Hi,
I am having a bit of bother with a PID controller I am creating on MATLAB. I have the below transfer function for my system, which i’ve to incorporate a PID controller into.
clc
clear all
% Implementing T.F G(s) = 85/(42s+1)
num = [85]; %Numerator of Transfer function (DC Gain k)
den = [42 1]; %Denominator of Transfer function (Time constant tau +1)
Gs = tf([num],[den],’InputDelay’,10) %a transfer function with a lag tim); %transfer function
u = 1; %unit step input
% plotting the graph
figure(1)
[x,y]=step(u*Gs);
plot(y,x);
xlabel(‘time /s’); ylabel(‘Level’);

I have tried using the below code for the PID controller, which doesn’t seem to have the same times. Also on all controllers i’ve done the step response never got to 1 without tuning before.
% Implementing T.F G(s) = 85/(42s+1)
num = [85]; %Numerator of Transfer function (DC Gain k)
den = [42 1]; %Denominator of Transfer function (Time constant tau +1)
Gs= tf(num,den); %TF function
H=[1]; %feedback

M=feedback(Gs,H);
step(M)
grid on

When I add the PID controller script I no longer get anything I expect. I set Kp to 2, which does quicken the response, however I expect some overshoot. I have increased Kp bu different higher values and it only provides quicker respnses without overshoot. This is not how any of my other controllers have operated.
%%
Kp = 2;
Ki = 0;
Kd = 0;

Gc=pid(Kp,Ki,Kd)
Mc=feedback(Gc*Gs,H)
step(Mc)
grid on

I know I am doing something wrong, but can’t see what. Is there anyone that can look this over and assist? I am using Matlab online as I do not have permission to download additional content on my laptop as IT won’t allow it. You may have also noticed that there is no delay added to the scripts. This is due to it not working if I do.Hi,
I am having a bit of bother with a PID controller I am creating on MATLAB. I have the below transfer function for my system, which i’ve to incorporate a PID controller into.
clc
clear all
% Implementing T.F G(s) = 85/(42s+1)
num = [85]; %Numerator of Transfer function (DC Gain k)
den = [42 1]; %Denominator of Transfer function (Time constant tau +1)
Gs = tf([num],[den],’InputDelay’,10) %a transfer function with a lag tim); %transfer function
u = 1; %unit step input
% plotting the graph
figure(1)
[x,y]=step(u*Gs);
plot(y,x);
xlabel(‘time /s’); ylabel(‘Level’);

I have tried using the below code for the PID controller, which doesn’t seem to have the same times. Also on all controllers i’ve done the step response never got to 1 without tuning before.
% Implementing T.F G(s) = 85/(42s+1)
num = [85]; %Numerator of Transfer function (DC Gain k)
den = [42 1]; %Denominator of Transfer function (Time constant tau +1)
Gs= tf(num,den); %TF function
H=[1]; %feedback

M=feedback(Gs,H);
step(M)
grid on

When I add the PID controller script I no longer get anything I expect. I set Kp to 2, which does quicken the response, however I expect some overshoot. I have increased Kp bu different higher values and it only provides quicker respnses without overshoot. This is not how any of my other controllers have operated.
%%
Kp = 2;
Ki = 0;
Kd = 0;

Gc=pid(Kp,Ki,Kd)
Mc=feedback(Gc*Gs,H)
step(Mc)
grid on

I know I am doing something wrong, but can’t see what. Is there anyone that can look this over and assist? I am using Matlab online as I do not have permission to download additional content on my laptop as IT won’t allow it. You may have also noticed that there is no delay added to the scripts. This is due to it not working if I do. Hi,
I am having a bit of bother with a PID controller I am creating on MATLAB. I have the below transfer function for my system, which i’ve to incorporate a PID controller into.
clc
clear all
% Implementing T.F G(s) = 85/(42s+1)
num = [85]; %Numerator of Transfer function (DC Gain k)
den = [42 1]; %Denominator of Transfer function (Time constant tau +1)
Gs = tf([num],[den],’InputDelay’,10) %a transfer function with a lag tim); %transfer function
u = 1; %unit step input
% plotting the graph
figure(1)
[x,y]=step(u*Gs);
plot(y,x);
xlabel(‘time /s’); ylabel(‘Level’);

I have tried using the below code for the PID controller, which doesn’t seem to have the same times. Also on all controllers i’ve done the step response never got to 1 without tuning before.
% Implementing T.F G(s) = 85/(42s+1)
num = [85]; %Numerator of Transfer function (DC Gain k)
den = [42 1]; %Denominator of Transfer function (Time constant tau +1)
Gs= tf(num,den); %TF function
H=[1]; %feedback

M=feedback(Gs,H);
step(M)
grid on

When I add the PID controller script I no longer get anything I expect. I set Kp to 2, which does quicken the response, however I expect some overshoot. I have increased Kp bu different higher values and it only provides quicker respnses without overshoot. This is not how any of my other controllers have operated.
%%
Kp = 2;
Ki = 0;
Kd = 0;

Gc=pid(Kp,Ki,Kd)
Mc=feedback(Gc*Gs,H)
step(Mc)
grid on

I know I am doing something wrong, but can’t see what. Is there anyone that can look this over and assist? I am using Matlab online as I do not have permission to download additional content on my laptop as IT won’t allow it. You may have also noticed that there is no delay added to the scripts. This is due to it not working if I do. pid controller MATLAB Answers — New Questions

​

Hello Everyone,

I have some data as attached. Then I have the code to plot the graph. But I have to convert my file to excel first.

Anyway have that I can plot grpah wothout convert my file to excel?

clc
clear all
close all
spec=readlines(‘nema2.spe’); % read as text
whos spec
data=readmatrix(‘nema2.spe.xlsx’); %the data from point1.prn must export to excel first.
whos data
%then from excel, it can be plotted.
plot(data(:,1),data(:,2))Hello Everyone,

I have some data as attached. Then I have the code to plot the graph. But I have to convert my file to excel first.

Anyway have that I can plot grpah wothout convert my file to excel?

clc
clear all
close all
spec=readlines(‘nema2.spe’); % read as text
whos spec
data=readmatrix(‘nema2.spe.xlsx’); %the data from point1.prn must export to excel first.
whos data
%then from excel, it can be plotted.
plot(data(:,1),data(:,2)) Hello Everyone,

I have some data as attached. Then I have the code to plot the graph. But I have to convert my file to excel first.

Anyway have that I can plot grpah wothout convert my file to excel?

clc
clear all
close all
spec=readlines(‘nema2.spe’); % read as text
whos spec
data=readmatrix(‘nema2.spe.xlsx’); %the data from point1.prn must export to excel first.
whos data
%then from excel, it can be plotted.
plot(data(:,1),data(:,2)) digital image processing, image processing, plot, plotting MATLAB Answers — New Questions

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Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks.Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks. Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks. lsqcurvefit, curve fitting, optimization MATLAB Answers — New Questions

​

I have blast data from 3 different organisms. Two animals and one human set. I am looking to develop a transfer function to translate the data sets from animal set to the human data set. I also have the ambient pressure from outside of the body for normalization as well as weight, speed of the wave, and weight of the individual body parts. I only have acess to the following toolboxes:
MATLAB Version 9.8 (R2020a)
Curve Fitting Toolbox Version 3.5.11 (R2020a)
Deep Learning Toolbox Version 14.0 (R2020a)
GUI Layout Toolbox Version 2.3.6 (R2023a)
Signal Processing Toolbox Version 8.4 (R2020a)
Statistics and Machine Learning Toolbox Version 11.7 (R2020a)

I want to be able to set up some sort of optimizaiton funciton that would work between both of the animal models, independetly. Likely a second order model. I have attached the raw data of the pressures as a CSV. Each of the data sets are computed at 0.8Mhz and the data listed by row is an maximum individual test, not a continous data set.

I am looking for gudiance on how to start this.
>>I have blast data from 3 different organisms. Two animals and one human set. I am looking to develop a transfer function to translate the data sets from animal set to the human data set. I also have the ambient pressure from outside of the body for normalization as well as weight, speed of the wave, and weight of the individual body parts. I only have acess to the following toolboxes:
MATLAB Version 9.8 (R2020a)
Curve Fitting Toolbox Version 3.5.11 (R2020a)
Deep Learning Toolbox Version 14.0 (R2020a)
GUI Layout Toolbox Version 2.3.6 (R2023a)
Signal Processing Toolbox Version 8.4 (R2020a)
Statistics and Machine Learning Toolbox Version 11.7 (R2020a)

I want to be able to set up some sort of optimizaiton funciton that would work between both of the animal models, independetly. Likely a second order model. I have attached the raw data of the pressures as a CSV. Each of the data sets are computed at 0.8Mhz and the data listed by row is an maximum individual test, not a continous data set.

I am looking for gudiance on how to start this.
>> I have blast data from 3 different organisms. Two animals and one human set. I am looking to develop a transfer function to translate the data sets from animal set to the human data set. I also have the ambient pressure from outside of the body for normalization as well as weight, speed of the wave, and weight of the individual body parts. I only have acess to the following toolboxes:
MATLAB Version 9.8 (R2020a)
Curve Fitting Toolbox Version 3.5.11 (R2020a)
Deep Learning Toolbox Version 14.0 (R2020a)
GUI Layout Toolbox Version 2.3.6 (R2023a)
Signal Processing Toolbox Version 8.4 (R2020a)
Statistics and Machine Learning Toolbox Version 11.7 (R2020a)

I want to be able to set up some sort of optimizaiton funciton that would work between both of the animal models, independetly. Likely a second order model. I have attached the raw data of the pressures as a CSV. Each of the data sets are computed at 0.8Mhz and the data listed by row is an maximum individual test, not a continous data set.

I am looking for gudiance on how to start this.
>> transfer function, biologics, curve fitting MATLAB Answers — New Questions

​

Hello together,
I used the function »feedback« to generate a closed-loop system, which worked well. Now I am trying to understand, how this function is mathematically implemented in Matlab. There are a lot of information’s, how to use the function, but I didn’t find anything which describes how it’s working and implemented mathematically. Does this function use iterative every sample from the output and add it to the input?
Thank you!Hello together,
I used the function »feedback« to generate a closed-loop system, which worked well. Now I am trying to understand, how this function is mathematically implemented in Matlab. There are a lot of information’s, how to use the function, but I didn’t find anything which describes how it’s working and implemented mathematically. Does this function use iterative every sample from the output and add it to the input?
Thank you! Hello together,
I used the function »feedback« to generate a closed-loop system, which worked well. Now I am trying to understand, how this function is mathematically implemented in Matlab. There are a lot of information’s, how to use the function, but I didn’t find anything which describes how it’s working and implemented mathematically. Does this function use iterative every sample from the output and add it to the input?
Thank you! feedback, closed loop, control MATLAB Answers — New Questions

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I want to use the function equilibrate, but I don’t understand the following written in Rescaling to Solve a Linear System.
"equilibrate is most useful when the scales of the b and x vectors in the original system x = Ab are irrelevant. However, if the scales of b and x are relevant, then using equilibrate to rescale A only for the linear system solve is not recommended. The obtained solution does not generally yield a small residual for the original system, even if expressed using the original basis vectors."
Could someone explain the meaning of this with an example?I want to use the function equilibrate, but I don’t understand the following written in Rescaling to Solve a Linear System.
"equilibrate is most useful when the scales of the b and x vectors in the original system x = Ab are irrelevant. However, if the scales of b and x are relevant, then using equilibrate to rescale A only for the linear system solve is not recommended. The obtained solution does not generally yield a small residual for the original system, even if expressed using the original basis vectors."
Could someone explain the meaning of this with an example? I want to use the function equilibrate, but I don’t understand the following written in Rescaling to Solve a Linear System.
"equilibrate is most useful when the scales of the b and x vectors in the original system x = Ab are irrelevant. However, if the scales of b and x are relevant, then using equilibrate to rescale A only for the linear system solve is not recommended. The obtained solution does not generally yield a small residual for the original system, even if expressed using the original basis vectors."
Could someone explain the meaning of this with an example? matrices MATLAB Answers — New Questions

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want to find the values of ‘K’ for which the function (D) becomes zero. ‘L’ and ‘a’ are the constants. Code:
syms A B L K a
D = [cosh(K*L)-cos(K*L), sinh(K*L)-sin(K*L); sinh(K*L)+sin(K*L)+K*a*L*(cosh(K*L)+cos(K*L)), cosh(K*L)-cos(K*L)+K*a*L*(sinh(K*L)+sin(K*L))];
d = det(D)
fzero(@(K) d, 10)
Error using fzero (line 309) Function value at starting guess must be finite and real.want to find the values of ‘K’ for which the function (D) becomes zero. ‘L’ and ‘a’ are the constants. Code:
syms A B L K a
D = [cosh(K*L)-cos(K*L), sinh(K*L)-sin(K*L); sinh(K*L)+sin(K*L)+K*a*L*(cosh(K*L)+cos(K*L)), cosh(K*L)-cos(K*L)+K*a*L*(sinh(K*L)+sin(K*L))];
d = det(D)
fzero(@(K) d, 10)
Error using fzero (line 309) Function value at starting guess must be finite and real. want to find the values of ‘K’ for which the function (D) becomes zero. ‘L’ and ‘a’ are the constants. Code:
syms A B L K a
D = [cosh(K*L)-cos(K*L), sinh(K*L)-sin(K*L); sinh(K*L)+sin(K*L)+K*a*L*(cosh(K*L)+cos(K*L)), cosh(K*L)-cos(K*L)+K*a*L*(sinh(K*L)+sin(K*L))];
d = det(D)
fzero(@(K) d, 10)
Error using fzero (line 309) Function value at starting guess must be finite and real. roots, non-polynomial MATLAB Answers — New Questions

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Is code generation supported for "convolution1DLayer"?Is code generation supported for "convolution1DLayer"? Is code generation supported for "convolution1DLayer"? code, generation, convolution1dlayer, convolution, 1d, layer, deep, learning, neural, network, matlab, coder, spatial, temporal MATLAB Answers — New Questions

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I have a nested cell array {48×691}. I would like to isolate an entire column of the nested cell array while grabbing all of the rows such that I would get the nested arrays in this way:
{:,1} {:, 2} {:,3} … {:,691}

In other words, how do I get all of the 691 columns from the nested array extracted such that I grab the 48 rows each time?

Thank you!I have a nested cell array {48×691}. I would like to isolate an entire column of the nested cell array while grabbing all of the rows such that I would get the nested arrays in this way:
{:,1} {:, 2} {:,3} … {:,691}

In other words, how do I get all of the 691 columns from the nested array extracted such that I grab the 48 rows each time?

Thank you! I have a nested cell array {48×691}. I would like to isolate an entire column of the nested cell array while grabbing all of the rows such that I would get the nested arrays in this way:
{:,1} {:, 2} {:,3} … {:,691}

In other words, how do I get all of the 691 columns from the nested array extracted such that I grab the 48 rows each time?

Thank you! indexing nested cell array, nested cell array, indexing MATLAB Answers — New Questions

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I am trying to run a Simulink mode via External Mode. Part of my model is a Protected Model Reference and I am using a release before release MATLAB R2023b.
 
If I attempt to run this model in External Mode it fails with the following error message:

Protected models do not work in External mode or rapid accelerator simulations.
Is it possible to protect a model and still be able to run it in External mode on these releases?I am trying to run a Simulink mode via External Mode. Part of my model is a Protected Model Reference and I am using a release before release MATLAB R2023b.
 
If I attempt to run this model in External Mode it fails with the following error message:

Protected models do not work in External mode or rapid accelerator simulations.
Is it possible to protect a model and still be able to run it in External mode on these releases? I am trying to run a Simulink mode via External Mode. Part of my model is a Protected Model Reference and I am using a release before release MATLAB R2023b.
 
If I attempt to run this model in External Mode it fails with the following error message:

Protected models do not work in External mode or rapid accelerator simulations.
Is it possible to protect a model and still be able to run it in External mode on these releases? protected, external, mode MATLAB Answers — New Questions

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