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| LinearDigitalFilter (std::shared_ptr< PIDSource > source, std::initializer_list< double > ffGains, std::initializer_list< double > fbGains) |
| Create a linear FIR or IIR filter. More...
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| LinearDigitalFilter (std::shared_ptr< PIDSource > source, std::initializer_list< double > ffGains, const std::vector< double > &fbGains) |
| Create a linear FIR or IIR filter. More...
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| LinearDigitalFilter (std::shared_ptr< PIDSource > source, const std::vector< double > &ffGains, std::initializer_list< double > fbGains) |
| Create a linear FIR or IIR filter. More...
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| LinearDigitalFilter (std::shared_ptr< PIDSource > source, const std::vector< double > &ffGains, const std::vector< double > &fbGains) |
| Create a linear FIR or IIR filter. More...
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double | Get () const override |
| Returns the current filter estimate without also inserting new data as PIDGet() would do.- Returns
- The current filter estimate
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void | Reset () override |
| Reset the filter state.
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double | PIDGet () override |
| Calculates the next value of the filter. More...
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| Filter (std::shared_ptr< PIDSource > source) |
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virtual void | SetPIDSourceType (PIDSourceType pidSource) override |
| Set which parameter you are using as a process control variable. More...
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PIDSourceType | GetPIDSourceType () const |
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PIDSourceType | GetPIDSourceType () const |
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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, I highly recommend the following articles: http://en.wikipedia.org/wiki/Linear_filter http://en.wikipedia.org/wiki/Iir_filter http://en.wikipedia.org/wiki/Fir_filter
Note 1: PIDGet() should be called by the user on a known, regular period. You can set up a Notifier to do this (look at the WPILib PIDController class), 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 PIDGet() gets called at the desired, constant frequency!