Zeros-poles-gain configuration

Bonjour;
Je travaille avec LV 2010
- Je n'arrive pas a configurer un terminal d'entrée pour la fonction zero-pole-gain avec un cluster de tableaux (z,p,k) issus d'une identification non-entière.comme illustré dans les pièces jointes.
- Comment faire pour indexer le signal d'entrée dans la boucle de simulation; Faut-il generer un tableau exterieur a la boucle et comment indexer le signal en fonction de " l'itération" comme pour les boucles While ou For.
Merci pour votre aide et à bientot.
Résolu !
Accéder à la solution.
Pièces jointes :
ni.png ‏47 KB
Sim.vi ‏119 KB

Bonjour,
Merci d'avoir posté sur le forum NI.
Quand j'ouvre votre VI je n'ai aucun fil brisés sur la fonction Zero-Pole-Gain. Il est executable.
Pour votre problème d'indexation, vous pouvez utiliser une variable que vous venez incrémenter à chaques tour de la boucle de simulation. Pour cela je conseille d'utiliser une Variable Globale Fonctionnelle comme celle jointe.
Cordialement,
Nicolas M.
National Instruments France
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Journées Techniques LabVIEW 2012 : du 27 septembre au 22 novembre
2 sessions en parallèle : Débuter a...
Pièces jointes :
FGV.vi ‏11 KB

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Hello!
Can any body explain to me why the step-response of the given transferfunction depends on dt?
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Attachments:
Test Zero-Pole1.vi ‏42 KB

Peter,
Yes, the dT influence is present in any simulation accomplished by the computer and even RK4 would suffer because of it. However, the Euler (also know as RK1) has a peculiar characteristic: when the sampling time grows large, because it can only see the next step ahead, the error resulting from this will be too great and it actually will become unstable for dt bigger than 0.8 (for this problem). If you try to use RK 2 or 3, the dT until it gets unstable increases because it can correct with intermediate steps. between major time steps. It works like you increase the number of sampling for your problem. However, if you keep increasing dT, you will find a point were the sampling time will make the solution unstable. The interesting part about using higher order solvers is that the error doesn't affect much the response.
I created this example that shows what I said above. It you will need Control Design and Simulation to use it. The Screenshot shows the difference.
Hope this helps!
Message Edited by Barp on 03-05-2008 08:06 AM
Barp - Control and Simulation Group - LabVIEW R&D - National Instruments
Attachments:
Differences better RKs.vi ‏160 KB
RK.PNG ‏37 KB

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Dear Chin ho,
After going through your questions I found some documentation which will be useful for you. But I you still have more questions, you can reply me any time.
1- About the data and amount of it I couldn't find any info but maybe you can use the new functions in LV version 2009 and find your answer.
Estimating States of Nonlinear Stochastic State-Space Models with Extended Kalman Filters
In previous versions of the LabVIEW Control Design and Simulation Module, you can use the Discrete Kalman Filter function and the Continuous Kalman Filter function to estimate the states of a linear discrete or linear continuous stochastic state-space model, respectively. In the LabVIEW 2009 Control Design and Simulation Module, you can use the Continuous Extended Kalman Filter function and the Discrete Extended Kalman Filter function to estimate the states of a nonlinear continuous or a nonlinear discrete stochastic state-space model, respectively.
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2- I think that I found a pdf document about this part of your question which will be helpful. If you would like to give your email address, I can send it to you.
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The LabVIEW Control Design and Simulation Module provides tools to study the dynamics of systems described by linear time-invariant (LTI) continuous and discrete models. You can create deterministic state-space, transfer function, and zero-pole-gain models. You also can create stochastic state-space models and the second-order statistics noise models. You can use these forms to describe both single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems.
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Deterministic State-Space Model
Continuous
x(t) = Ax("t) + Bu(t)
y(t) = Cx(t) + Du(t)
Discrete
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Stochastic State-Space Model
Continuous
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Discrete
x(k + 1) = Ax(k) + Bu(k) + Gw(k)
y(k) = Cx(k) + Du(k) + Hw(k) + v(k)
Second-Order Statistics Noise Model
Q = E{w . wT} – E{w} . ET{w}
R = E{v . vT} – E{v} . ET{v}
N = E{w . vT} – E{w} . ET{v}
where
t is continuous time.
k is the model sampling time multiplied by the discrete time step, where the discrete time step equals 0, 1, 2, …
x is the model state vector.
u is the model input vector.
y is the model output vector.
w is the process noise vector.
v is the measurement noise vector.
A is an n × n state matrix of the given model.
B is an n × m input matrix of the given model.
C is an r × n output matrix of the given model.
D is an r × m direct transmission matrix of the given model.
n is the number of model states.
m is the number of model inputs.
r is the number of model outputs.
G is a matrix relating w to the model states.
H is a matrix relating w to the model outputs.
Q is the auto-covariance matrix of w.
R is the auto-covariance matrix of v.
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Hi,
try to see the following link: DSP Laboratory: IIR Filter Design via the Bilinear Transformation
http://cnx.rice.edu/content/m13039/latest/
I hope this can help you.
The bilinear function is an alghoritmic transormation native of matlab language, so it has been implemented in other languages.
Best Regards
LencioniM

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Message was edited by: Limnos

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