液位控制系统的外文翻译(外文原文+中文翻译).doc

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1、Four short words sum up what has lifted most successful individuals above the crowd: a little bit more.-author-date液位控制系统的外文翻译(外文原文+中文翻译)外文翻译外文翻译 The liquid level control system based on ddematlabsimulink Process control is an important application field of automatic technology, it is to point to th

2、e level, temperature, flow control process variables, such as in metallurgy, machinery, chemical, electric power, etc can be widely used. Especially liquid level control technology in real life, played an important role in production, for example, the water supply, civil water tower if low water lev

3、els, can affect peoples lives in water; Industrial enterprises with water, if the drainage water drainage or controlled properly or not, in relation to the workshop of condition; Boiler drum, if the control level boiler is too low, can make level boiler overheating, possible accident; Jing flow, liq

4、uid level control tower control accuracy and level of the craft can influence the quality of the products and the cost, etc. In these production field, are basically labor strength or the operation has certain risk nature of work, extremely prone to accidents caused by operating error, the losses, k

5、illing manufacturer. Visible, in actual production, liquid level control accuracy and control effects directly affect the factory production cost and economic benefit of safety coefficient. Even equipment So, in order to ensure safety, convenient operation, you have to research the development of ad

6、vanced level control methods and strategies. The graduation design topic is the liquid level control system based on ddematlabsimulinkforce control, Among them was controlled object for tank level, Communication mode for DDE communications , Matlab is mainly used in the simulation test ,And force co

7、ntrol software used for modeling, This system mainly through combination of hardware and software device to achieve precise control of liquid level , In modern industry level control of important component, it influence upon production not allow to ignore, in order to ensure safety in production and

8、 the product quality and quantity, the level and perform effective control is very necessary, The following is a description of all aspects：一 PID controllerA proportionalintegralderivative controller (PID controller) is a generic .control loop feedback mechanism widely used in industrial control sys

9、tems. A PID controller attempts to correct the error between a measured process variable and a desired set point by calculating and then outputting a corrective action that can adjust the process accordingly.The PID controller calculation (algorithm) involves three separate parameters; the Proportio

10、nal, 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 weighted sum of these

11、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 respons

12、e 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 set point 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

13、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 action

14、s. 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.1.Control loop basicsA familiar example of a control loop is the action take

15、n to keep ones 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 per

16、son 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 set point (SP). The o

17、utput from the controller and input to the process (the tap position) is called the manipulated variable (MV). The difference between the measurement and the set point 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)

18、 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 b

19、e 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 w

20、ere 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 set point in either a constant, growing, or decaying sinusoid. A human would not do this because we are adaptive controllers, l

21、earning from the process history, but PID controllers do not have the ability to learn and must be set up correctly. Selecting the 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 con

22、troller 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 se

23、t point 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.

24、 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

25、, 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 Sect

26、ion 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

27、.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 output

28、 Kp: Proportional Gain, a tuning parameter e: Error = SP PV t: 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 unsta

29、ble (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 o

30、f 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

31、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 h

32、ave 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 o

33、utput Ki: Integral Gain, a tuning parameter e: Error = SP PV : Time in the past contributing to the integral response The integral term (when added to the proportional term) accelerates the movement of the process towards set point and eliminates the residual steady-state error that occurs with a pr

34、oportional only controller. However, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the set point value (cross over the set point and then create a deviation in the other direction). For further notes regarding integral gain tun

35、ing 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 ma

36、gnitude 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 output Kd: Derivative Gain, a tuning parameter e: Error = SP PV t: Time or instantaneous time (the present) The derivative term slows t

37、he 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, differentiatio

38、n 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

39、 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 the larger the error, the larger the Propor

40、tional 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 inte

41、grated 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.二 Matlab IntroductionThe MATLAB environment is well sui

42、ted to rapid prototyping and application development. The interactive programming environment, built-in math functions, toolboxes, editing and debugging tools, and deployment options all contribute to reducing your overall development time. By using the built-in math functions and the many specializ

43、ed functions contained within our toolboxes, MATLAB can significantly reduce the time it takes you to develop prototypes. In addition to integrated editing and debugging tools, MATLAB provides a performance profiler to help you further optimize your code when programming in MATLAB.Building applicati

44、ons around complex algorithms and graphics is easier than ever with the GUI builder, GUIDE. GUIDE was redesigned in MATLAB 6 to save you time. It offers all the drag and drop interface options you would expect, such as text boxes, radio buttons, check boxes, listboxes, sliders, pop-up menus, frames

45、and more.When youre ready to deploy your application, the MathWorks offers a number of different options that allow you to either convert or interface your MATLAB application to other environments including C/C+ and the Web. MATLAB is the most productive development environment for creating scientif

46、ic and engineering applications because it offers powerful tools for every step in the process to reduce your overall development time.MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems

47、 and solutions are expressed in familiar mathematical notation. Typical uses include Math and computation Algorithm development Data acquisition Modeling, simulation, and prototyping Data analysis, exploration, and visualization Scientific and engineering graphics Application development, including

48、graphical user interface building三DDE Introduction Dynamic data exchange (DDE, Dynamic data exchange) is real-time exchange data between applications, it is the effective method between different applications to share data a agreement. DDE agreement is a kind of open, and language unrelated, based on protocol, it allows multiple applications to any human agreed format data exchange or command. It is application through Shared memory process of the communication between a form, also need not user intervention of good data exchange method.DDE