Hello
I'm running an FMU model using FMILibrary directly.
The model has a certain size of non-linear system.
I would like to get the information of how many non-linear iteration were needed at every time step. Something similar what Dymola returns when selecting SimulationSetup-->Debug-->Nonlinear Iterations would be nice.
Any idea how to get to that information?
Thanks
Related
I have run ANN in matlab for prediction a variable based on several response variables.ALL variables have numerical values.I could not get a desirable results although I changed hidden neuron several times many runs of the model and so on.My question is should I use transformation of the input variables to get a better results?how can I know that which transformation I should choos?Thanks for any help.
I strongly advise you to use some methods from time series analysis like lagged correlation or window lagged correlation (with statistical tests). You can find it in most of statistical packages (e.g. in R). From one small picture it's hard to deduce whether your prediction is lagged or not. Testing huge amount of data can help you in revealing true dependencies and avoid trusting in spurious correlations.
I'm new to Matlab/Simulink but have a requirement to interface with a Simulink model from a piece of software.
At the moment I have created a simple Simulink function 'Inc' which has 1 input to an addition block with a constant 1 and an output. I have used To/From Workspace blocks and can run the simulation over a fixed time using a time/value vector input, such as simin = [0,0;5,0;5,1;10,1].
What I would like to do is run my model continuously? Reading Workspace values in real-time rather than a pre-defined time based vector?
I can't see how to set this up? Can Matlab/Simulink do this?
To summarise, I would like simin and simout to be single values i.e. simin = 1, then with the model running continuously(infinitely) at the next fixed step simout would update to simout = 2. simin changes would be made at the Workspace at varying intervals.
The short answer is you can't (easily) do that, that's not how Simulink works. The MATLAB workspace is read/accessed at the beginning of the simulation and passed to the Simulink engine, and even if the values in the workspace change before the simulation is finished, this is not taken into account by the Simulink model running.
There are ways to work around this, but if you are a novice to MATLAB & Simulink, be aware that these are fairly advanced techniques, and I would advise to familiarise yourself with Simulink first.
Have a look at these similar questions for suggestions of how to do what you want:
Stream data form MATLAB to Simulink
Problem of variable updating in workspace
How Do I Change a Block Parameter Based on the Output of Another Block?
Tuning block parameters at every time step in a simulation
Simulink Signal Viewing using Event Listeners and a MATLAB UI
Obviously, you need to change the simulation end time to Inf or some large number.
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 believe I am doing something fundamentally wrong when trying to import and test a transfer function in Simulink which was created within the System Identification Toolbox (SIT).
To give a simple example of what I am doing.
I have an input which is an offset sinusoidal wave from 12 seconds to 25 seconds with an amplitude of 1 and a frequency of 1.5rad/s which gives a measured output.
I have used SIT to create a simple 2 pole 1 zero transfer function which gives the following agreement:
I have then tried to import this transfer function into Simulink for investigation in the following configuration which has a sinusoidal input of frequency 1.5rad/s and a starting t=12. The LTI system block refers to the transfer function variable within the workspace:
When I run this simulation for 13 seconds the input to the block is as expected but the post transfer function signal shows little agreement with what would be expected and is an order of magnitude out.
pre:
post:
Could someone give any insight into where I am going wrong and why the output from the tf in simulink shows little resemblance to the model output displayed in the SIT. I have a basic grasp of control theory but I am struggling to make sense of this.
This could be due to different initial conditions used in SimuLink and the SI Toolbox, the latter should estimate initial conditions with the model, while Simulink does nothing special with initial conditions unless you specify them yourself.
To me it seems that your original signals are in periodic regime, since your output looks almost like a sine wave as well. In periodic regime, initial conditions have little effect. You can verify my assumption by simulating your model for a longer amount of time: if at the end, your signal reaches the right amplitude and phase lag as in your data, you will know that the initial conditions were wrong.
In any case, you can get the estimated initial state from the toolbox, I think using the InitialState property of the resulting object.
Another thing that might go wrong, is the time discretization that you use in Simulink in case you estimated a continuous time model (one in the Laplace variable s, not in z or q).
edit: In that case I would recommend you check what Simulink uses to discretize your CT model, by using c2d in MATLAB and a setup like the one below in Simulink. In MATLAB you can also "simulate" the response to a CT model using lsim, where you have to specify a discretization method.
This set-up allows you to load in a CT model and a discretized variant (in this case a state-space representation). By comparing the signals, you can see whether the discretization method you use is the same one that SimuLink uses (this depends on the integration method you set in the settings).
I am trying to run my simulink file which have pid controller connected to s-function block.
When i set three values parameters of pid which are proportional, integral and derivatives it takes too long to run the whole process. Why this is happened?
In the dialog box of my pid diagram, for porportional value, its equal to the value which correspond to the constant amplitude oscillation.
Then for integral, its equal to Kcu/Ti. Ti is the ultimate period(Pu)/2 and
lastly for derivatives, its equal tu Kcu*Td and Td is Pu /8. This is refer to ziegler nichols method. and again my question is why it takes too long to running this file?
MATLAB S-functions are slow because they run in the MATLAB interpreter. Consider implementing it using Simulink blocks or using a "Embedded MATLAB Function" (pre-R2011a) or "MATLAB Function" (R2011a+) block.
Read Guy and Seth's thoughts on Simulation performance.
#Nzbuu is right about the Matlab S-functions.
But I think the problem here could be somewhere else: #Syarina are you saying that the Simulink simulation gets slower after you set the proportional coefficient for the controller? If you simulate the plant alone, in this case the S-function, do you notice a significant difference in the execution speed? If it is really so, I suppose the PID controller makes the ODE system stiff. This means that the different states of the ODE system have really different dynamics - some are very fast, some are very slow. Using an ode-solver that is not suited for stiff equations you will find the simulation much slower (actually you would have luck if it converges at all).
My suggestion is try to change the solver - for example ode15s.