FREAC BAD SAMPLING RATE HOW TO
FREAC BAD SAMPLING RATE SOFTWARE
FREAC BAD SAMPLING RATE CODE

We define the error ε( t ) as the difference between the real sensor value and the value used by the control algorithm at any time, t. The maximum error is a function of the sampling rate. The sampling rate directly affects the temporal resolution of the input signal, much in the same way as the number of bits of resolution in the ADC affects the spatial resolution. The sampling rate refers to the number of times the data is read from the ADC and passed along to other application components that use the data. While the approach can be used for most types of sensors, in this article we focus only on analog inputs.Īnalog inputs provide data to the processor through an analog-to-digital converter (ADC). This method combines experimental measurement with analytical understanding of the application's needs, in order to engineer a good solution.

Prioritize the trade-offs, to determine a suitable sampling rate between the computed lower and upper bounds.

Identify the trade-offs between using the lower and upper bound rates.

Compute the lower and upper bound for sampling rates based on function alone.

If there is noise in the input, select the algorithm that will be used to filter the data.

Measure sensor characteristics of application.

FREAC BAD SAMPLING RATE HOW TO

The truth is there's usually no best answer for all systems, but there's often one answer that stands out as better than most others when the peculiarities of a specific application and the target hardware are considered.In this article, I show how to systematically identify a set of good sampling rates through a combination of experimentation and mathematical analysis, and I discuss how to select the right value from that set given the real-time requirements of the application.Here's a systematic approach to determining sampling rate:

Sample at a rate that's a multiple of the control algorithm frequency to minimize jitter.Sample fast enough to provide adequate response time.Sample slow enough that noise doesn't dominate the input signal.Sample as slow as possible to conserve processor time.Sample as fast as possible to obtain greatest accuracy.When selecting a sampling rate, there are usually several competing goals, such as: From a system perspective, which is better? Or more importantly, is there a good compromise between the resources used for sampling, and the effect that sampling has on processor use and other factors like real-time schedulability and priority inversion?

FREAC BAD SAMPLING RATE CODE

An alternative might be to execute the control code at half the speed. In reality, a 5ms polling time might be acceptable for that system, but without consideration of other factorsin particular, the real-time response of the systemit's difficult to say whether it's the best answer.įor example, what if the processor is overloaded and the 5ms sampling is using 40% of the CPU's capacity? Increasing the sampling interval to 10ms would halve the CPU utilization. Through testing, he ultimately selected a 5ms polling time because it didn't appear to erroneously register a single push as two and was sufficiently fast to not misinterpret an intentional double-push for a bounce. Remember the engineer who answered “5ms” to my question? His code was created for a push-button switch that required some debouncing. Often, trial-and-error methods are used to determine the sampling rate, and the testing is performed on a minimal system that includes only the sensor sampling codenot the entire application. Second, what is the definition of “best”? The best answer for one application is not necessarily the best answer for another application. Rather, systematic engineering analysis of the application and problem at hand can yield correct answers with just a few experiments.

In this article, I'll review that information and discuss how it applies to analog sensors.Īd-hoc methods of choosing the sampling rate raise several issues.įirst, it shouldn't take days of trial-and-error testing to determine the correct sampling rate for a given application. In my previous article, I explained a way to scientifically determine the best sampling rate for digital inputs.

FREAC BAD SAMPLING RATE SOFTWARE

The sampling rate would only be questioned if the sampling software didn't work. What if that rate wasn't actually the best sampling rate for that application? If the new software appeared to work with the specified sampling rate, the rate would stay fixed for the lifetime of the application, with the value never questioned. Including this value in the specifications left no flexibility to the system designer. In another application, the specifications listed a sampling rate that was formulated based solely on its use in a similar application.

Freac bad sampling rate