液位控制系统论文中英文资料对照外文翻译
The liquid level control system based on
dde\matlab\simulink
Process control is an important application field of automatic technology, it is to point to the 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 levels, can affect people's 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, liquid 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, killing 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 a d v a n c e d l e v e l c o n t r o l m e t h o d s a n d s t r a t e g i e s.
The graduation design topic is the liquid level control system based on dde\matlab\simulink\force control, Among them was controlled object for tank level, Communication mode for DDE communications , Matlab is mainly used in the simulation test ,And force control 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 the product quality and quantity, the level and perform effective control is very necessary, The following is a description of all aspects：
一PID controller
A proportional–integral–derivative controller (PID controller) is    a generic .control loop feedback mechanism widely used in industrial control systems.
controller翻译中文
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 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 weighted sum 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 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 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.
1.Control loop basics
A familiar example of a control loop is the action taken to keep one's shower water at the ideal tempera
ture, 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 set point (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 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) 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 w
ould oscillate around the set point 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. 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 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 set 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. 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 theory
Note: 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 term
The 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:
Where
Pout: Proportional output
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 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 term
The 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 output
Ki: Integral Gain, a tuning parameter
e: Error = SP − PV
τ: Ti me 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 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 set point value (cross over the set point 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 term
The 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 output
Kd: Derivative Gain, a tuning parameter
e: Error = SP − PV
t: 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

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