Table of Contents >Quanser Rapid Control Prototyping Toolkit >VI and Function Reference >VIs - By Category >VI Categories >Discrete VIs and Functions >CL Bias Removal >
Parent Polymorphic VI: | CL Bias Removal |
Requirements: | Quanser Rapid Control Prototyping Toolkit, LabVIEW 2020 or newer, Control Design and Simulation Module |
Removes any bias or offset from a scalar input signal.
x is the input signal for which to remove the bias. |
start time is the time in seconds at which to start measuring the bias in the input. |
stop time is the time in seconds at which to stop measuring the bias in the input and to output the final unbiased result. |
initial value is the value to output while the bias is being measured i.e., prior to the stop time. This input is only used when output during estimation is set to . |
output during estimation determines the value of the x - bias output while the bias is being estimated. See the details section for more information. |
x - bias is the input signal, x, with the bias removed. Prior to the stop time, the value of this output depends on the outputs during estimation setting. |
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bias is the estimated bias in the input signal. |
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done becomes true after the bias has been estimated. In other words, it is true when the time exceeds the stop time. |
This VI is useful for eliminating undesirable offsets in sensor measurements. It estimates the bias in the input signal, x, by computing the average of the input between the start time and stop time. After the stop time it outputs the value of the input signal with the bias removed.
The behaviour of the x - bias output prior to the stop time depends upon the output during estimation setting.
If the output during estimation input is then the output of the VI is the same as the x input until the stop time is reached.
If the output during estimation input is then the output of the VI is the equal to the x input with the bias removed based on the current bias estimate. As the bias estimate improves, the output will get closer to the final unbiased result.
Note that the effectiveness of the bias removal depends largely on the characteristics of the input signal. Since a moving average is used to estimate the bias, there is an implicit assumption that the bias is constant and the input signal is at least periodic (or constant) and only disturbed by Gaussian noise.
All input/output pairs of this function have direct feedthrough behaviour.
RCP CL Bias Removal Example | This example demonstrates the use of RCP CL Bias Removal, which removes bias or offset from an input signal. |
CL Enabled Moving Average | Computes a moving average of the input signal when enabled. |
Target |
Supported |
Comments |
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Yes |
Fully supported. |
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