I am aware that .p files are Matlab protected files. So, I am not trying to access them. However, I was wondering if I could use their output onto the Matlab shell as input to a Matlab program.
What I mean is the following: I have to simulate a dynamic system in Matlab using a controller. Afterwards, I need to assess its performance. This is done by the .p file. Now, the controller behaviour is defined by six distinct variables. I pretty much know their range. So, what I did was create an optimization to find the optimal coefficients. However, when I run the .p file I see that the coefficients I obtained as optimal are in fact not optimal, i.e. my cost function is biased in some way.
So, what I would like to do is to use the output of the .p file (there are always six strings with only two numerical values - so they would be easy to extract if it were a text file) to run a new optimization so that I can understand what I did wrong in my original cost function.
The alternative is finding the parameters starting from my values by trial and error, but considering there are six variables I would prefer a more mathematically pure approach.
Basically, the question is how I can read the output onto the command prompt of a Matlab .p function, and use it as input in a Matlab function.
Thanks for the help!
I want to minimize some function J using gradient information. I found two functions in scipy that may do the job, scipy.optimize.fmin_tnc (here) and scipy.optimize.minimize (here), and I implemented them. However, now I need the stepwise output of the function evaluations at each step of the (e.g. Newton) algorithm to plot its convergence. Is it possible to get this vector somehow out of these functions? It is not part of default return values as it seems.
There is a file called normxcorr2_general on MathWorks here that the author claims always gives correct answers while Matlab's built-in normxcorr2 gives incorrect answers when the two input matrices are close in size. After doing some testing, it is clear that the two functions do give significantly different outputs when the inputs are the same size.
Is normxcorr2_general actually more accurate? I don't have much experience in Matlab and I'm having trouble figuring that out from reading through the function script.
Edit: To clarify, if I understand it correctly then these functions are both implementing equation number (2) in this paper about computing normalized cross-correlations.
Hopefully, I will be able to explain my question well.
I am working on Nonlinear model predictive control implementation.
I have got 3 files:
1). a simulink slx file which is basically a nonlinear pendulum model.
2). A function file, to get the cost function from the simulink model.
3). MPC code.
code snippet of cost function
**simOut=sim('NonlinearPendulum','StopTime', num2str(Np*Ts));**
%Linearly interpolates X to obtain sampled output states at time instants.
T=simOut.get('Tsim');
X=simOut.get('xsim');
xt=interp1(T,X,linspace(0,Np*Ts,Np+1))';
U=U(1:Nu);
%Quadratic cost function
R=0.01;
J=sum(sum((xt-repmat(r,[1 Np+1])).*(xt-repmat(r,[1 Np+1]))))+R*(U-ur)*...
(U-ur)';
Now I take this cost function and optimize it using fmincon to generate a sequence of inputs to be applied to the model, using my MPC code.
A code snippet of my MPC code.
%Constraints -1<=u(t)<=1;
Acons=[eye(Nu,Nu);-eye(Nu,Nu)];
Bcons=[ones(Nu,1);ones(Nu,1)];
options = optimoptions(#fmincon,'Algorithm','active-set','MaxIter',100);
warning off
for a1=1:nf
X=[]; %Prediction output
T=[]; %Prediction time
Xsam=[];
Tsam=[];
%Nonlinear MPC controller
Ubreak=linspace(0,(Np-1)*Ts,Np); %Break points for 1D lookup, used to avoid
% several calls/compilations of simulink model in fmincon.
**J=#(v) pendulumCostFunction(v,x0,ur,r(:,a1),Np,Nu,Ts);**
U=fmincon(J,U0,Acons,Bcons,[],[],[],[],[],options);
%U=fmincon(J,U0,Acons,Bcons);
U0=U;
UUsam=[UUsam;U(1)];%Apply only the first selected input
%Apply the selected input to plant.
Ubreak=[0 Ts]; %Break points for 1D lookup
U=[UUsam(end) UUsam(end)];
**simOut=sim('NonlinearPendulum','StopTime', num2str(Ts));**
In both the codes, I have marked the times we call our simulink model. Now, issue is that to run this whole simulation for just 5 seconds it takes around 7-8 minutes on my windows machine, MATLAB R2014B.
Is there a way to optimize this? As, I am planning to extend this algorithm to 9th order system unlike 2nd order pendulum model.
If, anyone has suggestion on using simulink coder to generate C code:
I have tried that, and the problem I face is that I don't know what to do with the several files generated. Please be as detailed as possible.
From the code snippets, it appears that you are solving a linear time invariant model with a quadratic objective. Here is some MATLAB (and Python) code for an overhead crane pendulum and inverted pendulum, both with state space linear models and quadratic objectives.
One of the ways to make it run faster is to avoid a Simulink interface and a shooting method for solving the MPC. A simultaneous method with orthogonal collocation on finite elements is faster and also enables higher index DAE model forms if you'd like to use a nonlinear model.
I am trying to simulate a time series process using Matlab. For example, let's see the following example:
http://www.mathworks.com/help/econ/arima.print.html
When I run following code
model = arima(1,0,1);
[fit,VarCov] = estimate(model,Y,'print',false);
I get the following error:
??? Undefined function or method 'arima' for input arguments of type 'double'.
Does Matlab contain functions for Matlab? Can I calculate different functions,like calculate autocorrelation or autocovariance at different lag? Or estimate ARMA parameters?
You need to make sure that you have a license and code for the Econometrics Toolbox before using that function. MATLAB has annoying license requirements for both the main software and its various toolboxes :(
But, you may be able to find some public code that can do the same thing. Check out http://www.mathtools.net/