129 lines
4.7 KiB
Rust
129 lines
4.7 KiB
Rust
use serde::{Deserialize, Serialize};
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use super::{abs, copysign, macc, max, min};
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use core::f32;
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/// IIR state and coefficients type.
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///
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/// To represent the IIR state (input and output memory) during the filter update
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/// this contains the three inputs (x0, x1, x2) and the two outputs (y1, y2)
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/// concatenated. Lower indices correspond to more recent samples.
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/// To represent the IIR coefficients, this contains the feed-forward
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/// coefficients (b0, b1, b2) followd by the negated feed-back coefficients
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/// (-a1, -a2), all five normalized such that a0 = 1.
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pub type IIRState = [f32; 5];
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/// IIR configuration.
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///
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/// Contains the coeeficients `ba`, the output offset `y_offset`, and the
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/// output limits `y_min` and `y_max`.
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///
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/// This implementation achieves several important properties:
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///
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/// * Its transfer function is universal in the sense that any biquadratic
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/// transfer function can be implemented (high-passes, gain limits, second
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/// order integrators with inherent anti-windup, notches etc) without code
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/// changes preserving all features.
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/// * It inherits a universal implementation of "integrator anti-windup", also
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/// and especially in the presence of set-point changes and in the presence
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/// of proportional or derivative gain without any back-off that would reduce
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/// steady-state output range.
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/// * It has universal derivative-kick (undesired, unlimited, and un-physical
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/// amplification of set-point changes by the derivative term) avoidance.
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/// * An offset at the input of an IIR filter (a.k.a. "set-point") is
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/// equivalent to an offset at the output. They are related by the
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/// overall (DC feed-forward) gain of the filter.
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/// * It stores only previous outputs and inputs. These have direct and
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/// invariant interpretation (independent of gains and offsets).
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/// Therefore it can trivially implement bump-less transfer.
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/// * Cascading multiple IIR filters allows stable and robust
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/// implementation of transfer functions beyond bequadratic terms.
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#[derive(Copy, Clone, Deserialize, Serialize)]
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pub struct IIR {
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pub ba: IIRState,
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pub y_offset: f32,
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pub y_min: f32,
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pub y_max: f32,
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}
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impl IIR {
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/// Configures IIR filter coefficients for proportional-integral behavior
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/// with gain limit.
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///
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/// # Arguments
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///
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/// * `kp` - Proportional gain. Also defines gain sign.
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/// * `ki` - Integral gain at Nyquist. Sign taken from `kp`.
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/// * `g` - Gain limit.
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pub fn set_pi(&mut self, kp: f32, ki: f32, g: f32) -> Result<(), &str> {
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let ki = copysign(ki, kp);
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let g = copysign(g, kp);
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let (a1, b0, b1) = if abs(ki) < f32::EPSILON {
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(0., kp, 0.)
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} else {
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let c = if abs(g) < f32::EPSILON {
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1.
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} else {
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1. / (1. + ki / g)
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};
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let a1 = 2. * c - 1.;
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let b0 = ki * c + kp;
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let b1 = ki * c - a1 * kp;
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if abs(b0 + b1) < f32::EPSILON {
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return Err("low integrator gain and/or gain limit");
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}
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(a1, b0, b1)
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};
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self.ba.copy_from_slice(&[b0, b1, 0., a1, 0.]);
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Ok(())
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}
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/// Compute the overall (DC feed-forward) gain.
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pub fn get_k(&self) -> f32 {
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self.ba[..3].iter().sum()
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}
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/// Compute input-referred (`x`) offset from output (`y`) offset.
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pub fn get_x_offset(&self) -> Result<f32, &str> {
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let k = self.get_k();
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if abs(k) < f32::EPSILON {
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Err("k is zero")
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} else {
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Ok(self.y_offset / k)
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}
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}
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/// Convert input (`x`) offset to equivalent output (`y`) offset and apply.
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///
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/// # Arguments
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/// * `xo`: Input (`x`) offset.
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pub fn set_x_offset(&mut self, xo: f32) {
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self.y_offset = xo * self.get_k();
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}
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/// Feed a new input value into the filter, update the filter state, and
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/// return the new output. Only the state `xy` is modified.
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///
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/// # Arguments
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/// * `xy` - Current filter state.
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/// * `x0` - New input.
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pub fn update(&self, xy: &mut IIRState, x0: f32) -> f32 {
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let n = self.ba.len();
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debug_assert!(xy.len() == n);
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// `xy` contains x0 x1 y0 y1 y2
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// Increment time x1 x2 y1 y2 y3
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// Shift x1 x1 x2 y1 y2
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// This unrolls better than xy.rotate_right(1)
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xy.copy_within(0..n - 1, 1);
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// Store x0 x0 x1 x2 y1 y2
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xy[0] = x0;
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// Compute y0 by multiply-accumulate
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let y0 = macc(self.y_offset, xy, &self.ba);
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// Limit y0
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let y0 = max(self.y_min, min(self.y_max, y0));
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// Store y0 x0 x1 y0 y1 y2
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xy[n / 2] = y0;
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y0
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}
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}
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