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WPILibC++ 2023.4.3
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This class implements a linear, digital filter. More...
#include <frc/filter/LinearFilter.h>
Public Member Functions | |
LinearFilter (std::span< const double > ffGains, std::span< const 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. More... | |
T | 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... | |
template<int Derivative, int Samples> | |
static LinearFilter< T > | FiniteDifference (const wpi::array< int, Samples > stencil, units::second_t period) |
Creates a finite difference filter that computes the nth derivative of the input given the specified stencil points. More... | |
template<int Derivative, int Samples> | |
static LinearFilter< T > | BackwardFiniteDifference (units::second_t period) |
Creates a backward finite difference filter that computes the nth derivative of the input given the specified number of samples. More... | |
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:
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!
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inline |
Create a linear FIR or IIR filter.
ffGains | The "feedforward" or FIR gains. |
fbGains | The "feedback" or IIR gains. |
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inline |
Create a linear FIR or IIR filter.
ffGains | The "feedforward" or FIR gains. |
fbGains | The "feedback" or IIR gains. |
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inlinestatic |
Creates a backward finite difference filter that computes the nth derivative of the input given the specified number of samples.
For example, a first derivative filter that uses two samples and a sample period of 20 ms would be
LinearFilter<double>::BackwardFiniteDifference<1, 2>(20_ms);
Derivative | The order of the derivative to compute. |
Samples | The number of samples to use to compute the given derivative. This must be one more than the order of derivative or higher. |
period | The period in seconds between samples taken by the user. |
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inline |
Calculates the next value of the filter.
input | Current input value. |
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inlinestatic |
Creates a finite difference filter that computes the nth derivative of the input given the specified stencil points.
Stencil points are the indices of the samples to use in the finite difference. 0 is the current sample, -1 is the previous sample, -2 is the sample before that, etc. Don't use positive stencil points (samples from the future) if the LinearFilter will be used for stream-based online filtering (e.g., taking derivative of encoder samples in real-time).
Derivative | The order of the derivative to compute. |
Samples | The number of samples to use to compute the given derivative. This must be one more than the order of the derivative or higher. |
stencil | List of stencil points. |
period | The period in seconds between samples taken by the user. |
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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.
Note: T = 1 / (2 pi f) where f is the cutoff frequency in Hz, the frequency below which the input starts to attenuate.
This filter is stable for time constants greater than zero.
timeConstant | The discrete-time time constant in seconds. |
period | The period in seconds between samples taken by the user. |
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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.
taps | The number of samples to average over. Higher = smoother but slower |
std::runtime_error | if number of taps is less than 1. |
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inline |
Reset the filter state.
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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.
Note: T = 1 / (2 pi f) where f is the cutoff frequency in Hz, the frequency above which the input starts to attenuate.
This filter is stable for time constants greater than zero.
timeConstant | The discrete-time time constant in seconds. |
period | The period in seconds between samples taken by the user. |