I am using Speedgoat Performance P3 with IO334.
I have found an example demonstrating the use of Analog Interface (IO334_ad_da_hdlc.slx).
I have managed to generate HDL code and succesfully ran the model on Speedgoat. In this particular model the sine signals are supposed to be generated on the FPGA (there is internal sine generator inside DUT), send to AO channels and then routed by external wiring back to the AI channels.
However, I dont understand what kind of use is to loopback "data" and "trigger" label in IO334_ad_da_hdlc.slx. Input ports (AD_xx, AD_valid_X_X) and output ports (DA_xx, AD_Trigger_X_X) are mapped to the respective AI and AO interfaces via HDL advisor and external wiring connects signals from AOs to AIs. Can you clarify what is the purpose of this software loopback in the model?I am using Speedgoat Performance P3 with IO334.
I have found an example demonstrating the use of Analog Interface (IO334_ad_da_hdlc.slx).
I have managed to generate HDL code and succesfully ran the model on Speedgoat. In this particular model the sine signals are supposed to be generated on the FPGA (there is internal sine generator inside DUT), send to AO channels and then routed by external wiring back to the AI channels.
However, I dont understand what kind of use is to loopback "data" and "trigger" label in IO334_ad_da_hdlc.slx. Input ports (AD_xx, AD_valid_X_X) and output ports (DA_xx, AD_Trigger_X_X) are mapped to the respective AI and AO interfaces via HDL advisor and external wiring connects signals from AOs to AIs. Can you clarify what is the purpose of this software loopback in the model? I am using Speedgoat Performance P3 with IO334.
I have found an example demonstrating the use of Analog Interface (IO334_ad_da_hdlc.slx).
I have managed to generate HDL code and succesfully ran the model on Speedgoat. In this particular model the sine signals are supposed to be generated on the FPGA (there is internal sine generator inside DUT), send to AO channels and then routed by external wiring back to the AI channels.
However, I dont understand what kind of use is to loopback "data" and "trigger" label in IO334_ad_da_hdlc.slx. Input ports (AD_xx, AD_valid_X_X) and output ports (DA_xx, AD_Trigger_X_X) are mapped to the respective AI and AO interfaces via HDL advisor and external wiring connects signals from AOs to AIs. Can you clarify what is the purpose of this software loopback in the model? speedgoat, hdl coder, real-time MATLAB Answers — New Questions

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Hi all,
I am constructing a NN that nests a Neural ODE. The NN has two datasets as inputs: i) The initial values of internal states (InitialValue) that are used to feed the Neural ODE and, ii) The sequences (input_2) that are included in the NN after the Neural ODE. The input_2 and the outputs of the Neural ODE must be summed.
I tried creating the entries of the NN in cell and dlarray format. For the last one I also defined the dimensions ‘CBT’ and/or ‘CB’ according the structure of the dataserie, nevertheles the problem persists.
The error I got is the following
%% Generate data
data_1 = randn(1,1000);
data_2 = randn(1,1000);
tspan = 1:1:50;
InitialValue = data_1(:,1:end-length(tspan))’;
indices = 1:length(InitialValue);
targets = arrayfun(@(i) data_1(:, i + tspan), indices, ‘UniformOutput’, false)’;
input_2 = arrayfun(@(i) data_2(:, i + tspan), indices, ‘UniformOutput’, false)’;
%% Create neuralnetwork
% NeuralODE layers
OdeLayer = [fullyConnectedLayer(5)
tanhLayer
fullyConnectedLayer(1)];
OdeNet = dlnetwork(OdeLayer,Initialize=false);
% Main layer
net = dlnetwork;
Layers =[featureInputLayer(1,’Name’,’Input 1′)
neuralODELayer(OdeNet,tspan,"Name",’OdeLayer’,’GradientMode’,’adjoint’)];
% add extra input for adition to NeuralODE output
net = addLayers(net, Layers);
net = addLayers(net, sequenceInputLayer(1,’Name’,’Input 2′));
net = addLayers(net, additionLayer(2,’Name’,’adition’));
% connect layers
net = connectLayers(net,’Input 2′,’adition/in2′);
net = connectLayers(net,’OdeLayer’,’adition/in1′);
%% Train Network
% gather inputs and targets
input_1_ds = arrayDatastore(InitialValue,"OutputType","same");
input_2_ds = arrayDatastore(input_2,"OutputType","same");
target_ds = arrayDatastore(targets,"OutputType","same");
cds = combine(input_1_ds, input_2_ds, target_ds);
opt = trainingOptions("adam");
% training
net = trainnet(cds,net,"l2loss",opt);
Thanks in advance for your feedback and comments.Hi all,
I am constructing a NN that nests a Neural ODE. The NN has two datasets as inputs: i) The initial values of internal states (InitialValue) that are used to feed the Neural ODE and, ii) The sequences (input_2) that are included in the NN after the Neural ODE. The input_2 and the outputs of the Neural ODE must be summed.
I tried creating the entries of the NN in cell and dlarray format. For the last one I also defined the dimensions ‘CBT’ and/or ‘CB’ according the structure of the dataserie, nevertheles the problem persists.
The error I got is the following
%% Generate data
data_1 = randn(1,1000);
data_2 = randn(1,1000);
tspan = 1:1:50;
InitialValue = data_1(:,1:end-length(tspan))’;
indices = 1:length(InitialValue);
targets = arrayfun(@(i) data_1(:, i + tspan), indices, ‘UniformOutput’, false)’;
input_2 = arrayfun(@(i) data_2(:, i + tspan), indices, ‘UniformOutput’, false)’;
%% Create neuralnetwork
% NeuralODE layers
OdeLayer = [fullyConnectedLayer(5)
tanhLayer
fullyConnectedLayer(1)];
OdeNet = dlnetwork(OdeLayer,Initialize=false);
% Main layer
net = dlnetwork;
Layers =[featureInputLayer(1,’Name’,’Input 1′)
neuralODELayer(OdeNet,tspan,"Name",’OdeLayer’,’GradientMode’,’adjoint’)];
% add extra input for adition to NeuralODE output
net = addLayers(net, Layers);
net = addLayers(net, sequenceInputLayer(1,’Name’,’Input 2′));
net = addLayers(net, additionLayer(2,’Name’,’adition’));
% connect layers
net = connectLayers(net,’Input 2′,’adition/in2′);
net = connectLayers(net,’OdeLayer’,’adition/in1′);
%% Train Network
% gather inputs and targets
input_1_ds = arrayDatastore(InitialValue,"OutputType","same");
input_2_ds = arrayDatastore(input_2,"OutputType","same");
target_ds = arrayDatastore(targets,"OutputType","same");
cds = combine(input_1_ds, input_2_ds, target_ds);
opt = trainingOptions("adam");
% training
net = trainnet(cds,net,"l2loss",opt);
Thanks in advance for your feedback and comments. Hi all,
I am constructing a NN that nests a Neural ODE. The NN has two datasets as inputs: i) The initial values of internal states (InitialValue) that are used to feed the Neural ODE and, ii) The sequences (input_2) that are included in the NN after the Neural ODE. The input_2 and the outputs of the Neural ODE must be summed.
I tried creating the entries of the NN in cell and dlarray format. For the last one I also defined the dimensions ‘CBT’ and/or ‘CB’ according the structure of the dataserie, nevertheles the problem persists.
The error I got is the following
%% Generate data
data_1 = randn(1,1000);
data_2 = randn(1,1000);
tspan = 1:1:50;
InitialValue = data_1(:,1:end-length(tspan))’;
indices = 1:length(InitialValue);
targets = arrayfun(@(i) data_1(:, i + tspan), indices, ‘UniformOutput’, false)’;
input_2 = arrayfun(@(i) data_2(:, i + tspan), indices, ‘UniformOutput’, false)’;
%% Create neuralnetwork
% NeuralODE layers
OdeLayer = [fullyConnectedLayer(5)
tanhLayer
fullyConnectedLayer(1)];
OdeNet = dlnetwork(OdeLayer,Initialize=false);
% Main layer
net = dlnetwork;
Layers =[featureInputLayer(1,’Name’,’Input 1′)
neuralODELayer(OdeNet,tspan,"Name",’OdeLayer’,’GradientMode’,’adjoint’)];
% add extra input for adition to NeuralODE output
net = addLayers(net, Layers);
net = addLayers(net, sequenceInputLayer(1,’Name’,’Input 2′));
net = addLayers(net, additionLayer(2,’Name’,’adition’));
% connect layers
net = connectLayers(net,’Input 2′,’adition/in2′);
net = connectLayers(net,’OdeLayer’,’adition/in1′);
%% Train Network
% gather inputs and targets
input_1_ds = arrayDatastore(InitialValue,"OutputType","same");
input_2_ds = arrayDatastore(input_2,"OutputType","same");
target_ds = arrayDatastore(targets,"OutputType","same");
cds = combine(input_1_ds, input_2_ds, target_ds);
opt = trainingOptions("adam");
% training
net = trainnet(cds,net,"l2loss",opt);
Thanks in advance for your feedback and comments. neural ode, neural network, deep learning MATLAB Answers — New Questions

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How to highlight those cracks in this image and calculate their lengths?How to highlight those cracks in this image and calculate their lengths? How to highlight those cracks in this image and calculate their lengths? image binary crack length, crack MATLAB Answers — New Questions

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helo
I have problem when use simulation function for quantum. number of qubits is limited by 20-30 qubits. how can imrove this problem by 130 qubits?helo
I have problem when use simulation function for quantum. number of qubits is limited by 20-30 qubits. how can imrove this problem by 130 qubits? helo
I have problem when use simulation function for quantum. number of qubits is limited by 20-30 qubits. how can imrove this problem by 130 qubits? quantum, number of qubit MATLAB Answers — New Questions

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Hello MatLab world.

I have several .tiif files in my hands that I need to access, read and extract certain data from. As it is the first time that I’m handlind .tiff, I need your lights.
I have 102 folders (daily data from 01/07/2019 to 10/10/2019: 102 days=102 folders) . Each folder contains a zipped file with 73 .tiff files. These .tiff files contain hourly temperature data.
First thing I must extract only the first 24 of the 73 as they represent the 24 hourly values of the first day. Second thing, I must access the data these 24 .tiff contain and locate the temperature values I need.

I am copying the few lines of code I have written and please enlighten me:
cd ‘C:ProjectsLIFE ASTIC.3THESSTemperature’
t = Tiff(‘THESSTemperature_20190701T0000Z.tiff’,’r’);
filename = ‘THESSTemperature_20190701T0000Z.tiff’;
imageData = read(t);
imshow(imageData);
info = imfinfo(filename)

Thank you in advance!Hello MatLab world.

I have several .tiif files in my hands that I need to access, read and extract certain data from. As it is the first time that I’m handlind .tiff, I need your lights.
I have 102 folders (daily data from 01/07/2019 to 10/10/2019: 102 days=102 folders) . Each folder contains a zipped file with 73 .tiff files. These .tiff files contain hourly temperature data.
First thing I must extract only the first 24 of the 73 as they represent the 24 hourly values of the first day. Second thing, I must access the data these 24 .tiff contain and locate the temperature values I need.

I am copying the few lines of code I have written and please enlighten me:
cd ‘C:ProjectsLIFE ASTIC.3THESSTemperature’
t = Tiff(‘THESSTemperature_20190701T0000Z.tiff’,’r’);
filename = ‘THESSTemperature_20190701T0000Z.tiff’;
imageData = read(t);
imshow(imageData);
info = imfinfo(filename)

Thank you in advance! Hello MatLab world.

I have several .tiif files in my hands that I need to access, read and extract certain data from. As it is the first time that I’m handlind .tiff, I need your lights.
I have 102 folders (daily data from 01/07/2019 to 10/10/2019: 102 days=102 folders) . Each folder contains a zipped file with 73 .tiff files. These .tiff files contain hourly temperature data.
First thing I must extract only the first 24 of the 73 as they represent the 24 hourly values of the first day. Second thing, I must access the data these 24 .tiff contain and locate the temperature values I need.

I am copying the few lines of code I have written and please enlighten me:
cd ‘C:ProjectsLIFE ASTIC.3THESSTemperature’
t = Tiff(‘THESSTemperature_20190701T0000Z.tiff’,’r’);
filename = ‘THESSTemperature_20190701T0000Z.tiff’;
imageData = read(t);
imshow(imageData);
info = imfinfo(filename)

Thank you in advance! .tiff files MATLAB Answers — New Questions

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Hi, I have more than 200 ‘.txt’ files to process the data. Each file has same number of coloumns, i.e 10, but number of rows vary. Imported data to array variables and created a structure of each file. And further processing data of individual file for analysis.
Hy=importdata(‘hyde0710p1c1.15txt’);
Ii=importdata(‘iisc0710p1c1.15txt’);
save(‘Hy710.txt’,’Hy’); save(‘Ii710.txt’,’Ii’);
hyde_org=struct(‘GPSTOW’,Hy(:,1),’PRN’,Hy(:,2),’EL’,Hy(:,3),’AZ’,Hy(:,4),’IPPMLat’,Hy(:,5),’IPPGeoLong’,Hy(:,6),’IPPGeoLat’,Hy(:,7),’SlantCodeTEC’,Hy(:,8),’SlantSmoothTEC’,Hy(:,9),’VTEC’,Hy(:,10));
iisc_org=struct(‘GPSTOW’,Ii(:,1),’PRN’,Ii(:,2),’EL’,Ii(:,3),’AZ’,Ii(:,4),’IPPMLat’,Ii(:,5),’IPPGeoLong’,Ii(:,6),’IPPGeoLat’,Ii(:,7),’SlantCodeTEC’,Ii(:,8),’SlantSmoothTEC’,Ii(:,9),’VTEC’,Ii(:,10));
But,now I need to append all the data(to append rows one after the other file’s) of 200 files in ta single new file for processing.
Can anyone help please?Hi, I have more than 200 ‘.txt’ files to process the data. Each file has same number of coloumns, i.e 10, but number of rows vary. Imported data to array variables and created a structure of each file. And further processing data of individual file for analysis.
Hy=importdata(‘hyde0710p1c1.15txt’);
Ii=importdata(‘iisc0710p1c1.15txt’);
save(‘Hy710.txt’,’Hy’); save(‘Ii710.txt’,’Ii’);
hyde_org=struct(‘GPSTOW’,Hy(:,1),’PRN’,Hy(:,2),’EL’,Hy(:,3),’AZ’,Hy(:,4),’IPPMLat’,Hy(:,5),’IPPGeoLong’,Hy(:,6),’IPPGeoLat’,Hy(:,7),’SlantCodeTEC’,Hy(:,8),’SlantSmoothTEC’,Hy(:,9),’VTEC’,Hy(:,10));
iisc_org=struct(‘GPSTOW’,Ii(:,1),’PRN’,Ii(:,2),’EL’,Ii(:,3),’AZ’,Ii(:,4),’IPPMLat’,Ii(:,5),’IPPGeoLong’,Ii(:,6),’IPPGeoLat’,Ii(:,7),’SlantCodeTEC’,Ii(:,8),’SlantSmoothTEC’,Ii(:,9),’VTEC’,Ii(:,10));
But,now I need to append all the data(to append rows one after the other file’s) of 200 files in ta single new file for processing.
Can anyone help please? Hi, I have more than 200 ‘.txt’ files to process the data. Each file has same number of coloumns, i.e 10, but number of rows vary. Imported data to array variables and created a structure of each file. And further processing data of individual file for analysis.
Hy=importdata(‘hyde0710p1c1.15txt’);
Ii=importdata(‘iisc0710p1c1.15txt’);
save(‘Hy710.txt’,’Hy’); save(‘Ii710.txt’,’Ii’);
hyde_org=struct(‘GPSTOW’,Hy(:,1),’PRN’,Hy(:,2),’EL’,Hy(:,3),’AZ’,Hy(:,4),’IPPMLat’,Hy(:,5),’IPPGeoLong’,Hy(:,6),’IPPGeoLat’,Hy(:,7),’SlantCodeTEC’,Hy(:,8),’SlantSmoothTEC’,Hy(:,9),’VTEC’,Hy(:,10));
iisc_org=struct(‘GPSTOW’,Ii(:,1),’PRN’,Ii(:,2),’EL’,Ii(:,3),’AZ’,Ii(:,4),’IPPMLat’,Ii(:,5),’IPPGeoLong’,Ii(:,6),’IPPGeoLat’,Ii(:,7),’SlantCodeTEC’,Ii(:,8),’SlantSmoothTEC’,Ii(:,9),’VTEC’,Ii(:,10));
But,now I need to append all the data(to append rows one after the other file’s) of 200 files in ta single new file for processing.
Can anyone help please? batch file .txt file processing MATLAB Answers — New Questions

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I need the visualizations of the HOG descriptor as an image that i can extract regions for my further analysis.I need the visualizations of the HOG descriptor as an image that i can extract regions for my further analysis. I need the visualizations of the HOG descriptor as an image that i can extract regions for my further analysis. hog, hog features, hog visualization MATLAB Answers — New Questions

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I am trying a sample example to glow LED using MCU C2000 LaunchPad F28379D using example simulink model. After compilation program builds and run without any error but there is no pulse at the configured PIN of the MCU. How do i rectify this.I am trying a sample example to glow LED using MCU C2000 LaunchPad F28379D using example simulink model. After compilation program builds and run without any error but there is no pulse at the configured PIN of the MCU. How do i rectify this. I am trying a sample example to glow LED using MCU C2000 LaunchPad F28379D using example simulink model. After compilation program builds and run without any error but there is no pulse at the configured PIN of the MCU. How do i rectify this. c2000, f28379dlaunchpad, simulation MATLAB Answers — New Questions

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One is to compare two solutions and study the error by taking their difference. The two solutions are two surf figures generated by surf(x1,y1,z1) and surf(x2,y2,z2) but x1 and x2 are different in size and so are y1, y2, z1, z2. One can think of this as two solutions z1, z2 obtained on 2 meshes. Is there a smart way of taking z1-z2 and surf’ing it?One is to compare two solutions and study the error by taking their difference. The two solutions are two surf figures generated by surf(x1,y1,z1) and surf(x2,y2,z2) but x1 and x2 are different in size and so are y1, y2, z1, z2. One can think of this as two solutions z1, z2 obtained on 2 meshes. Is there a smart way of taking z1-z2 and surf’ing it? One is to compare two solutions and study the error by taking their difference. The two solutions are two surf figures generated by surf(x1,y1,z1) and surf(x2,y2,z2) but x1 and x2 are different in size and so are y1, y2, z1, z2. One can think of this as two solutions z1, z2 obtained on 2 meshes. Is there a smart way of taking z1-z2 and surf’ing it? surf MATLAB Answers — New Questions

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I am getting the error flag -2 for my nonlinear mpc design despite their being no constraints. If there is no constraints, how is there infeasibility? The only way I can get it to be feasible is to make the intial condition the exact same as the reference. If there is deviation the problem is infeasible

clear all
nx = 4;
ny = 2;
nu = 3;

nlobj = nlmpc(nx,ny,nu);

nlobj.Ts = 0.1;
nlobj.PredictionHorizon = 10;
nlobj.ControlHorizon = 10;

nlobj.Model.StateFcn = @mystateFunctionREDUCED;
nlobj.Model.IsContinuousTime = true;

nlobj.Model.NumberOfParameters = 5;
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;

nlobj.Model.OutputFcn = @OutputFcn
;

x0 = [1;0;pi/8;0];
mv = [m*g+1; 0; 0];
lastMV = mv;
%validateFcns(nlobj, x0, u0, [], {rho,l,g,J,m});
nloptions = nlmpcmoveopt;
nloptions.Parameters = {rho,l,g,J,m};
Duration = 1;
Ts = 0.01;
xHistory = x0′;
yref = [1;pi/9];

%nlobj.Weights.OutputVariables = [3 3];
%nlobj.Weights.ManipulatedVariablesRate = 0.1;
% nlobj.States(1).Min = 0;
% nlobj.States(1).Max = 20;
% nlobj.States(3).Min = 0;
% nlobj.States(3).Max = pi;

%nlobj.Optimization.CustomIneqConFcn = "myIneqConFunctionreduced";
nlobj.Weights.ManipulatedVariablesRate = [0 0 0];
nlobj.Weights.OutputVariables = [1 1];
%nlobj.Weights.ECR = 0;
Tarray = [];
Uarray = [];
for k = 1:(Duration/Ts)

xk = xHistory(k,:);
% Compute optimal control moves
[mv,nloptions,info] = nlmpcmove(nlobj,xk,lastMV,yref’,[],nloptions);
info
%disp(mv)
% Implement first optimal control move
ODEFUN = @(t,xk) statefcn(xk,mv);
[TOUT,XOUT] = ode45(ODEFUN,[0 Ts], xHistory(k,:)’);
x0 = mystateFunctionREDUCED(x0,mv,rho,l,g,J,m)*Ts+x0;
uHistory(k+1,:) = mv’;
lastMV = mv;
%[t,y] = ode15s(@mystateFunction,[0 Ts],[2 0]);
%T = (m*x0(1)*x0(4)^2-m*g*sin(x0(3))+mv(1)*sin(x0(3)+x0(5))-m*rho/l*mv(3))/(1+m*rho^2/l);
%Tarray = [Tarray T];
Uarray = [Uarray mv];
xHistory(k+1,:) = XOUT(end,:);
%opt.Slack0
%T
%par = m*x0(1).*x0(4).^2-m*g*sin(x0(3))
% Save plant states
%xHistory = [xHistory x0];

end

function z = mystateFunctionREDUCED(x,u,rho,l,g,J,m)
z = zeros(4,1);
% rho = params(1);
% l = params(2);
% %T = params(3);
% g = params(3);
% J = params(4);
% m = params(5);
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
end

function y = OutputFcn(x,u,rho,l,g,J,m)
y = zeros(2,1);
y(1) = x(1);
y(2) = x(3);
end

function f = statefcn(in1,in2)
%QuadrotorStateFcn
% F = QuadrotorStateFcn(IN1,IN2)

% This function was generated by the Symbolic Math Toolbox version 24.1.
% 09-Jan-2025 19:28:44
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;
x = in1;
u = in2;
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
f = [z(1);z(2);z(3);z(4)];
endI am getting the error flag -2 for my nonlinear mpc design despite their being no constraints. If there is no constraints, how is there infeasibility? The only way I can get it to be feasible is to make the intial condition the exact same as the reference. If there is deviation the problem is infeasible

clear all
nx = 4;
ny = 2;
nu = 3;

nlobj = nlmpc(nx,ny,nu);

nlobj.Ts = 0.1;
nlobj.PredictionHorizon = 10;
nlobj.ControlHorizon = 10;

nlobj.Model.StateFcn = @mystateFunctionREDUCED;
nlobj.Model.IsContinuousTime = true;

nlobj.Model.NumberOfParameters = 5;
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;

nlobj.Model.OutputFcn = @OutputFcn
;

x0 = [1;0;pi/8;0];
mv = [m*g+1; 0; 0];
lastMV = mv;
%validateFcns(nlobj, x0, u0, [], {rho,l,g,J,m});
nloptions = nlmpcmoveopt;
nloptions.Parameters = {rho,l,g,J,m};
Duration = 1;
Ts = 0.01;
xHistory = x0′;
yref = [1;pi/9];

%nlobj.Weights.OutputVariables = [3 3];
%nlobj.Weights.ManipulatedVariablesRate = 0.1;
% nlobj.States(1).Min = 0;
% nlobj.States(1).Max = 20;
% nlobj.States(3).Min = 0;
% nlobj.States(3).Max = pi;

%nlobj.Optimization.CustomIneqConFcn = "myIneqConFunctionreduced";
nlobj.Weights.ManipulatedVariablesRate = [0 0 0];
nlobj.Weights.OutputVariables = [1 1];
%nlobj.Weights.ECR = 0;
Tarray = [];
Uarray = [];
for k = 1:(Duration/Ts)

xk = xHistory(k,:);
% Compute optimal control moves
[mv,nloptions,info] = nlmpcmove(nlobj,xk,lastMV,yref’,[],nloptions);
info
%disp(mv)
% Implement first optimal control move
ODEFUN = @(t,xk) statefcn(xk,mv);
[TOUT,XOUT] = ode45(ODEFUN,[0 Ts], xHistory(k,:)’);
x0 = mystateFunctionREDUCED(x0,mv,rho,l,g,J,m)*Ts+x0;
uHistory(k+1,:) = mv’;
lastMV = mv;
%[t,y] = ode15s(@mystateFunction,[0 Ts],[2 0]);
%T = (m*x0(1)*x0(4)^2-m*g*sin(x0(3))+mv(1)*sin(x0(3)+x0(5))-m*rho/l*mv(3))/(1+m*rho^2/l);
%Tarray = [Tarray T];
Uarray = [Uarray mv];
xHistory(k+1,:) = XOUT(end,:);
%opt.Slack0
%T
%par = m*x0(1).*x0(4).^2-m*g*sin(x0(3))
% Save plant states
%xHistory = [xHistory x0];

end

function z = mystateFunctionREDUCED(x,u,rho,l,g,J,m)
z = zeros(4,1);
% rho = params(1);
% l = params(2);
% %T = params(3);
% g = params(3);
% J = params(4);
% m = params(5);
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
end

function y = OutputFcn(x,u,rho,l,g,J,m)
y = zeros(2,1);
y(1) = x(1);
y(2) = x(3);
end

function f = statefcn(in1,in2)
%QuadrotorStateFcn
% F = QuadrotorStateFcn(IN1,IN2)

% This function was generated by the Symbolic Math Toolbox version 24.1.
% 09-Jan-2025 19:28:44
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;
x = in1;
u = in2;
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
f = [z(1);z(2);z(3);z(4)];
end I am getting the error flag -2 for my nonlinear mpc design despite their being no constraints. If there is no constraints, how is there infeasibility? The only way I can get it to be feasible is to make the intial condition the exact same as the reference. If there is deviation the problem is infeasible

clear all
nx = 4;
ny = 2;
nu = 3;

nlobj = nlmpc(nx,ny,nu);

nlobj.Ts = 0.1;
nlobj.PredictionHorizon = 10;
nlobj.ControlHorizon = 10;

nlobj.Model.StateFcn = @mystateFunctionREDUCED;
nlobj.Model.IsContinuousTime = true;

nlobj.Model.NumberOfParameters = 5;
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;

nlobj.Model.OutputFcn = @OutputFcn
;

x0 = [1;0;pi/8;0];
mv = [m*g+1; 0; 0];
lastMV = mv;
%validateFcns(nlobj, x0, u0, [], {rho,l,g,J,m});
nloptions = nlmpcmoveopt;
nloptions.Parameters = {rho,l,g,J,m};
Duration = 1;
Ts = 0.01;
xHistory = x0′;
yref = [1;pi/9];

%nlobj.Weights.OutputVariables = [3 3];
%nlobj.Weights.ManipulatedVariablesRate = 0.1;
% nlobj.States(1).Min = 0;
% nlobj.States(1).Max = 20;
% nlobj.States(3).Min = 0;
% nlobj.States(3).Max = pi;

%nlobj.Optimization.CustomIneqConFcn = "myIneqConFunctionreduced";
nlobj.Weights.ManipulatedVariablesRate = [0 0 0];
nlobj.Weights.OutputVariables = [1 1];
%nlobj.Weights.ECR = 0;
Tarray = [];
Uarray = [];
for k = 1:(Duration/Ts)

xk = xHistory(k,:);
% Compute optimal control moves
[mv,nloptions,info] = nlmpcmove(nlobj,xk,lastMV,yref’,[],nloptions);
info
%disp(mv)
% Implement first optimal control move
ODEFUN = @(t,xk) statefcn(xk,mv);
[TOUT,XOUT] = ode45(ODEFUN,[0 Ts], xHistory(k,:)’);
x0 = mystateFunctionREDUCED(x0,mv,rho,l,g,J,m)*Ts+x0;
uHistory(k+1,:) = mv’;
lastMV = mv;
%[t,y] = ode15s(@mystateFunction,[0 Ts],[2 0]);
%T = (m*x0(1)*x0(4)^2-m*g*sin(x0(3))+mv(1)*sin(x0(3)+x0(5))-m*rho/l*mv(3))/(1+m*rho^2/l);
%Tarray = [Tarray T];
Uarray = [Uarray mv];
xHistory(k+1,:) = XOUT(end,:);
%opt.Slack0
%T
%par = m*x0(1).*x0(4).^2-m*g*sin(x0(3))
% Save plant states
%xHistory = [xHistory x0];

end

function z = mystateFunctionREDUCED(x,u,rho,l,g,J,m)
z = zeros(4,1);
% rho = params(1);
% l = params(2);
% %T = params(3);
% g = params(3);
% J = params(4);
% m = params(5);
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
end

function y = OutputFcn(x,u,rho,l,g,J,m)
y = zeros(2,1);
y(1) = x(1);
y(2) = x(3);
end

function f = statefcn(in1,in2)
%QuadrotorStateFcn
% F = QuadrotorStateFcn(IN1,IN2)

% This function was generated by the Symbolic Math Toolbox version 24.1.
% 09-Jan-2025 19:28:44
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;
x = in1;
u = in2;
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
f = [z(1);z(2);z(3);z(4)];
end control, mpc MATLAB Answers — New Questions

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