Learning PID Tuning I

8/1/2019 Learning PID Tuning I

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Learning PID Tuning I: Process Reaction Curve

Most PID tuning rules are based on the assumption that the plant can be approaximated by a first-order plus time delay system.

This code explains why this assumption is valid and how to identify such an approximation model. Finaly, we use a 4th-order

example to show how this can be done and compare it with oscilation based tuning approach.

Contents

Step Response

Maximum Slope of Step Responses

Approximation of the 4th Order System using the Maximum Slope Line

Process Reaction Curve Approximation

PID Tuning

The ITAE Tuning Rule

Closed-Loop Response Comparison

Step Response

Consider a typical first order system,

and a fourth order system

Their step responses are generated using the MATLAB Control System Toolbox as follows.

G = tf(1,[1 1]);

subplot(221)

step(G)

G2 = tf(1, [1 4 6 4 1]);

subplot(222)

step(G2)
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Maximum Slope of Step Responses

The difference is that the response of the first order system has the maximum response slope at the t=0, whilst that of the 4 th

order system is at t>0. This difference is true for all high-order (>1) systems.

The maximum slope is the maximum reseponse speed. For a first order system, if we assume the system can keep the maximum

resppnse speed all the time, then the system will take exact the time of the time constant to reach its steady-state. Therefore, the

time constant can be identified by taking the maximum slope and measuring the time period between the points where the the

maximum slope line accrosses the initial and final response lines.

subplot(223)

step(G)

hold

plot([0 1],[0 1],'Linewidth',2)

plot([1 1],[0 1],':')

set(gca,'Xtick',1)

Current plot held


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Approximation of the 4th Order System using the Maximum Slope Line

To approximate the 4th order system, we wish to keep the maximum response speed being the same between the actual system

and the approximated first-order plus time delay system. Therefore, this leads to the so called process reaction curve approach to

identify the approaximation.

[y,t]=step(G2);

% The maximum response speed and the corresponding time point

[dydt,idx]=max(diff(y)./diff(t));

% The crossing point with the initial line

t0=t(idx)-y(idx)/dydt;

% The crossing point with the steady state line

t1=t(idx)+(1-y(idx))/dydt;

% plot the step response with the maximum slope

subplot(224)

plot(t,y,'-',[t0 t1],[0 1],'r--','Linewidth',2)


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Process Reaction Curve Approximation

The step response is termed as the Process Reaction Curve in process. However, manually to draw the maximum slope on a

Process Reaction Curve is neither accurate nor convinient. The submission of Process Reaction Curve provides a tool to get the

first-order plus time delay approaximate model directly from the supplied step response data (Process Reaction Curve).

Let us apply this function to the step response of the 4th order system, then compare how good of the approximation is.

[model,controller]=ReactionCurve(t,y);

fprintf('Process gain: %g, Time constant: %g, Time delay: %g\n',model.gain,

model.time_constant, model.time_delay)

% We can compare how good the approximation is.

figure

Ga = tf(model.gain,[model.time_constant 1]);

set(Ga,'InputDelay',model.time_delay')

step(Ga)

hold

plot(t,y,'--','Linewidth',2)

legend('approximation','Process Reaction Curve')


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% This example shows that the approximation matches the maximum response

% speed well but overall response speed is slower than original system.

% This is the general behaviour of this approach.

Process gain: 0.998794, Time constant: 4.45908, Time delay: 1.42509

Current plot held


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PID Tuning

There are many PID tuning rules around for first-order plus time delay systems. The following tuning table was derived by Ziegler-

Nichols to provide a quarter decay ratio (the ratio of the second peak over the first peak). (alpha: time delay, tau: time constant,

Kp: gain)

Controller Kc Ti Td

P tau/(Kp*alpha)

PI 0.9*tau/(Kp*alpha) 3.33*alpha

PID 1.2*tau/(Kp*alpha) 2*alpha 0.5*alpha

For the 4th order example, the corresponding controller is derived by the ReactionCurve function as follows:

Controller Kc Ti Td

P 2.4381

PI 2.194 6.929

PID 2.929 4.2 1.0497


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The ITAE Tuning Rule

For comparison, the minimum ITAE approximate model controller tuning rules (for setpoint tracking) are presented in the following

table.

Controller Kc Ti Td

PI 0.586/Kp*(tau/alpha)^0.916 tau/(1.03-0.165*alpha/tau)

PID 0.965/Kp*(tau/alpha)^0.855 tau/(0.796-0.147*alpha/tau) 0.308*tau*(alpha/tau) 0.929

For the 4th order example, the ITAE PI and PID controllers are:

Controller Kc Ti Td

PI 1.6681 4.5628

PID 2.5622 5.9532 0.4760

Closed-Loop Response Comparison

Let us take the PID controller derived above.

K = controller.PID;

% Connect it with the 4th-order system to form a closed-loop system.

T = feedback(G2*K,1);

% The closed-loop response to a step input is as follows.

[y,t]=step(T);

% Compare it with the ITAE PID controller derived as above

%

k=znpidtuning(G2,3);

K2=2.5622*(1+tf(1,[5.9532 0])+tf([0.4760 0],1));

T2=feedback(G2*K2,1);

y2=step(T2,t);

figure

plot(t,y,'-',t,y2,'--','Linewidth',2)

grid

legend('Approximate Model Tuning','ITAE Tuning')

% Clearly, the ITAE tuning rule gives much better result.


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Published with MATLAB 7.5

Documents

Learning PID Tuning I