Which Algorithm "pidtune()" function used for finding PID gains?

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Hafiz Hamza
Hafiz Hamza 2024-8-1
评论： Sam Chak 2024-8-1
I am working on tunning the PID gains for a flyback converter. I am curious about what methods out of the popular lmethods ( Ziegler–Nichols, Aström's AMIGO, Skogestad's Internal Model Control, and Chien-Hrones-Reswick) the "pidtune()" function used to give the PID gains.
I also go throw the pidtune.m file using the "edit pidtune" command in the MATLAB script to have a look what is going on inside the pidtune function. After going through several iteration line by line I am still unable to figure out the answer. To me its look like a kind of heuristic algorithm.
I am seeking assistance from all the community.
Regards

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Sam Chak
Sam Chak 2024-8-1
Hi @Hafiz Hamza
The pidtune() command utilizes a proprietary algorithm for tuning the PID gains to achieve a balance between performance and robustness. If you do not specify the desired properties, namely the target phase margin and the closed-loop performance objective, for tuning a PID controller using the pidtuneOptions() command, then the algorithm will determine a crossover frequency based on the given plant dynamics, and then design for a target phase margin of 60 degrees.
https://www.mathworks.com/help/control/getstart/pid-tuning-algorithm.html
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Hafiz Hamza
Hafiz Hamza 2024-8-1
@Sam Chak Thank you for your response. But I am confused here "proprietary algorithm".
What are these algorithms?. Yes I have my system model derived. Could you please elaborate why one should not use Ziegler–Nichols, Aström's AMIGO, Skogestad's Internal Model Control, or Chien-Hrones-Reswick methods when he/she has derived the system matehmatical model.
I shall be gratefull if you share your approach ( intellectual formulas) to determine the PID gains.
Regarding genetic algorithims how would you come up with a objective function if let suppose my system is continuously subjected to varying loads?
I am looking forwrad to your response.
Regards
Sam Chak
Sam Chak 2024-8-1
Ran in:
Hi @Hafiz Hamza
The "proprietary algorithm" could be utilizing the in-house 'fmincon()' algorithm or a similar optimizer to tune the PID gains until the optimization objectives are met (e.g., the target phase margin and the closed-loop performance).
You may have misinterpreted my previous comment. However, you can definitely use any of the Ziegler–Nichols, Åström's AMIGO, Skogestad's Internal Model Control, or Chien-Hrones-Reswick methods to tune the PID gains, even when the Plant's transfer function is known. These autotuning methods are model-free, reactive approaches. In other words, you inject an input signal and measure the output response. From the data analysis, you can obtain the response characteristics such as time constant, dead-time, etc.
For example, if you select the Chien-Hrones-Reswick method, these characteristics serve as the parameters to estimate the PID gains using the specified formulas that guarantee 0% (conservative) and 20% (aggressive) overshoot. No complex control design and stability proof is required!!!
Unfortunately, I am unfamiliar with the flyback converter, so I cannot comment on how to construct the objective function. However, I can share my unpublished PID formulas for 0% overshoot, if that would be helpful.
Gp = tf(20, [1 4.5 64]) % Plant's transfer function (stable)
Gp = 20 ---------------- s^2 + 4.5 s + 64 Continuous-time transfer function.
Ts = 0.561; % Desired Settling Time of the Closed-loop System
[Gc, Gh] = zeropid(Gp, Ts) % Scroll down to the end of the script
Gc = 1 Kp + Ki * --- + Kd * s s with Kp = 4.86, Ki = 17, Kd = 0.349 Continuous-time PID controller in parallel form. Gh = 0.821 s^2 + 4.094 s + 16.95 ---------------------------- 0.3487 s^2 + 4.863 s + 16.95 Continuous-time transfer function.
Gcl = feedback(Gc*Gp, Gh) % Closed-loop System
Gcl = 2.431 s^4 + 67.82 s^3 + 709.4 s^2 + 3298 s + 5749 ------------------------------------------------------------- 0.3487 s^5 + 12.16 s^4 + 169.5 s^3 + 1182 s^2 + 4122 s + 5749 Continuous-time transfer function.
S = stepinfo(Gcl) % Performances
S = struct with fields:
RiseTime: 0.3150 TransientTime: 0.5610 SettlingTime: 0.5610 SettlingMin: 0.9066 SettlingMax: 0.9999 Overshoot: 0 Undershoot: 0 Peak: 0.9999 PeakTime: 1.2900
step(Gcl, round(4*Ts, 0)), hold on
step(Gp), grid on, ylim([-0.2, 1.2])
xline(S.SettlingTime, '--', sprintf('Settling Time: %.3f sec', S.SettlingTime), 'color', '#7F7F7F', 'LabelVerticalAlignment', 'bottom')
legend('Closed-loop', 'Open-loop Plant', 'location', 'east')
%% The 0% overshoot method
function [C, H] = zeropid(P, Ts)
% Gp is a stable 2nd-order Plant
% Ts is the desired settling time
[numP, denP] = tfdata(minreal(P), 'v');
a1 = denP(2);
a2 = denP(3);
b = numP(3);
Tc = -log(0.02)/Ts; % Desired time constant
% Formulas
k1 = (3*(b^2)*((Tc/b)^2) - a2)/b;
k2 = (b^2)*((Tc/b)^3);
k3 = (3*b*(Tc/b) - a1)/b;
k4 = 2*b*((Tc/b)^2); % P-gain of PID controller
k5 = k2; % I-gain of PID controller
k6 = Tc/b; % D-gain of PID controller
% PID controller and the Compensator
C = pid(k4, k5, k6); % PID control in the forward path
H = tf([k3 k1 k2], [k6 k4 k5]); % Compensator in the feedback path
end

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