System id: estimated response and simulation mismatch

I'm using the SI Transfer Function Estimation express VI to model a plant transfer function for a system. The real data is a step response, where the stimulus is a flow rate ratio, and the response is the resulting droplet size (it's a fluid emulsion generation system, drop size decreases as pump ratio increases).
My problem comes in when I compare the simulated response data I see in the "preview" during configuration of the express VI, to a plot of the simulated response using the SI Model Simulation.VI - they do not match, the shape of the response is quite different. I'm fairly new to the control design and system id modules, so I assume I'm doing something wrong.
A screenshot of the express vi preview as well as front panel/block diagram is attached, along with the vi and data file.
TIA
Solved!
Go to Solution.
Attachments:
determine TF screenshots.jpg ‏516 KB
determine TF from data.vi ‏137 KB
090506 60deg ratio 2tau.txt ‏35 KB

After puzzling over this for two days, I finally figured out the error on my own, obviously only a few hours after I posted this question! (I should have posted sooner )
For the sake of posterity...
error #1: I'm using the data in an array form, and I forgot to wire in the sampling rate to the SI Model Simulation.vi
error #2: The transfer functions did not match in the express VI preview and on my front panel because I had not specified the realization type in the SI Convert to Transfer Function.vi - default is "minimum" and it was therefore minimizing states. Changing it to full fixed that inconsistency.

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Synchronize Control and Simulation loops

When simulating control systems with LV Control and Simulation loops, I often have multiple loops running at different rates. For example, I have a PWM loop running at 20 kHz, a data acquisition loop running at 100 kHz, and a control loop running at 10 kHz. How can I synchronize all of these loops so that they stay on the same time base? Obviously the master time base will need to be at least as fast as the fastest simulation loop.
I've tried synchronizing all loops to the 1 kHz clock (I'm running on Windows), but each loop runs one period per clock tick (e.g. my 20 kHz loop counts up 50us per clock tick, my 100 kHz loop counts up 10us per clock tick, etc). I need all of the loops to be synchronized to one master time base so the simulation time is identical in each loop, but each loop will be executed at a different rate.
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Go to Solution.

Hello,
A quick suggestion - why can't you run all three systems in a single simulation loop, but have different sample rates for the blocks for each system ?
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www-isc-ltd.com

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Can you send your css file? I tried it on Web Server 7.0 update 3 seems to be working fine.
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--> [
GET /mystyle.css HTTP/1.1^M
Host: ....
User-Agent: Mozilla/5.0 (X11; U; SunOS sun4u; en-US; rv:1.8.1.2) Gecko/20070225
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Control Design and Simulation and DAQ

I'm learning Control Design and Simulations. I have some questions about it.
1. For System Identification use, we can find the transfer function/model of the system by feeding data measurement from the system. But, how long data do we need to be sufficent for Identification System to estimate the model? Until the get saturated value (steady state)? or any other intervals? What if the system is unstable? How will Identification System Toolkit estimate that case?
2. For connecting to hardware, we just connect the clock in the simulation loop to the hardware? And the simulation loop will simulate the blocks inside once it got the trigger/data from the DAQ? what about the step time and solver? Do they follow the timing from hardware? or they will run on their own supplied values/clock?if we use USB DAQ, we can't have any clock from it (as far as I know it's because USB connection is not that stable for clocking since it might be any jitters or delays.) So, how do we configure the timing parameter for USB DAQ?
3. There is possibility to convert model in transfer function to state space, how do we know the states inside that conversion?
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Thanks in advance...

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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I pasted some info about the mathematic part below.
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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Continuous
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Stochastic State-Space Model
Continuous
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y(t) = Cx(t) + Du(t) + Hw(t) + v(t)
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.
N is the cross-covariance matrix between w and v.
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How to use Fuzzy Logic Controller for transfer function in labview control and simulation loop?

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fuzzy in simulation loop.JPG ‏52 KB

Hi Sankhadip,
Sorry for the late response. I was looking at your code and
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DONE

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Attachments:
feedback design1.JPG ‏25 KB
feedback system1.JPG ‏42 KB

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http://www.ni.com/pdf/manuals/371242c.pdf
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System id: estimated response and simulation mismatch

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