Goal
I'm trying to have a second signal (caused by force applied to a sensor's surface area), once it reaches a set minimum, to determine a servo motor's position. The motor's position is otherwise determined by a first signal (generated by EMGs that are measured with electrodes placed on a subject's body) that make give the servo preset positions when the signal crosses a certain threshold.
Schematic
I cannot seem to get the two signals to work together. Any corrections and recommendations on how to make the logic within Simulink function as intended would be immensely appreciated. I know little of the program and cannot find a good approach.
In your diagram you go from numeric values (Sensors) to logical ones (>=301) back to numeric (*90) and again to logical (AND). What you are doing may be possible but hides the intention. First of all I would recommend you to clearly distinguish between logical conditions and values, similar to the following model. To help you with that you can turn on data-type display from Format > Port/signal display > Signal data types followed by an Update (Ctrl-D).
Alternatively you could reduce the conditions as much as possible and operate only with values, such as
However I do not fully understand your requirements and I'm also not sure what the input of the servo is representing (Speed, position, ...)? Maybe you could explain the desired behaviour a bit more detailed? In particular how do you come up with the limits (301, 300, ...).
Related
I am generation some data whose plots are as shown below
In all the plots i get some outliers at the beginning and at the end. Currently i am truncating the first and the last 10 values. Is there a better way to handle this?
I am basically trying to automatically identify the two points shown below.
This is a fairly general problem with lots of approaches, usually you will use some a priori knowledge of the underlying system to make it tractable.
So for instance if you expect to see the pattern above - a fast drop, a linear section (up or down) and a fast rise - you could try taking the derivative of the curve and looking for large values and/or sign reversals. Perhaps it would help to bin the data first.
If your pattern is not so easy to define but you are expecting a linear trend you might fit the data to an appropriate class of curve using fit and then detect outliers as those whose error from the fit exceeds a given threshold.
In either case you still have to choose thresholds - mean, variance and higher order moments can help here but you would probably have to analyse existing data (your training set) to determine the values empirically.
And perhaps, after all that, as Shai points out, you may find that lopping off the first and last ten points gives the best results for the time you spent (cf. Pareto principle).
I currently work in an experimental rock mechanics lab, and when I conduct an experiment, I record the output signals such as effective torque, normal force and motor velocity. However, the latter quantity causes significant cross-talk over the recorded channels, and I want to filter this out. Let me give an example:
Here the upper plot is the strong signal (motor velocity), and the lower is an idle signal that is affected by the cross-talk (blue is raw signal, red is median filtered). The idle channel is only recording noise. We see three effects here. When the motor voltage changes:
the amplitude of the noise increases
the idle signal's median shifts
there is a spike that lasts approximately 0.1 seconds
If we zoom in on the first spike that occurs at around 115 seconds, we get the following plot. This does not seem to be your typical delta-function type of spike, but rather some kind of electronic "echo".
I have seen much work on blind source separation through independent component analysis (ICA), but that did not prove to be effective in my situation. However, since I know the shape of the signal that is causing the cross-talk, there may be better ways to include this information. My question is this: is there a filter or a combination of filters that can tackle the effects mentioned above?
As I am a geologist and not an electrician or mathematician, I don't have a proper background for this kind of material, so please bear with me. I write Python, MATLAB and C++ quite well, so suggested algorithms written in any of those languages is preferred (but not required).
The crosstalk you encounter, results from a parasitic transmission line. Just think of your typical FM-receiver - where the wires equal the antennae. These effects include parasitic and inductive coupling, and form an oscillator (which is the reason, why you cannot see, the theoretically ideal delta spike)
I recognize two different approaches:
use a hardware filtering circuit
use a software-implemented filter
ad 1:
depending on the needed bandwith (maximum frequency/rate of change) on the idle channel, you can determine the corner frequency, as well as the required filter-order, for a given rate of suppression
ad 2:
you can implement several types of filters (IIF, FIR) which resemble these circuits.
Additionally, if you are measuring the aggressive signal anyways, you can use the measurement on the idle channel to determine system-parameters for a mathematical model of the crosstalk. With this model you'd be able, to exclude the interference by calculation
I am trying to implement the algorithm for estimating the fundamental matrix between two images using RANSAC. So far I have found the interest points using Harris corner detection. I am stuck at computing the putative correspondences using these interest points. I don't want to use matlab toolbox for that , I like to know a way to learn about corresponding point extraction from two images and it's implementation. I have read about block matching but have not completely understood the concept of it. Any samples and guidelines would help me to understand this problem better.
Thanks in advance.
There are many ways to search for corresponding interest points, but they're usually based on describing each of these interest points using the characteristics of the image around them, and, for each point in one image, comparing its surrounding's characteristics to the characteristics of the surroundings of other interest points in the other image.
Now assume you've decided to consider only a squared region (a block) around each point of interest that contains the intensity values of the image around the point. Now you can compare these blocks, and match those that are close to each other. The problem is now how to define "close" or, in other words, how to define the distance metric you'll use to compare these blocks.
There are many approaches, for example, you could use the sum-of-absolute-differences between two blocks, which means you could subtract two blocks, take the absolute value of the resulting block, and then sum all values in this resulting block, obtaining a scalar value which represents how close these blocks are. If this distance is less than a given threshold, you can consider the two blocks a match. This is basically what block matching does.
Similarly, you could define other types of regions to describe your points of interes, for example by changing their shapes, sizes, orientations etc, and create more complex descriptors for these points of interest, which might capture more distinguishable characteristics (which is highly desired if you have the purpose of matching them later).
If you want to learn more about the topic, I think this presentation can get you started:
http://courses.cs.washington.edu/courses/cse455/09wi/Lects/lect6.pdf
First for those, who are not familiar with Simulink, there is a imaginable outside-Simulink partial solution:
I need to create a vector satisfying the following conditions:
known initial value a1
known final value a2
it has a pre-defined step size, but the length is not pre-determined
the first derivative over the whole range is limited to v_max resp. -v_max
the second derivative over the whole range is limited to a_max resp. -a_max
the third derivative over the whole range is limited to j_max resp. -j_max
at the first and the final point all derivatives are zero.
Before you ask "what have you tried so far", I just had the idea to solve it outside Simulink and I tried the whole stuff below ;)
But maybe you guys have a good idea, while I keep working on my own solution.
I'd like to generate smooth ramp signals (3rd derivative limited) based on a trigger signal in Simulink.
To get a triggered step I created a triggered subsystem propagating the trigger output. It looks like that:
But I actually don't want a step, I need a very smooth ramp with limited derivatives up to the 3rd order. The math behind is:
displacement: x
speed: v = x'
acceleration: a = v' = x''
jerk: j = a' = v'' = x'''
(If this looks familiar to you, I once had a very similar question. I thought about a bounty on it, but after the necessary edit of the question both answers would have been invalid)
As there are just rate limiters of 1st order, I used two derivates and a double integration to resolve my problem. But there is a mayor drawback, I can not ignore anymore. For the sake of illustration I chose a relatively big step size of 0.1.
The complete minimal example (Fixed Step, stepsize: 0.1, ode4): Download here
It can be seen, that the signal not even reaches the intended step height of 10 and furthermore is not constant at the end.
Over the development process of my whole model, this approach was satisfactory enough for small step sizes. But I reached the point where I really need the smooth ramp as intended. That means I need a finally constant signal at exactly the value, specified by the step height gain.
I already spent days to resolve the problem, and hope to fine some help here now.
Some of my ideas:
dynamically increase the step height over the actual desired value and saturate the final output. If the rate limits,step height and the simulation step size wouldn't be flexible one could probably find a satisfying solution. But as everything has to be flexible, there are too much cases where the acceleration and jerk limit is violated.
I tried to use the Matlab function block and write my own 3rd order rate limiter. Though it seems possible for me for the trigger moment, I have no solution how to smooth the "deceleration" at the end of the ramp. Also I'd need C-compilers, which would make it hard to use my model on other systems without problems. (At least I think so.)
The solver can not be changed siginificantly (either ode3 or ode4) and a fixed step size is mandatory (0.00001 to 0.01).
Currently used, not really useful approach:
For a dynamic amplification of 1.07 I get the following output (all values normalised on their limits):
Though the displacement looks nice, the violation of the acceleration limit is very harmful.
For a dynamic amplification of 1.05 I get the following output (all values normalised on their limits):
The acceleration stays in its boundaries, but the displacement does not reach the intended value. (not really clear in the picture) The jerk is still to big. (I could live with that, but it's not nice)
So it appears to me that a inside-Simulink solutions is far from reality. Any ideas how to create a well-behaving custom function block?
Simulation step size, step height, and the rate limits are known before the simulation starts. (But I have a lot of these triggered smooth ramps in a row, it should feed a event-discrete control). So I could imagine to create the whole smooth ramp outside simulink and save it as a timeseries object and append it on the current signal when the trigger is activated.
The problems you see are because the difference is not conditioned very well.
Taking the difference amplifies the numerical that exists in your simulation.
Also the jerk will always be large if you try to apply an actual step.
I guess for your approach it would be better to work the other way around:
i.e. make a jerk, acceleration and velocity with which your step is achieved.
I think your looking for something like the ref3 block:
http://www.dct.tue.nl/home_of_ref3.htm
Note the disclaimer on the site and that it is a little cumbersome to use.
An easy (yet to be improved) way is to use a rate limiter and then a state space model with a filter. From the filter you get the velocity, which in turn you can apply a rate limiter to. You continue with rate-limiter and filters until you have the desired curve.
Otherwise you can come up with numerical rate-limiters of higher order using ie runge kutta formulas or finite differences. However it was pointed out, that they may suffer from bad conditioning.
What I usually do is to use one rate limiter and a filter of 3rd Order and just tune the time constant (1 tripple pole), such that my needs are met. This works well, esp
Integrator chains of length > 1 are unstable!
There is a huge field of research dealing with trajectory planning. The easiest way might be to use FIR filters (Biagotti et al) or to implement an online trajectory planner (Ezair et al 2014 / Knierim et al 2012).
I have two datasets at the time (in the form of vectors) and I plot them on the same axis to see how they relate with each other, and I specifically note and look for places where both graphs have a similar shape (i.e places where both have seemingly positive/negative gradient at approximately the same intervals). Example:
So far I have been working through the data graphically but realize that since the amount of the data is so large plotting each time I want to check how two sets correlate graphically it will take far too much time.
Are there any ideas, scripts or functions that might be useful in order to automize this process somewhat?
The first thing you have to think about is the nature of the criteria you want to apply to establish the similarity. There is a wide variety of ways to measure similarity and the more precisely you can describe what you want for "similar" to mean in your problem the easiest it will be to implement it regardless of the programming language.
Having said that, here is some of the thing you could look at :
correlation of the two datasets
difference of the derivative of the datasets (but I don't think it would be robust enough)
spectral analysis as mentionned by #thron of three
etc. ...
Knowing the origin of the datasets and their variability can also help a lot in formulating robust enough algorithms.
Sure. Call your two vectors A and B.
1) (Optional) Smooth your data either with a simple averaging filter (Matlab 'smooth'), or the 'filter' command. This will get rid of local changes in velocity ("gradient") that appear to be essentially noise (as in the ascending component of the red trace.
2) Differentiate both A and B. Now you are directly representing the velocity of each vector (Matlab 'diff').
3) Add the two differentiated vectors together (element-wise). Call this C.
4) Look for all points in C whose absolute value is above a certain threshold (you'll have to eyeball the data to get a good idea of what this should be). Points above this threshold indicate highly similar velocity.
5) Now look for where a high positive value in C is followed by a high negative value, or vice versa. In between these two points you will have similar curves in A and B.
Note: a) You could do the smoothing after step 3 rather than after step 1. b) Re 5), you could have a situation in which a 'hill' in your data is at the edge of the vector and so is 'cut in half', and the vectors descend to baseline before ascending in the next hill. Then 5) would misidentify the hill as coming between the initial descent and subsequent ascent. To avoid this, you could also require that the points in A and B in between the two points of velocity similarity have high absolute values.