I am looking at the DC-Link capacitor voltage in an inverter model I built, and I'm interested in obtaining the ripple voltage from this waveform. I am not interested in the values of the waveform, but in the difference in peak to peak values. How can I do this in simulink?
Related
I am trying to calculate the trajectory of an IMU sensor by integrating the acceleration twice, but I keep getting distorted data. I would like to know what is maximal resolution I can actually produce from this sensor (MPU9250).
The sensor is operating at a sampling rate of 200Hz
The LSB is 0.6mm/s^2.
The noise covariance matrix of the sensor has the diagonal elements [19.7, 19.7, 52.2] mm/s^2
Assuming that I know the exact orientation of the sensor (using a camera/marker based system for the purpose of debugging), What is the actual highest spatiotemporal resolution I can get with this sensor?
integrated acceleration twice to get trajectory
I've successfully plotted the signal strength coverage map for a generic narrow band (read single frequency) horn antenna using MATLAB's in-built functions design(), txsite() and coverage().
MATLAB uses the 'Longley-Rice' propagation model when terrain data is present which I downloaded and introduced using addCustomTerrain().
However, I don't want my antenna to be narrow band operating at a single frequency.
I want to model the coverage map I would get on location with a known ultra-wide band (UWB) transient pulsed signal. I have the time domain E-field of this waveform as well as the FFT and energy spectral density.
My plan was to loop over many tx antennas, each having an operating frequency equal to one of the ~1000 frequency bins in the UWB spectral content and an output power equal to the scaled energy spectral density (ESD) multiplied by the frequency step size (df) and divided by the total time period of the measured pulsed signal (to get power). P = ESD * (df/T).
However, when I ran this looped code, I got:
"Error using em.EmStructures/savesolution
The calculated result is invalid; possible cause is a coarse mesh. Please consider refining the mesh
manually."
I assume this means MATLAB can't model 1000 different antennas on the same exact location.. but any idea on this error?
Is what I'm trying to do possible in MATLAB?
Are there alternative methods?
Thank you for any help in advance!
do you know maybe where I can find code or example for velocity estimation from IMU (acc+gyro+magnetometer) data. I calculated biases from data where IMU stands still. I want to implement velocity estimation with some kind of filter (Kalman/Complementary) but I can't find any. I also have camera velocity estimation, maybe it can help as some kind of fusion?
Thank you in advance!
Kind regards
I don't have an example code that exactly works for your case. But this approach can help (based on past experience),
Kalman filter:
Decide and formulate the states X, control inputs U, outputs, prediction and observation equations.
Implement/ reuse some implementation of Kalman Filter. Here is a Simulink based implementation for reference.
Set the measurement noise and prediction error variances. It may require some fine tuning later.
Verify that the KF works against some reference. If you have another way to measure velocity, check the KF velocity against it.
States and Control inputs:
States could be a array containing
Linear velocities [Vx, Vy, Vz]
Angular velocities [omega_x, omega_y, omega_z]
Bias in gyroscope. This bias is largely constant but can change with temperature and other factors. Accelerometer measurements will be used by KF to correct for gyro bias.
Bias in Accelerometer. This bias is largely constant but can change with temperature and other factors. Camera velocity will be used by KF to correct for accel bias.
Orientation (Euler angles or quaternion)
Control inputs need not be the actual commands that are being sent to your actuators.
In this case, control inputs can be the net force or net acceleration which is,
Accelerometer data (Which is specific force) + Acceleration due to gravity
Prediction equations:
Prediction equations predict the states for next time step based on current states and control inputs.
This MathWorks documentation has a good reference for prediction equations relevant to IMU.
Observation/measurement model:
Relates measurements with states.
Accel data is already used in prediction. Ignore it here.
Gyro data is [gx, gy, gz] = [omega_x + gyro_bias_x, ....] + errors
One way to handle magnetometer is to obtain yaw angle from it - arctan(y/x) and then use the yaw_mag as measurement.
Camera data is [vx_cam, vy_cam, vz_cam] = [Vx, Vy, Vx] + errors
Finally append all the rows and bring it to Y=C*X + noise form.
Y denotes the measurements from different sensors and X represents the states.
Y would be [gx, gy, gz, yaw_mag, vx,cam, vy_cam, vz_cam] in this case.
Disclaimer: I am a MathWorks employee and links are shared from MathWorks documentation.
Model approach:
I am modelling on Matlab-Simulink a very thin flexible structure. All points of the model are link with each other with springs and dampers this way (without the tethers in the center):
Mesh description
The general equation of my model applied at each point of the mesh is the following:
Dynamic formula of mass/spring/damper system
With k the springs stiffness, and c the dampers damping.
To adapt the physical properties of the material I want to model, the spring stiffness has been set to a very high value, around k = 5000. This mean that my spring links are highly reactive to any deformation.
Problem:
This leads to my problem: High stiffness links induce high frequency displacement that I can consider as noise in the simulation.
The simulation is much slower as the variable time step, I am using must be very low.
This high-frequency displacements (around 160 Hz, which the resonance frequency of the springs) stays all along the simulations.
Here is a simulation of my structure rotating at a constant angular speed:
In-time evolution of a random point of my structure in spherical coordinates
We can see that R is vibrating at a very high frequency. However, the displacement amplitude is clearly negligible.
To speed up the simulation, I want to suppress those vibrations!
Investigation:
To suppress them, I investigate on signal filtering techniques, mainly low-pass filtering. On every loop of our simulation, and what should enter our filter are data of all my points in all my axis.
Simulink low-pass filter block
The continuous version of low-pass filter in Simulink library has been tested on the acceleration, the speed and the position, with several cut-off frequencies from 100 Hz to 500 Hz.
For example, for a cut-off frequency of 200Hz and filtering the position at t=0.6 sec I have:
In-time filtered evolution of a random point of my structure in spherical coordinates
It is an in-plane movement so I don’t have any elevation angle, but azimuth angle and point distance from the center are completely diverging.
The problem might come from:
The fact that I am in a closed-loop system
The fact that for the mesh we have, the filter receives 81 vectors of 3*1 at each time step and maybe the filter block is not made to function with that.
The fact that for the mesh we have, the filter receives 81 vectors of 3*1 at each time step and maybe the filter block is not made to function with that.
Main question:
Are there filtering techniques for closed-loop and multiple inputs system that could solve my problem?
Digital filter designer works with SISO signals. Just demux your signals and apply some lowpass filters. You gave lots of info that made it harder to understand the core problem, if there is anything else you can re-iterate. I'd start with a 3rd order Butterworth LPF wc at around 100Hz for your needs.
I have an acoustic waveform of a Spanish phoneme and I'd like to compute its magnitude spectrum and plot it in dB magnitude on a linear frequency scale. How would I be able to accomplish this in MATLAB?
Thanks
First a quick heads up: At stackoverflow you are expected to show some of your own efforts to solve the problem and then ask for help.
Now to your question:
You can plot the spectrogram using the "spectrogram" Matlab function.
[s,f,t] = spectrogram(x,window,noverlap,f,fs)
Check the details here: https://www.mathworks.com/help/signal/ref/spectrogram.html
For a speech signal you will want to specify the sampling frequency "fs" (you can get that when you read the file using:
[y,Fs] = audioread(filename)
You will probably want to specify the variables "window" and "noverlap" since speech signals can show distinct properties depending on the dimension of the window (fast phenomena will not be visible on big windows ). A typical values are 20ms windows with 10ms overlap (select the best value by considering your sampling frequency and the nearest 2^n value for fast Fourier calculation).
The window size and overlap are also valid when you calculate spectrum. If you apply FFT to the whole waveform then you will get the "average" spectral information for the sentence. To catch specific phenomena you must use windowing techniques and perform a short-term Fourier analysis.
use sptool
Signal Processing toolbox Show its Document