I am trying to train the rotatry inverted pendulum enviroment using a PPO agent. It’s working… but It’s reaching a limit and not learnign past this limit. I am not too sure why. Newbie to RL here so go easy on me :). I think it’s something to do with the yellow line, Q0. Also it could be reaching a local optima, but I don’t think this is the problem. I think the problem is with Q0 not getting past 100 and the agent not being able to extract more useful info. Hopefully, someone whith a little more experinace has something to say!
mdl = "rlQubeServoModel";
open_system(mdl)

theta_limit = 5*pi/8;
dtheta_limit = 30;
volt_limit = 12;
Ts = 0.005;

rng(22)

obsInfo = rlNumericSpec([7 1]);
actInfo = rlNumericSpec([1 1],UpperLimit=1,LowerLimit=-1);

agentBlk = mdl + "/RL Agent";
simEnv = rlSimulinkEnv(mdl,agentBlk,obsInfo,actInfo);

numObs = prod(obsInfo.Dimension);
criticLayerSizes = [400 300];
actorLayerSizes = [400 300];

% critic:
criticNetwork = [
featureInputLayer(numObs)
fullyConnectedLayer(criticLayerSizes(1), …
Weights=sqrt(2/numObs)*…
(rand(criticLayerSizes(1),numObs)-0.5), …
Bias=1e-3*ones(criticLayerSizes(1),1))
reluLayer
fullyConnectedLayer(criticLayerSizes(2), …
Weights=sqrt(2/criticLayerSizes(1))*…
(rand(criticLayerSizes(2),criticLayerSizes(1))-0.5), …
Bias=1e-3*ones(criticLayerSizes(2),1))
reluLayer
fullyConnectedLayer(1, …
Weights=sqrt(2/criticLayerSizes(2))* …
(rand(1,criticLayerSizes(2))-0.5), …
Bias=1e-3)
];

criticNetwork = dlnetwork(criticNetwork);
summary(criticNetwork)

critic = rlValueFunction(criticNetwork,obsInfo);

% actor:
% Input path layers
inPath = [
featureInputLayer( …
prod(obsInfo.Dimension), …
Name="netOin")
fullyConnectedLayer( …
prod(actInfo.Dimension), …
Name="infc")
];

% Path layers for mean value
meanPath = [
tanhLayer(Name="tanhMean");
fullyConnectedLayer(prod(actInfo.Dimension));
scalingLayer(Name="scale", …
Scale=actInfo.UpperLimit)
];

% Path layers for standard deviations
% Using softplus layer to make them non negative
sdevPath = [
tanhLayer(Name="tanhStdv");
fullyConnectedLayer(prod(actInfo.Dimension));
softplusLayer(Name="splus")
];

net = dlnetwork();
net = addLayers(net,inPath);
net = addLayers(net,meanPath);
net = addLayers(net,sdevPath);
net = connectLayers(net,"infc","tanhMean/in");
net = connectLayers(net,"infc","tanhStdv/in");
plot(net)
net = initialize(net);
summary(net)
actor = rlContinuousGaussianActor(net, obsInfo, actInfo, …
ActionMeanOutputNames="scale",…
ActionStandardDeviationOutputNames="splus",…
ObservationInputNames="netOin");

actorOpts = rlOptimizerOptions(LearnRate=1e-4);
criticOpts = rlOptimizerOptions(LearnRate=1e-4);
agentOpts = rlPPOAgentOptions(…
ExperienceHorizon=600,…
ClipFactor=0.02,…
EntropyLossWeight=0.01,…
ActorOptimizerOptions=actorOpts,…
CriticOptimizerOptions=criticOpts,…
NumEpoch=3,…
AdvantageEstimateMethod="gae",…
GAEFactor=0.95,…
SampleTime=0.1,…
DiscountFactor=0.997);
agent = rlPPOAgent(actor,critic,agentOpts);

trainOpts = rlTrainingOptions(…
MaxEpisodes=20000,…
MaxStepsPerEpisode=600,…
Plots="training-progress",…
StopTrainingCriteria="AverageReward",…
StopTrainingValue=430,…
ScoreAveragingWindowLength=100);

trainingStats = train(agent, simEnv, trainOpts);

thanks in advanced!I am trying to train the rotatry inverted pendulum enviroment using a PPO agent. It’s working… but It’s reaching a limit and not learnign past this limit. I am not too sure why. Newbie to RL here so go easy on me :). I think it’s something to do with the yellow line, Q0. Also it could be reaching a local optima, but I don’t think this is the problem. I think the problem is with Q0 not getting past 100 and the agent not being able to extract more useful info. Hopefully, someone whith a little more experinace has something to say!
mdl = "rlQubeServoModel";
open_system(mdl)

theta_limit = 5*pi/8;
dtheta_limit = 30;
volt_limit = 12;
Ts = 0.005;

rng(22)

obsInfo = rlNumericSpec([7 1]);
actInfo = rlNumericSpec([1 1],UpperLimit=1,LowerLimit=-1);

agentBlk = mdl + "/RL Agent";
simEnv = rlSimulinkEnv(mdl,agentBlk,obsInfo,actInfo);

numObs = prod(obsInfo.Dimension);
criticLayerSizes = [400 300];
actorLayerSizes = [400 300];

% critic:
criticNetwork = [
featureInputLayer(numObs)
fullyConnectedLayer(criticLayerSizes(1), …
Weights=sqrt(2/numObs)*…
(rand(criticLayerSizes(1),numObs)-0.5), …
Bias=1e-3*ones(criticLayerSizes(1),1))
reluLayer
fullyConnectedLayer(criticLayerSizes(2), …
Weights=sqrt(2/criticLayerSizes(1))*…
(rand(criticLayerSizes(2),criticLayerSizes(1))-0.5), …
Bias=1e-3*ones(criticLayerSizes(2),1))
reluLayer
fullyConnectedLayer(1, …
Weights=sqrt(2/criticLayerSizes(2))* …
(rand(1,criticLayerSizes(2))-0.5), …
Bias=1e-3)
];

criticNetwork = dlnetwork(criticNetwork);
summary(criticNetwork)

critic = rlValueFunction(criticNetwork,obsInfo);

% actor:
% Input path layers
inPath = [
featureInputLayer( …
prod(obsInfo.Dimension), …
Name="netOin")
fullyConnectedLayer( …
prod(actInfo.Dimension), …
Name="infc")
];

% Path layers for mean value
meanPath = [
tanhLayer(Name="tanhMean");
fullyConnectedLayer(prod(actInfo.Dimension));
scalingLayer(Name="scale", …
Scale=actInfo.UpperLimit)
];

% Path layers for standard deviations
% Using softplus layer to make them non negative
sdevPath = [
tanhLayer(Name="tanhStdv");
fullyConnectedLayer(prod(actInfo.Dimension));
softplusLayer(Name="splus")
];

net = dlnetwork();
net = addLayers(net,inPath);
net = addLayers(net,meanPath);
net = addLayers(net,sdevPath);
net = connectLayers(net,"infc","tanhMean/in");
net = connectLayers(net,"infc","tanhStdv/in");
plot(net)
net = initialize(net);
summary(net)
actor = rlContinuousGaussianActor(net, obsInfo, actInfo, …
ActionMeanOutputNames="scale",…
ActionStandardDeviationOutputNames="splus",…
ObservationInputNames="netOin");

actorOpts = rlOptimizerOptions(LearnRate=1e-4);
criticOpts = rlOptimizerOptions(LearnRate=1e-4);
agentOpts = rlPPOAgentOptions(…
ExperienceHorizon=600,…
ClipFactor=0.02,…
EntropyLossWeight=0.01,…
ActorOptimizerOptions=actorOpts,…
CriticOptimizerOptions=criticOpts,…
NumEpoch=3,…
AdvantageEstimateMethod="gae",…
GAEFactor=0.95,…
SampleTime=0.1,…
DiscountFactor=0.997);
agent = rlPPOAgent(actor,critic,agentOpts);

trainOpts = rlTrainingOptions(…
MaxEpisodes=20000,…
MaxStepsPerEpisode=600,…
Plots="training-progress",…
StopTrainingCriteria="AverageReward",…
StopTrainingValue=430,…
ScoreAveragingWindowLength=100);

trainingStats = train(agent, simEnv, trainOpts);

thanks in advanced! I am trying to train the rotatry inverted pendulum enviroment using a PPO agent. It’s working… but It’s reaching a limit and not learnign past this limit. I am not too sure why. Newbie to RL here so go easy on me :). I think it’s something to do with the yellow line, Q0. Also it could be reaching a local optima, but I don’t think this is the problem. I think the problem is with Q0 not getting past 100 and the agent not being able to extract more useful info. Hopefully, someone whith a little more experinace has something to say!
mdl = "rlQubeServoModel";
open_system(mdl)

theta_limit = 5*pi/8;
dtheta_limit = 30;
volt_limit = 12;
Ts = 0.005;

rng(22)

obsInfo = rlNumericSpec([7 1]);
actInfo = rlNumericSpec([1 1],UpperLimit=1,LowerLimit=-1);

agentBlk = mdl + "/RL Agent";
simEnv = rlSimulinkEnv(mdl,agentBlk,obsInfo,actInfo);

numObs = prod(obsInfo.Dimension);
criticLayerSizes = [400 300];
actorLayerSizes = [400 300];

% critic:
criticNetwork = [
featureInputLayer(numObs)
fullyConnectedLayer(criticLayerSizes(1), …
Weights=sqrt(2/numObs)*…
(rand(criticLayerSizes(1),numObs)-0.5), …
Bias=1e-3*ones(criticLayerSizes(1),1))
reluLayer
fullyConnectedLayer(criticLayerSizes(2), …
Weights=sqrt(2/criticLayerSizes(1))*…
(rand(criticLayerSizes(2),criticLayerSizes(1))-0.5), …
Bias=1e-3*ones(criticLayerSizes(2),1))
reluLayer
fullyConnectedLayer(1, …
Weights=sqrt(2/criticLayerSizes(2))* …
(rand(1,criticLayerSizes(2))-0.5), …
Bias=1e-3)
];

criticNetwork = dlnetwork(criticNetwork);
summary(criticNetwork)

critic = rlValueFunction(criticNetwork,obsInfo);

% actor:
% Input path layers
inPath = [
featureInputLayer( …
prod(obsInfo.Dimension), …
Name="netOin")
fullyConnectedLayer( …
prod(actInfo.Dimension), …
Name="infc")
];

% Path layers for mean value
meanPath = [
tanhLayer(Name="tanhMean");
fullyConnectedLayer(prod(actInfo.Dimension));
scalingLayer(Name="scale", …
Scale=actInfo.UpperLimit)
];

% Path layers for standard deviations
% Using softplus layer to make them non negative
sdevPath = [
tanhLayer(Name="tanhStdv");
fullyConnectedLayer(prod(actInfo.Dimension));
softplusLayer(Name="splus")
];

net = dlnetwork();
net = addLayers(net,inPath);
net = addLayers(net,meanPath);
net = addLayers(net,sdevPath);
net = connectLayers(net,"infc","tanhMean/in");
net = connectLayers(net,"infc","tanhStdv/in");
plot(net)
net = initialize(net);
summary(net)
actor = rlContinuousGaussianActor(net, obsInfo, actInfo, …
ActionMeanOutputNames="scale",…
ActionStandardDeviationOutputNames="splus",…
ObservationInputNames="netOin");

actorOpts = rlOptimizerOptions(LearnRate=1e-4);
criticOpts = rlOptimizerOptions(LearnRate=1e-4);
agentOpts = rlPPOAgentOptions(…
ExperienceHorizon=600,…
ClipFactor=0.02,…
EntropyLossWeight=0.01,…
ActorOptimizerOptions=actorOpts,…
CriticOptimizerOptions=criticOpts,…
NumEpoch=3,…
AdvantageEstimateMethod="gae",…
GAEFactor=0.95,…
SampleTime=0.1,…
DiscountFactor=0.997);
agent = rlPPOAgent(actor,critic,agentOpts);

trainOpts = rlTrainingOptions(…
MaxEpisodes=20000,…
MaxStepsPerEpisode=600,…
Plots="training-progress",…
StopTrainingCriteria="AverageReward",…
StopTrainingValue=430,…
ScoreAveragingWindowLength=100);

trainingStats = train(agent, simEnv, trainOpts);

thanks in advanced! ppo, rl, machine learning, ml, help, code, deep learning MATLAB Answers — New Questions

​

Hi. I have this file ‘DATA’. The first 3 columns are the nodes in space (X,Y,Z in [m]). The 4th column is not of interest. The fifth column is the velocity in [m/s].
Is it possible to visualize the motion (green direction) of a sphere (e.g., the red one in the figure) moving following the velocity values in the fifth column of DATA?
load("DATA.mat");

figure
plot3(DATA(:,1),DATA(:,2),DATA(:,3),’-k’,’LineWidth’,0.1);
hold on
plot3(DATA(1,1),DATA(1,2),DATA(1,3),’r.’,’MarkerSize’,30);
hold off
axis equal
xlabel(‘x’)
ylabel(‘y’)
zlabel(‘z’)
grid offHi. I have this file ‘DATA’. The first 3 columns are the nodes in space (X,Y,Z in [m]). The 4th column is not of interest. The fifth column is the velocity in [m/s].
Is it possible to visualize the motion (green direction) of a sphere (e.g., the red one in the figure) moving following the velocity values in the fifth column of DATA?
load("DATA.mat");

figure
plot3(DATA(:,1),DATA(:,2),DATA(:,3),’-k’,’LineWidth’,0.1);
hold on
plot3(DATA(1,1),DATA(1,2),DATA(1,3),’r.’,’MarkerSize’,30);
hold off
axis equal
xlabel(‘x’)
ylabel(‘y’)
zlabel(‘z’)
grid off Hi. I have this file ‘DATA’. The first 3 columns are the nodes in space (X,Y,Z in [m]). The 4th column is not of interest. The fifth column is the velocity in [m/s].
Is it possible to visualize the motion (green direction) of a sphere (e.g., the red one in the figure) moving following the velocity values in the fifth column of DATA?
load("DATA.mat");

figure
plot3(DATA(:,1),DATA(:,2),DATA(:,3),’-k’,’LineWidth’,0.1);
hold on
plot3(DATA(1,1),DATA(1,2),DATA(1,3),’r.’,’MarkerSize’,30);
hold off
axis equal
xlabel(‘x’)
ylabel(‘y’)
zlabel(‘z’)
grid off motion, trajectory MATLAB Answers — New Questions

​

I want to supres the warning: "Warning: The specified amount of data was not returned within the Timeout period for ‘readline’.
‘tcpclient’ unable to read any data. For more information on possible reasons, see tcpclient Read Warnings.". I am using tcpipclient and the readline function.I want to supres the warning: "Warning: The specified amount of data was not returned within the Timeout period for ‘readline’.
‘tcpclient’ unable to read any data. For more information on possible reasons, see tcpclient Read Warnings.". I am using tcpipclient and the readline function. I want to supres the warning: "Warning: The specified amount of data was not returned within the Timeout period for ‘readline’.
‘tcpclient’ unable to read any data. For more information on possible reasons, see tcpclient Read Warnings.". I am using tcpipclient and the readline function. tcpipclient, warning MATLAB Answers — New Questions

​

Hello everybody,
I’m trying to implement a genetic algorithm that works with custom data types in MATLAB. Each chromosome of the population is made of two parts: a string and a vector of integers.
The string part is the binary representation of a number and I need to perform admissibility checks over it after crossover and mutation operations in order to avoid that the number represented could exceed a certain value.
I’ve followed this example for working with custom data types but I still have a question: how do I pass the max value as parameter to crossover and mutation functions? And, more generally, is there a way to pass any extra parameter to these functions?
For now I’m using global variables, but this feels a very bad idea and I’d like to find out which is the right way to do what I need.
Thanks in advance!Hello everybody,
I’m trying to implement a genetic algorithm that works with custom data types in MATLAB. Each chromosome of the population is made of two parts: a string and a vector of integers.
The string part is the binary representation of a number and I need to perform admissibility checks over it after crossover and mutation operations in order to avoid that the number represented could exceed a certain value.
I’ve followed this example for working with custom data types but I still have a question: how do I pass the max value as parameter to crossover and mutation functions? And, more generally, is there a way to pass any extra parameter to these functions?
For now I’m using global variables, but this feels a very bad idea and I’d like to find out which is the right way to do what I need.
Thanks in advance! Hello everybody,
I’m trying to implement a genetic algorithm that works with custom data types in MATLAB. Each chromosome of the population is made of two parts: a string and a vector of integers.
The string part is the binary representation of a number and I need to perform admissibility checks over it after crossover and mutation operations in order to avoid that the number represented could exceed a certain value.
I’ve followed this example for working with custom data types but I still have a question: how do I pass the max value as parameter to crossover and mutation functions? And, more generally, is there a way to pass any extra parameter to these functions?
For now I’m using global variables, but this feels a very bad idea and I’d like to find out which is the right way to do what I need.
Thanks in advance! genetic algorithm MATLAB Answers — New Questions

​

Hi, I am trying to dispaly 3 images using the below command. My A, B, C are in the format of 656 x 875 x 3 unit 8. I think the error is because of unit 8 format. I tried imfuse() as well, although it works for two images, but not for three images.
A = imread(‘1.bmp’);
B = imread(‘2.bmp’);
C = imread(‘3.bmp’);
overlay_im = cat(3, A, B, C);
imshow(overlay_im);
However, the above code throws below error
Error in imshow (line 253)
images.internal.imageDisplayParseInputs({‘Parent’,’Border’,’Reduce’},preparsed_varargin{:});

Error in image_comb (line 276)
imshow(overlay_im);Hi, I am trying to dispaly 3 images using the below command. My A, B, C are in the format of 656 x 875 x 3 unit 8. I think the error is because of unit 8 format. I tried imfuse() as well, although it works for two images, but not for three images.
A = imread(‘1.bmp’);
B = imread(‘2.bmp’);
C = imread(‘3.bmp’);
overlay_im = cat(3, A, B, C);
imshow(overlay_im);
However, the above code throws below error
Error in imshow (line 253)
images.internal.imageDisplayParseInputs({‘Parent’,’Border’,’Reduce’},preparsed_varargin{:});

Error in image_comb (line 276)
imshow(overlay_im); Hi, I am trying to dispaly 3 images using the below command. My A, B, C are in the format of 656 x 875 x 3 unit 8. I think the error is because of unit 8 format. I tried imfuse() as well, although it works for two images, but not for three images.
A = imread(‘1.bmp’);
B = imread(‘2.bmp’);
C = imread(‘3.bmp’);
overlay_im = cat(3, A, B, C);
imshow(overlay_im);
However, the above code throws below error
Error in imshow (line 253)
images.internal.imageDisplayParseInputs({‘Parent’,’Border’,’Reduce’},preparsed_varargin{:});

Error in image_comb (line 276)
imshow(overlay_im); imfuse, display multiple images, combine images, imshowpair(), cat MATLAB Answers — New Questions

​

I would like to load a shapefile (.shp) into Roadrunner, and while I can verify that the map appears correctly in QGIS, when I load the .shp and .prj files into Roadrunner, some parts are corrupted and not loaded properly. What could be the cause of this issue?
Thank you
(RoadRunner 2024a)I would like to load a shapefile (.shp) into Roadrunner, and while I can verify that the map appears correctly in QGIS, when I load the .shp and .prj files into Roadrunner, some parts are corrupted and not loaded properly. What could be the cause of this issue?
Thank you
(RoadRunner 2024a) I would like to load a shapefile (.shp) into Roadrunner, and while I can verify that the map appears correctly in QGIS, when I load the .shp and .prj files into Roadrunner, some parts are corrupted and not loaded properly. What could be the cause of this issue?
Thank you
(RoadRunner 2024a) #roadrunner, #shp MATLAB Answers — New Questions

​

hi all, I can not open the mat file (attached) in matlab while using load

Error using load
Unable to read MAT-file . File might be corrupt.

I already checked the ans here: https://www.mathworks.com/matlabcentral/answers/178877-unable-to-read-mat-file-file-may-be-corrupt
but it’s not work for me
Thankshi all, I can not open the mat file (attached) in matlab while using load

Error using load
Unable to read MAT-file . File might be corrupt.

I already checked the ans here: https://www.mathworks.com/matlabcentral/answers/178877-unable-to-read-mat-file-file-may-be-corrupt
but it’s not work for me
Thanks hi all, I can not open the mat file (attached) in matlab while using load

Error using load
Unable to read MAT-file . File might be corrupt.

I already checked the ans here: https://www.mathworks.com/matlabcentral/answers/178877-unable-to-read-mat-file-file-may-be-corrupt
but it’s not work for me
Thanks load MATLAB Answers — New Questions

​

Hi all,
Seeking for you advise if I want to use simulink to do UVM for RTL Verilog verification.
I have seen this from Matlab website.
https://www.mathworks.com/help/hdlverifier/ug/fpga-in-the-loop-fil-simulation.html
Seem like to do this verification, I require FPGA Circuit Board.
Would it be possible I do it without FPGA Board, just load my code adn do the verification through Simulink.

ThanksHi all,
Seeking for you advise if I want to use simulink to do UVM for RTL Verilog verification.
I have seen this from Matlab website.
https://www.mathworks.com/help/hdlverifier/ug/fpga-in-the-loop-fil-simulation.html
Seem like to do this verification, I require FPGA Circuit Board.
Would it be possible I do it without FPGA Board, just load my code adn do the verification through Simulink.

Thanks Hi all,
Seeking for you advise if I want to use simulink to do UVM for RTL Verilog verification.
I have seen this from Matlab website.
https://www.mathworks.com/help/hdlverifier/ug/fpga-in-the-loop-fil-simulation.html
Seem like to do this verification, I require FPGA Circuit Board.
Would it be possible I do it without FPGA Board, just load my code adn do the verification through Simulink.

Thanks transferred MATLAB Answers — New Questions

​

My question is about following example which can be found in documentation:
#include "mex.hpp"
#include "mexAdapter.hpp"

using namespace matlab::data;
using matlab::mex::ArgumentList;

class MexFunction : public matlab::mex::Function {
ArrayFactory factory;
public:
void operator()(ArgumentList outputs, ArgumentList inputs) {
double sm = 0;
const TypedArray<double> inArray = inputs[0];
for (auto& elem : inArray) {
sm += elem;
}
outputs[0] = factory.createScalar(sm);
}
};
How important is to use the keyword "const" in declaration of inArray ? Will there be performance difference if I omit it ? I use following function in MEX module which is called from MexFunction::operator():
void Initialize(TypedArray<double> x, TypedArray<double> y)
{
…
}

void operator()(ArgumentList outputs, ArgumentList inputs)
{
…
Initialize(inputs[1], inputs[2])
…
}
Should I declare the input arguments of Initialize function as const to improve performance ? Problem is that code can be compiled and run without problems but I don’t know what happens under the hood. If possible I want to avoid uneccessary allocations and deallocations. Especially if it is that simple like adding one keyword. In other projects unrelated to MATLAB i use references or pointers where possible if the underlying data type is complex. What is preferred way to pass data in MEX modules ?My question is about following example which can be found in documentation:
#include "mex.hpp"
#include "mexAdapter.hpp"

using namespace matlab::data;
using matlab::mex::ArgumentList;

class MexFunction : public matlab::mex::Function {
ArrayFactory factory;
public:
void operator()(ArgumentList outputs, ArgumentList inputs) {
double sm = 0;
const TypedArray<double> inArray = inputs[0];
for (auto& elem : inArray) {
sm += elem;
}
outputs[0] = factory.createScalar(sm);
}
};
How important is to use the keyword "const" in declaration of inArray ? Will there be performance difference if I omit it ? I use following function in MEX module which is called from MexFunction::operator():
void Initialize(TypedArray<double> x, TypedArray<double> y)
{
…
}

void operator()(ArgumentList outputs, ArgumentList inputs)
{
…
Initialize(inputs[1], inputs[2])
…
}
Should I declare the input arguments of Initialize function as const to improve performance ? Problem is that code can be compiled and run without problems but I don’t know what happens under the hood. If possible I want to avoid uneccessary allocations and deallocations. Especially if it is that simple like adding one keyword. In other projects unrelated to MATLAB i use references or pointers where possible if the underlying data type is complex. What is preferred way to pass data in MEX modules ? My question is about following example which can be found in documentation:
#include "mex.hpp"
#include "mexAdapter.hpp"

using namespace matlab::data;
using matlab::mex::ArgumentList;

class MexFunction : public matlab::mex::Function {
ArrayFactory factory;
public:
void operator()(ArgumentList outputs, ArgumentList inputs) {
double sm = 0;
const TypedArray<double> inArray = inputs[0];
for (auto& elem : inArray) {
sm += elem;
}
outputs[0] = factory.createScalar(sm);
}
};
How important is to use the keyword "const" in declaration of inArray ? Will there be performance difference if I omit it ? I use following function in MEX module which is called from MexFunction::operator():
void Initialize(TypedArray<double> x, TypedArray<double> y)
{
…
}

void operator()(ArgumentList outputs, ArgumentList inputs)
{
…
Initialize(inputs[1], inputs[2])
…
}
Should I declare the input arguments of Initialize function as const to improve performance ? Problem is that code can be compiled and run without problems but I don’t know what happens under the hood. If possible I want to avoid uneccessary allocations and deallocations. Especially if it is that simple like adding one keyword. In other projects unrelated to MATLAB i use references or pointers where possible if the underlying data type is complex. What is preferred way to pass data in MEX modules ? mex, performance, c++ MATLAB Answers — New Questions

​

I used a Arduino Mega to generate PWM pulses with varying duty cycles and frequency 100Hz.

This is the following output on a external logic analyzer connected to the 3, 5 pins. It shows a duty cycle of 20%

I configured an ESP32 WROOM(30 pin) to read the same PWM input using the digital pins with sample time = 0.005. I’m not getting the same input as expected.
Also the PWM inputs appear discontinuous in the simulink logic analyzer

What frequency should I configure such that I get the same input PWM as in the external logic analyzer?I used a Arduino Mega to generate PWM pulses with varying duty cycles and frequency 100Hz.

This is the following output on a external logic analyzer connected to the 3, 5 pins. It shows a duty cycle of 20%

I configured an ESP32 WROOM(30 pin) to read the same PWM input using the digital pins with sample time = 0.005. I’m not getting the same input as expected.
Also the PWM inputs appear discontinuous in the simulink logic analyzer

What frequency should I configure such that I get the same input PWM as in the external logic analyzer? I used a Arduino Mega to generate PWM pulses with varying duty cycles and frequency 100Hz.

This is the following output on a external logic analyzer connected to the 3, 5 pins. It shows a duty cycle of 20%

I configured an ESP32 WROOM(30 pin) to read the same PWM input using the digital pins with sample time = 0.005. I’m not getting the same input as expected.
Also the PWM inputs appear discontinuous in the simulink logic analyzer

What frequency should I configure such that I get the same input PWM as in the external logic analyzer? arduino, pwm, simulink, electric_motor_control, input, esp32, output MATLAB Answers — New Questions

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