Control & Design Simulation Collector Problem

i had attempt to work out a collector in control & design simulation loop but i found that there is some problem
1. The data i collect is in horizontal form, how to change the data from horizontal to vertical?
2. The data i collect is not moving for the actual speed, did i collect data from wrong wire? Please correct me if i put wrong.
Thankyou very much
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Attachments:
1.xls ‏7 KB
DC motor control 3.vi ‏403 KB

Hi barp
Thanks for your reply, Erm for the second question, please take a look on the picture below. When the setpoint, desired speed is 500, the Actual Speed suppose osillating at 500 for instance (501, 502, 499), but in this case the actual speed is exactly 500, which is not the actual speed but is the desired speed.
My question is from the block diagram above, am i place the correct wire in the right way in order to collect the actual speed data?
and the solver is Runga-Kutta 1(Euler) is that same with Runga Kutta 1(RK1)?
Thankyou very much.

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Solved: the necessary icon is called: Collector.

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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.
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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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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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Discrete
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x(t) = Ax(t) + Bu(t) + Gw(t)
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Discrete
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N = E{w . vT} – E{w} . ET{v}
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u is the model input vector.
y is the model output vector.
w is the process noise vector.
v is the measurement noise vector.
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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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Control & Design Simulation Collector Problem

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