PID中英文对照翻译.docx

1、PID中英文对照翻译中英文互译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 l

2、oops 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

3、 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, a

4、nd 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 th

5、e “bread and butter of control engineering. It is an important component in every control engineers 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

6、 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 feature

7、s 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 s

8、um 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 Ti, and derivative time Td. The

9、 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 differ

10、ent 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 fi

11、gure 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 increa

12、ses 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 actio

13、n 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

14、 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

15、without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2 Derivative 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 reveale

16、d 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。Figure 6.3 Noise filtering and high frequency roll off Set point weighting and 2 DOF Windup Tuning Computer implementationIn the case

17、of the PID controller these issues emerged organically as the technology developed but they are actually important in the implementation of all controllers. Many of these questions are closely related to fundamental properties of feedback, some of them have been discussed earlier in the book.6.3 Fil

18、tering and Set Point WeightingDifferentiation is always sensitive to noise. This is clearly seen from the transfer function G(s) =s of a differentiator which goes to infinity for large s. The following example is also illuminating.where the noise is sinusoidal noise with frequency w. The derivative

19、of the signal isThe signal to noise ratio for the original signal is 1/an but the signal to noise ratio of the differentiated signal is w/an. This ratio can be arbitrarily high if w is large.In a practical controller with derivative action it is there for necessary to limit the high frequency gain o

20、f the derivative term. This can be done by implementing the derivative term as 6.2instead of D=sTdY. The approximation given by (6.2) can be interpreted as the ideal derivative sTd filtered by a first-order system with the time constant Td/N. The approximation acts as a derivative for low-frequency

21、signal components. The gain, however, is limited to KN. This means that high-frequency measurement noise is amplified at most by a factor KN. Typical values of N are 8 to 20.Further limitation of the high-frequency gainThe transfer function from measurement y to controller output u of a PID controll

22、er with the approximate derivative isThis controller has constant gainat high frequencies. It follows from the discussion on robustness against process variations in Section 5.5 that it is highly desirable to roll off the controller gain at high frequencies. This can be achieved by additionallow pas

23、s filtering of the control signal bywhere Tf is the filter time constant and n is the order of the filter. The choice of Tf is a compromise between filtering capacity and performance. The value of T f can be coupled to the controller time constants in the same way as for the derivative filter above.

24、 If the derivative time is used, T f= Td/N is a suitable choice. If the controller is only PI, T f =Ti/N may be suitable.The controller can also be implemented as 6.3This structure has the advantage that we can develop the design methods for an ideal PID controller and use an iterative design proced

25、ure. The controller is first designed for the process P(s). The design gives the controller parameter Td. An ideal controller for the process P(s)/(1+sTd/N)2 is then designed giving a new value of Td etc. Such a procedure will also give a clear picture of the tradeoff between performance and filteri

26、ng.Set Point WeightingWhen using the control law given by （6.1） it follows that a step change in the reference signal will result in an impulse in the control signal. This is often highly undesirable there for derivative action is frequently not applied to the reference signal. This problem can be a

27、voided by filtering the reference value before feeding it to the controller. Another possibility is to let proportional action act only on part of the reference signal. This is called set point weighting. A PID controller given by （6.1） then becomes 6.4where b and c are additional parameter. The int

28、egral term must be based on error feedback to ensure the desired steady state. The controller given by D6.4E has a structure with two degrees of freedom because the signal path from y to u is different from that from r to u. The transfer function from r to u is 6.5 Time tFigure 6.4 Response to a ste

29、p in the reference for systems with different set point weights b= 0 dashed, b = 0.5 full and b=1.0 dash dotted. The process has the transfer function P（s）=1/（s+1）3 and the controller parameters are k = 3, ki = 1.5 and kd = 1.5.and the transfer function from y to u is 6.6Set point weighting is thus

30、a special case of controllers having two degrees of freedom.The system obtained with the controller （6.4） respond to load disturbances and measurement noise in the same way as the controller （6.1） . The response to reference values can be modified by the parameters b and c. This is illustrated in Fi

31、gure 6.4, which shows the response of a PID controller to set-point changes, load disturbances, and measurement errors for different values of b. The figure shows clearly the effect of changing b. The overshoot for set-point changes is smallest for b = 0, which is the case where the reference is onl

32、y introduced in the integral term, and increases with increasing b.The parameter c is normally zero to avoid large transients in the control signal due to sudden changes in the set-point.6.4 Different ParameterizationsThe PID algorithm given by Equation（6.1）can be represented by the transfer function 6.7 6.8 6.9 An interacting controller of the form Equation D6.8E that corresponds to a non-interacting controller can be found only ifThe parameters are then given by 6.10The non-interacting controller given by Eq