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[wpimath] Add time-varying RKDP (#7362)
This makes the ground truth for the Taylor series AQ discretization more accurate.
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@@ -107,6 +107,32 @@ public final class NumericalIntegration {
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return x.plus((k1.plus(k2.times(2.0)).plus(k3.times(2.0)).plus(k4)).times(h / 6.0));
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}
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/**
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* Performs 4th order Runge-Kutta integration of dx/dt = f(t, y) for dt.
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*
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* @param <Rows> Rows in y.
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* @param <Cols> Columns in y.
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* @param f The function to integrate. It must take two arguments t and y.
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* @param t The initial value of t.
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* @param y The initial value of y.
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* @param dtSeconds The time over which to integrate.
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* @return the integration of dx/dt = f(x) for dt.
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*/
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public static <Rows extends Num, Cols extends Num> Matrix<Rows, Cols> rk4(
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BiFunction<Double, Matrix<Rows, Cols>, Matrix<Rows, Cols>> f,
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double t,
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Matrix<Rows, Cols> y,
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double dtSeconds) {
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final var h = dtSeconds;
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Matrix<Rows, Cols> k1 = f.apply(t, y);
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Matrix<Rows, Cols> k2 = f.apply(t + dtSeconds * 0.5, y.plus(k1.times(h * 0.5)));
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Matrix<Rows, Cols> k3 = f.apply(t + dtSeconds * 0.5, y.plus(k2.times(h * 0.5)));
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Matrix<Rows, Cols> k4 = f.apply(t + dtSeconds, y.plus(k3.times(h)));
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return y.plus((k1.plus(k2.times(2.0)).plus(k3.times(2.0)).plus(k4)).times(h / 6.0));
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}
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/**
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* Performs adaptive Dormand-Prince integration of dx/dt = f(x, u) for dt. By default, the max
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* error is 1e-6.
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@@ -252,4 +278,132 @@ public final class NumericalIntegration {
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return x;
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}
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/**
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* Performs adaptive Dormand-Prince integration of dx/dt = f(t, y) for dt.
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*
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* @param <Rows> Rows in y.
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* @param <Cols> Columns in y.
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* @param f The function to integrate. It must take two arguments t and y.
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* @param t The initial value of t.
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* @param y The initial value of y.
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* @param dtSeconds The time over which to integrate.
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* @param maxError The maximum acceptable truncation error. Usually a small number like 1e-6.
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* @return the integration of dx/dt = f(x, u) for dt.
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*/
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@SuppressWarnings("overloads")
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public static <Rows extends Num, Cols extends Num> Matrix<Rows, Cols> rkdp(
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BiFunction<Double, Matrix<Rows, Cols>, Matrix<Rows, Cols>> f,
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double t,
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Matrix<Rows, Cols> y,
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double dtSeconds,
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double maxError) {
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// See https://en.wikipedia.org/wiki/Dormand%E2%80%93Prince_method for the
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// Butcher tableau the following arrays came from.
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// final double[6][6]
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final double[][] A = {
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{1.0 / 5.0},
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{3.0 / 40.0, 9.0 / 40.0},
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{44.0 / 45.0, -56.0 / 15.0, 32.0 / 9.0},
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{19372.0 / 6561.0, -25360.0 / 2187.0, 64448.0 / 6561.0, -212.0 / 729.0},
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{9017.0 / 3168.0, -355.0 / 33.0, 46732.0 / 5247.0, 49.0 / 176.0, -5103.0 / 18656.0},
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{35.0 / 384.0, 0.0, 500.0 / 1113.0, 125.0 / 192.0, -2187.0 / 6784.0, 11.0 / 84.0}
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};
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// final double[7]
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final double[] b1 = {
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35.0 / 384.0, 0.0, 500.0 / 1113.0, 125.0 / 192.0, -2187.0 / 6784.0, 11.0 / 84.0, 0.0
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};
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// final double[7]
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final double[] b2 = {
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5179.0 / 57600.0,
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0.0,
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7571.0 / 16695.0,
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393.0 / 640.0,
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-92097.0 / 339200.0,
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187.0 / 2100.0,
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1.0 / 40.0
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};
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// final double[6]
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final double[] c = {1.0 / 5.0, 3.0 / 10.0, 4.0 / 5.0, 8.0 / 9.0, 1.0, 1.0};
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Matrix<Rows, Cols> newY;
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double truncationError;
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double dtElapsed = 0.0;
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double h = dtSeconds;
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// Loop until we've gotten to our desired dt
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while (dtElapsed < dtSeconds) {
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do {
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// Only allow us to advance up to the dt remaining
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h = Math.min(h, dtSeconds - dtElapsed);
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var k1 = f.apply(t, y);
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var k2 = f.apply(t + h * c[0], y.plus(k1.times(A[0][0]).times(h)));
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var k3 = f.apply(t + h * c[1], y.plus(k1.times(A[1][0]).plus(k2.times(A[1][1])).times(h)));
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var k4 =
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f.apply(
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t + h * c[2],
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y.plus(k1.times(A[2][0]).plus(k2.times(A[2][1])).plus(k3.times(A[2][2])).times(h)));
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var k5 =
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f.apply(
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t + h * c[3],
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y.plus(
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k1.times(A[3][0])
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.plus(k2.times(A[3][1]))
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.plus(k3.times(A[3][2]))
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.plus(k4.times(A[3][3]))
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.times(h)));
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var k6 =
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f.apply(
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t + h * c[4],
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y.plus(
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k1.times(A[4][0])
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.plus(k2.times(A[4][1]))
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.plus(k3.times(A[4][2]))
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.plus(k4.times(A[4][3]))
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.plus(k5.times(A[4][4]))
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.times(h)));
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// Since the final row of A and the array b1 have the same coefficients
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// and k7 has no effect on newY, we can reuse the calculation.
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newY =
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y.plus(
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k1.times(A[5][0])
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.plus(k2.times(A[5][1]))
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.plus(k3.times(A[5][2]))
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.plus(k4.times(A[5][3]))
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.plus(k5.times(A[5][4]))
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.plus(k6.times(A[5][5]))
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.times(h));
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var k7 = f.apply(t + h * c[5], newY);
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truncationError =
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(k1.times(b1[0] - b2[0])
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.plus(k2.times(b1[1] - b2[1]))
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.plus(k3.times(b1[2] - b2[2]))
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.plus(k4.times(b1[3] - b2[3]))
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.plus(k5.times(b1[4] - b2[4]))
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.plus(k6.times(b1[5] - b2[5]))
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.plus(k7.times(b1[6] - b2[6]))
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.times(h))
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.normF();
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if (truncationError == 0.0) {
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h = dtSeconds - dtElapsed;
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} else {
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h *= 0.9 * Math.pow(maxError / truncationError, 1.0 / 5.0);
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}
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} while (truncationError > maxError);
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dtElapsed += h;
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y = newY;
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}
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return y;
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}
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}
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