such as a large setpoint change. In the case of the derivative term, this is due to taking the derivative of the error, which is very large in the case of an instantaneous step change.5. Limitations of PID controlWhile PID controllers are applicable to many control problems, they can perform poorly in some applications.PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or "hunt" about the control setpoint value. The control system performance can be improved by combining the feedback (or closed-loop) control of a PID controller with feed-forward (or open-loop) control. Knowledge about the system (such as the desired acceleration and inertia) can be "fed forward" and combined with the PID output to improve the overall system performance. The feed-forward value alone can often provide the major portion of the controller output. The PID controller can then be used primarily to respond to whatever difference or "error" remains between the setpoint (SP) and the actual value of the process variable (PV). Since the feed-forward output is not affected by the process feedback, it can never cause the control system to oscillate, thus improving the system response and stability.For example, in most motion control systems, in order to accelerate a mechanical load under control, more force or torque is required from the prime mover, motor, or actuator. If a velocity loop PID controller is being used to control the speed of the load and command the force or torque being applied by the prime mover, then it is beneficial to take the instantaneous acceleration desired for the load, scale that value appropriately and add it to the output of the PID velocity loop controller. This means that whenever the load is being accelerated or decelerated, a proportional amount of force is commanded from the prime mover regardless of the feedback value. The PID loop in this situation uses the feedback information to effect any increase or decrease of the combined output in order to reduce the remaining difference between the process setpoint and thefeedback value. Working together, the combined open-loop feed-forward controller and closed-loop PID controller can provide a more responsive, stable and reliable control system.Another problem faced with PID controllers is that they are linear. Thus, performance of PID controllers in non-linear systems (such as HV AC systems) is variable. Often PID controllers are enhanced through methods such as PID gain scheduling or fuzzy logic. Further practical application issues can arise from instrumentation connected to the controller. A high enough sampling rate, measurement precision, and measurement accuracy are required to achieve adequate control performance.A problem with the Derivative term is that small amounts of measurement or process noise can cause large amounts of change in the output. It is often helpful to filter the measurements with a low-pass filter in order to remove higher-frequency noise components. However, low-pass filtering and derivative control can cancel each other out, so reducing noise by instrumentation means is a much better choice. Alternatively, the differential band can be turned off in many systems with little loss of control. This is equivalent to using the PID controller as a PI controller.6. Cascade controlOne distinctive advantage of PID controllers is that two PID controllers can be used together to yield better dynamic performance. This is called cascaded PID control. In cascade control there are two PIDs arranged with one PID controlling the set point of another. A PID controller acts as outer loop controller, which controls the primary physical parameter, such as fluid level or velocity. The other controller acts as inner loop controller, which reads the output of outer loop controller as set point, usually controlling a more rapid changing parameter, flowrate or accelleration. It can be mathematically proved that the working frequency of the controller is increased and the time constant of the object is reduced by using cascaded PID controller.[vague]7. Physical implementation of PID controlIn the early history of automatic process control the PID controller was implemented as a mechanical device. These mechanical controllers used a lever, spring and a mass and were often energized by compressed air. These pneumatic controllers were once the industry standard.Electronic analog controllers can be made from a solid-state or tube amplifier, a capacitor and a resistance. Electronic analog PID control loops were often found within more complex electronic systems, for example, the head positioning of a disk drive, the power conditioning of a power supply, or even the movement-detection circuit of a modern seismometer. Nowadays, electronic controllers have largely been replaced by digital controllers implemented with microcontrollers or FPGAs.Most modern PID controllers in industry are implemented in software in programmable logic controllers (PLCs) or as a panel-mounted digital controller. Software implementations have the advantages that they are relatively cheap and are flexible with respect to the implementation of the PID algorithm.PID控制器比例积分微分控制器（PID调节器）是一个控制环，广泛地应用于工业控制系统里的反馈机制。
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International Journal of Advanced Research in Computer Science and Software EngineeringSalim Sunil Kumar Jyoti Ohri机电部机电部机电部(NIT Kurukshetra) (NIT Kurukshetra) (NIT Kurukshetra)印度印度印度基于LabVIEW的直流电机及温度控制PID控制器摘要虚拟仪器是一种图形化的编程软件。
虚拟仪器提供了集成数据和具有灵活性的采集软件/硬件与过程控制应用软件自动化测试和测量应用程序。
在本文中，使用LabVIEW软件作出直流电机的控制过程和计算出速度，设计出一个PID温度控制系统电磁炉。
通过虚拟仪器辅助PID控制器的参数调整来控制电动机转速和控制温度的电磁烤箱。
为了获得最佳的过程响应，设计的控制器采用多种方法分析控制参数和调优参数顺序。
关键词：虚拟仪器，电磁炉的温度控制，PID调节器，PID参数整定方法。
1 引言虚拟仪器是一种计算机仪器系统。
该系统是基于在计算机上的硬件设备及使用者的特定设计的虚拟面板和程序来实现检测和控制的目的。
近年来，虚拟仪器技术已被广泛应用于各个领域，如工业控制，通信，电力自动化，电子和工业生产。
直流电动机已经在工业控制领域流行很长一段时间，因为他们有很多很好的特性，例如：高启动转矩特性，高响应性能，更容易被控制，在线性控制等方面有不同的方法使电机有不同的性能。
直流电动机的基本特性是，速度可以调整，通过不同的端子电压。
PID参数的调整，通过改变不同的方法获得最佳的响应。
本文是PID控制器的设计，监督和控制直流电动机的速度响应，还介绍虚拟仪器图形监控软件LabVIEW 仿真，涉及监管控制系统的设计，建造和展示。
有很多算法/文学调谐的PID 控制器，如反应曲线，齐格勒尼科尔斯的方法，Tyreus Luyben 提出的方法。
Lab-VIEW 的是图形化开发环境，用于创建灵活和可扩展的测试，测量和控制应用的迅速，达到最低的成本。
本文的结构主要分为： 1、直流电动机和电磁炉的数学模型。
2、PID 控制理论和优化算法。
3、实验室软件仿真结果。
2 直流电动机的数学模型及电磁炉直流电动机系统是一个单独的他励直流电动机，这是经常使用的速度调整和位置调整。
等效电路的控制直流电动机的电枢电压控制方法。
()()T b a a Ta K K B Js R s L K s E s s G +++=Ω=)()()(如今电磁炉用于电子制造加热元件组件。
电磁炉放出一定量的热传递电能从一个线圈通过感应到一个金属容器中。
电磁炉的表面温度的调节，通过改变它接收的冷却量。
直接放在电磁炉，使冷空气被迫被迫通过风扇。
从烘箱中热传递的量是在它的气流速度成正比。
该系统监控元件的表面温度，并且通过改变冷却风扇的速度控制。
在烘箱内有电磁灯的电流控制器的热电偶用于执行温度测量。
电磁炉过程的数学模型，可以通过线性传热和系统识别理论研究。
电磁炉过程中的延迟时间，所以具有以下形式的传递函数（施加到加热元件上的电压）从输入到输出（看到的温度由热电偶）是1)ex p()()()(00+-==s sT K s H s P s T d τT0（次）- 观察到的或期望的温度， P （S ）- 电源输入（灯泡）， K - 增益， τ -时间常数， TD-时间延迟，K 、Td 、&、τ的空气流的速度，从而改变了传输滞后从加热器的测量输出，以及不同的传热系数K ，的Td 与τ全部的依赖。
它是在过程控制中的应用非常普遍。
3 PID 控制理论和调整算法PID 控制理论的发展开始于上世纪60年代，PID 控制是一个历史最悠久的控制系统的设计方法。
然而，这种方法仍然广泛用于现在的结构很简单的PID 控制器，它是迄今应用于工业最广泛的控制方法。
主要是调整PID 控制器的一个适当的比例增益（Kp ），积分增益（KL ）和微分增益（KD ），以达到最佳的控制性能。
输入e （t ）和输出u （t ）的关系，dt t de K dt t e K t e K t U DtI p )()()()(0+⎰+=传递函数表示如下：)()()(s E s U S K s K K s C D I p =++=建议调整PID 控制器的方法有很多。
我们已经使用以下四种方法进行调谐。
1、过程反应曲线（开环） 2、齐格勒尼科尔斯方法（闭环） 3、Tyreus 和Luyben过程反应曲线（开环）：这是一个手动过程，它是基于测量系统的阶跃响应。
从变更为输入变量r （t ）的输出c （t ）的仔细监测和步骤响应图表记录仪上记录。
这里R （T ）应尽可能小。
所记录的输出，画切线，启用以下测量：T M R =在KP , KJ , KD , P ，PI ，PID 的实证关系过程反应曲线表1表1 显示过程反应曲线参数齐格勒Nichols方法（闭环）的Ziegler-Nichols的控制器tuning.The长期连续循环的连续循环法是一种类型，指的是连续的具有恒定振幅的振荡和改变比例的试验和错误的过程是基于增益（KP），KP减少直到点在该系统进入不稳定状态，即在哪个点发生持续振荡从更大的价值。
因此，增益系统开始振荡注意到最终收益（KU）和周期振荡是最终的时间段（KU），它使我们能够使用的最终增益值，最终振荡周期（浦）计算两个参数Kc.These，Ku和钚用于查找环路调整常数（P，PI 或PID控制器），用列于表2的配方。
表2显示齐格勒尼科尔斯参数Tyreus Luyben：上述的齐格勒- 尼科尔斯调整参数的修改由Tyreus Luyben所示表3表3 显示Tyreus 和Luyben 的参数4 仿真结果案例1为直流电动机 Z-N 计算模拟结果：一个典型的程序将调整PID 控制器 a ）第一次使用比例控制器的增益为1。
b ）不断增加/减少KC 响应，直到有一些超调。
持续振荡有KC =1.1084c ）计算最终收益（KU ），然后计算出最终的振荡周期。
Ku=1.1084 Pu=0.007表4 齐格勒尼科尔斯模拟结果PID 控制器的传递函数为直流电动机的使用齐格勒Nichols 整定的方法，在下列等式中。
S S S 0114.19066504.00005819.02++=模拟结果为Tyreus和Luyben计算：据Tyreus的Luyben调整方法直流电动机PID增益阶跃响应如图在表5。
和PID 为直流电动机的控制器的传递函数示于下列等式，使用Tyreus Luyben调谐方法S SS715466.3250381.0000559.02++=表5模拟结果PID响应使用Tyreus和Luyben计算。
比例加积分加微分控制的阶跃响应; p值=0.5038中，x=32.7151中，x==0.000559）已观察到，由Tyreus Luyben方法的结果，在更短的振荡在振荡意味着更好的可控性，鲁棒性和敏感性改变系统齐格勒 - 尼科尔斯Tyreus的和的Luyben调整算法和阶跃响应规格控制系统的响应。
表6比较的Ziegler-Nichols和Tyreus Luyben从仿真结果可以得出结论，使用Tyreus的Luyben调整算法的阻尼比，峰值过冲，峰值和相位裕度阶跃响应规格是更好，但所有其他参数几乎一样相比，齐格勒- 尼科尔斯。
所以减少振动控制系统响应调整算法从这里Tyreus Luyben 结果。
减振荡意味着更好的可控性和较低的灵敏度，改变系统状态。
案例2 电磁炉案例（一）采取下列形式：传递函数的电磁炉随时间延迟.如下列方程12)4exp(3)(0+-=s s s H修改齐格勒 - 尼科尔斯调谐的节中给出的方法已被用来调整PID 控制器参数。
随着调谐参数值0,95.0,8.3,8945.1====f D I C T T T K下步的响应时间曲线显示没有PID 控制器参数整定PID 控制器参数整定和上响应显示。
表7显示带和不带PID 控制器的电磁炉的响应值。
的温度控制系统，可以快速实现稳定与稳态误差不超过0.2％和0％，超调量。
表7 响应数据电磁炉因此，我们可以说，该系统能快速达到稳定，在一个短的时间内以高的精度。
案例（二）：因为在LabVIEW 控制设计（CD ）反馈功能正常工作，如果有唯一合理的传递函数的广义一阶标准传输功能在LabVIEW 和Pade 逼近过程中的延迟时间是用来表示时间延迟反馈回路。
在这种情况下，命中和试验方法已被用来调整PID 控制器参数的调谐参数值是KC=1 TI=4，TD= 0，TF=0，K= 3，τ=2，TD =1当我们增加了延迟时间，我们再次用试验方法调整PID控制器。
在这种情况下，我们采取的控制器参数：KC=1，TI= 5，TD= 0，TF=0，K= 3，τ= 11，TD= 4在广义一阶标准传递函数可以通过改变KC，TI和TD的价值，能够看到稳定的响应。
通过以下方式获得，模拟控制系统的阶跃响应分别示出了系统的稳定性。
温度控制系统始终稳定，无PID自整定，除了在一些静态的角度和时间常数。
PID 控制器参数整定的增加，系统性能稳定，但稳定时间增加。
因此，电磁炉的控制系统将具有低的效率与帕德逼近。
5 结论本文PID控制器使用LabVIEW设计的程序图形，使我们对结构和数据有了清晰的了解。
LabVIEW是一种调试工具，这使得更好地控制应用程序。
而MATLAB是更好的数据操作软件。
执行时间和调查能力LabVIEW比MATLAB更好。
因此，我们使用LabVIEW进行直流电机调速。
Tyreus和减少振动控制系统响应Luyben方法的结果。
减振荡意味着更好的可控性和较低的灵敏度，改变系统状态。
这种技术可以扩展到其他类型的电机。
PID控制器的参数可以通过模糊逻辑和遗传算法（GA）进行调整。
该系统的模拟PID温度控制是一个成功的例子。
仿真结果表明，该系统具有温度自动控制等特点。
它还发现标准的一阶系统和一般系统的稳定性之间波动大。
利用LabVIEW专业开发系统或应用程序生成器，它可以建立独立的可执行标准传递函数。
系统的时间已大大降低，与传统仪器构造的系统相比，它具有这样的优点，稳定的温度下，在很短的时间，有良好的性能和灵活性。
因此，虚拟仪器具有良好的灵活性和功能强大的测量系统中的应用程序，这意味着它在设计工业控制系统中是一个不错的选择，具有测量精度高，易于操作，精度高，稳定性较好等。
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