WPILibC++  2020.3.2-60-g3011ebe
frc::LinearFilter< T > Class Template Reference

This class implements a linear, digital filter. More...

#include <LinearFilter.h>

## Public Member Functions

LinearFilter (wpi::ArrayRef< double > ffGains, wpi::ArrayRef< double > fbGains)
Create a linear FIR or IIR filter. More...

LinearFilter (std::initializer_list< double > ffGains, std::initializer_list< double > fbGains)
Create a linear FIR or IIR filter. More...

void Reset ()
Reset the filter state.

Calculate (T input)
Calculates the next value of the filter. More...

## Static Public Member Functions

static LinearFilter< T > SinglePoleIIR (double timeConstant, units::second_t period)
Creates a one-pole IIR low-pass filter of the form:
y[n] = (1 - gain) * x[n] + gain * y[n-1]
where gain = e-dt / T, T is the time constant in seconds. More...

static LinearFilter< T > HighPass (double timeConstant, units::second_t period)
Creates a first-order high-pass filter of the form:
y[n] = gain * x[n] + (-gain) * x[n-1] + gain * y[n-1]
where gain = e-dt / T, T is the time constant in seconds. More...

static LinearFilter< T > MovingAverage (int taps)
Creates a K-tap FIR moving average filter of the form:
y[n] = 1/k * (x[k] + x[k-1] + … + x[0]) More...

## Detailed Description

### template<class T> class frc::LinearFilter< T >

This class implements a linear, digital filter.

All types of FIR and IIR filters are supported. Static factory methods are provided to create commonly used types of filters.

Filters are of the form:
y[n] = (b0 * x[n] + b1 * x[n-1] + … + bP * x[n-P]) - (a0 * y[n-1] + a2 * y[n-2] + … + aQ * y[n-Q])

Where:
y[n] is the output at time "n"
x[n] is the input at time "n"
y[n-1] is the output from the LAST time step ("n-1")
x[n-1] is the input from the LAST time step ("n-1")
b0 … bP are the "feedforward" (FIR) gains
a0 … aQ are the "feedback" (IIR) gains
IMPORTANT! Note the "-" sign in front of the feedback term! This is a common convention in signal processing.

What can linear filters do? Basically, they can filter, or diminish, the effects of undesirable input frequencies. High frequencies, or rapid changes, can be indicative of sensor noise or be otherwise undesirable. A "low pass" filter smooths out the signal, reducing the impact of these high frequency components. Likewise, a "high pass" filter gets rid of slow-moving signal components, letting you detect large changes more easily.

Example FRC applications of filters:

• Getting rid of noise from an analog sensor input (note: the roboRIO's FPGA can do this faster in hardware)
• Smoothing out joystick input to prevent the wheels from slipping or the robot from tipping
• Smoothing motor commands so that unnecessary strain isn't put on electrical or mechanical components
• If you use clever gains, you can make a PID controller out of this class!

For more on filters, we highly recommend the following articles:
https://en.wikipedia.org/wiki/Linear_filter
https://en.wikipedia.org/wiki/Iir_filter
https://en.wikipedia.org/wiki/Fir_filter

Note 1: Calculate() should be called by the user on a known, regular period. You can use a Notifier for this or do it "inline" with code in a periodic function.

Note 2: For ALL filters, gains are necessarily a function of frequency. If you make a filter that works well for you at, say, 100Hz, you will most definitely need to adjust the gains if you then want to run it at 200Hz! Combining this with Note 1 - the impetus is on YOU as a developer to make sure Calculate() gets called at the desired, constant frequency!

## ◆ LinearFilter() [1/2]

template<class T>
 frc::LinearFilter< T >::LinearFilter ( wpi::ArrayRef< double > ffGains, wpi::ArrayRef< double > fbGains )
inline

Create a linear FIR or IIR filter.

Parameters
 ffGains The "feed forward" or FIR gains. fbGains The "feed back" or IIR gains.

## ◆ LinearFilter() [2/2]

template<class T>
 frc::LinearFilter< T >::LinearFilter ( std::initializer_list< double > ffGains, std::initializer_list< double > fbGains )
inline

Create a linear FIR or IIR filter.

Parameters
 ffGains The "feed forward" or FIR gains. fbGains The "feed back" or IIR gains.

## ◆ Calculate()

template<class T>
 T frc::LinearFilter< T >::Calculate ( T input )
inline

Calculates the next value of the filter.

Parameters
 input Current input value.
Returns
The filtered value at this step

## ◆ HighPass()

template<class T>
 static LinearFilter frc::LinearFilter< T >::HighPass ( double timeConstant, units::second_t period )
inlinestatic

Creates a first-order high-pass filter of the form:
y[n] = gain * x[n] + (-gain) * x[n-1] + gain * y[n-1]
where gain = e-dt / T, T is the time constant in seconds.

This filter is stable for time constants greater than zero.

Parameters
 timeConstant The discrete-time time constant in seconds. period The period in seconds between samples taken by the user.

## ◆ MovingAverage()

template<class T>
 static LinearFilter frc::LinearFilter< T >::MovingAverage ( int taps )
inlinestatic

Creates a K-tap FIR moving average filter of the form:
y[n] = 1/k * (x[k] + x[k-1] + … + x[0])

This filter is always stable.

Parameters
 taps The number of samples to average over. Higher = smoother but slower

## ◆ SinglePoleIIR()

template<class T>
 static LinearFilter frc::LinearFilter< T >::SinglePoleIIR ( double timeConstant, units::second_t period )
inlinestatic

Creates a one-pole IIR low-pass filter of the form:
y[n] = (1 - gain) * x[n] + gain * y[n-1]
where gain = e-dt / T, T is the time constant in seconds.

This filter is stable for time constants greater than zero.

Parameters
 timeConstant The discrete-time time constant in seconds. period The period in seconds between samples taken by the user.

The documentation for this class was generated from the following file: