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[wpimath] Add typedefs for common types

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This makes complex code significantly easier to read.

frc::Vectord<Size> = Eigen::Vector<double, Size>
frc::Matrixd<Rows, Cols> = Eigen::Matrix<double, Rows, Cols>
This commit is contained in:
Peter Johnson
2022-04-29 22:29:20 -07:00
parent 97c493241f
commit e767605e94
76 changed files with 1136 additions and 1449 deletions
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141
wpimath/src/test/native/cpp/system/DiscretizationTest.cpp
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@@ -6,8 +6,8 @@
#include <functional>
#include "Eigen/Core"
#include "Eigen/Eigenvalues"
#include "frc/EigenCore.h"
#include "frc/system/Discretization.h"
#include "frc/system/NumericalIntegration.h"
#include "frc/system/RungeKuttaTimeVarying.h"
@@ -15,17 +15,16 @@
// Check that for a simple second-order system that we can easily analyze
// analytically,
TEST(DiscretizationTest, DiscretizeA) {
Eigen::Matrix<double, 2, 2> contA{{0, 1}, {0, 0}};
frc::Matrixd<2, 2> contA{{0, 1}, {0, 0}};
Eigen::Vector<double, 2> x0{1, 1};
Eigen::Matrix<double, 2, 2> discA;
frc::Vectord<2> x0{1, 1};
frc::Matrixd<2, 2> discA;
frc::DiscretizeA<2>(contA, 1_s, &discA);
Eigen::Vector<double, 2> x1Discrete = discA * x0;
frc::Vectord<2> x1Discrete = discA * x0;
// We now have pos = vel = 1 and accel = 0, which should give us:
Eigen::Vector<double, 2> x1Truth{1.0 * x0(0) + 1.0 * x0(1),
0.0 * x0(0) + 1.0 * x0(1)};
frc::Vectord<2> x1Truth{1.0 * x0(0) + 1.0 * x0(1), 0.0 * x0(0) + 1.0 * x0(1)};
EXPECT_EQ(x1Truth, x1Discrete);
}
@@ -33,20 +32,20 @@ TEST(DiscretizationTest, DiscretizeA) {
// Check that for a simple second-order system that we can easily analyze
// analytically,
TEST(DiscretizationTest, DiscretizeAB) {
Eigen::Matrix<double, 2, 2> contA{{0, 1}, {0, 0}};
Eigen::Matrix<double, 2, 1> contB{0, 1};
frc::Matrixd<2, 2> contA{{0, 1}, {0, 0}};
frc::Matrixd<2, 1> contB{0, 1};
Eigen::Vector<double, 2> x0{1, 1};
Eigen::Vector<double, 1> u{1};
Eigen::Matrix<double, 2, 2> discA;
Eigen::Matrix<double, 2, 1> discB;
frc::Vectord<2> x0{1, 1};
frc::Vectord<1> u{1};
frc::Matrixd<2, 2> discA;
frc::Matrixd<2, 1> discB;
frc::DiscretizeAB<2, 1>(contA, contB, 1_s, &discA, &discB);
Eigen::Vector<double, 2> x1Discrete = discA * x0 + discB * u;
frc::Vectord<2> x1Discrete = discA * x0 + discB * u;
// We now have pos = vel = accel = 1, which should give us:
Eigen::Vector<double, 2> x1Truth{1.0 * x0(0) + 1.0 * x0(1) + 0.5 * u(0),
0.0 * x0(0) + 1.0 * x0(1) + 1.0 * u(0)};
frc::Vectord<2> x1Truth{1.0 * x0(0) + 1.0 * x0(1) + 0.5 * u(0),
0.0 * x0(0) + 1.0 * x0(1) + 1.0 * u(0)};
EXPECT_EQ(x1Truth, x1Discrete);
}
@@ -55,24 +54,23 @@ TEST(DiscretizationTest, DiscretizeAB) {
// Test that the discrete approximation of Q ≈ ∫ e^(Aτ) Q e^(Aᵀτ) dτ
// 0
TEST(DiscretizationTest, DiscretizeSlowModelAQ) {
Eigen::Matrix<double, 2, 2> contA{{0, 1}, {0, 0}};
Eigen::Matrix<double, 2, 2> contQ{{1, 0}, {0, 1}};
frc::Matrixd<2, 2> contA{{0, 1}, {0, 0}};
frc::Matrixd<2, 2> contQ{{1, 0}, {0, 1}};
constexpr auto dt = 1_s;
Eigen::Matrix<double, 2, 2> discQIntegrated = frc::RungeKuttaTimeVarying<
std::function<Eigen::Matrix<double, 2, 2>(
units::second_t, const Eigen::Matrix<double, 2, 2>&)>,
Eigen::Matrix<double, 2, 2>>(
[&](units::second_t t, const Eigen::Matrix<double, 2, 2>&) {
return Eigen::Matrix<double, 2, 2>(
(contA * t.value()).exp() * contQ *
(contA.transpose() * t.value()).exp());
frc::Matrixd<2, 2> discQIntegrated = frc::RungeKuttaTimeVarying<
std::function<frc::Matrixd<2, 2>(units::second_t,
const frc::Matrixd<2, 2>&)>,
frc::Matrixd<2, 2>>(
[&](units::second_t t, const frc::Matrixd<2, 2>&) {
return frc::Matrixd<2, 2>((contA * t.value()).exp() * contQ *
(contA.transpose() * t.value()).exp());
},
0_s, Eigen::Matrix<double, 2, 2>::Zero(), dt);
0_s, frc::Matrixd<2, 2>::Zero(), dt);
Eigen::Matrix<double, 2, 2> discA;
Eigen::Matrix<double, 2, 2> discQ;
frc::Matrixd<2, 2> discA;
frc::Matrixd<2, 2> discQ;
frc::DiscretizeAQ<2>(contA, contQ, dt, &discA, &discQ);
EXPECT_LT((discQIntegrated - discQ).norm(), 1e-10)
@@ -85,24 +83,23 @@ TEST(DiscretizationTest, DiscretizeSlowModelAQ) {
// Test that the discrete approximation of Q ≈ ∫ e^(Aτ) Q e^(Aᵀτ) dτ
// 0
TEST(DiscretizationTest, DiscretizeFastModelAQ) {
Eigen::Matrix<double, 2, 2> contA{{0, 1}, {0, -1406.29}};
Eigen::Matrix<double, 2, 2> contQ{{0.0025, 0}, {0, 1}};
frc::Matrixd<2, 2> contA{{0, 1}, {0, -1406.29}};
frc::Matrixd<2, 2> contQ{{0.0025, 0}, {0, 1}};
constexpr auto dt = 5_ms;
Eigen::Matrix<double, 2, 2> discQIntegrated = frc::RungeKuttaTimeVarying<
std::function<Eigen::Matrix<double, 2, 2>(
units::second_t, const Eigen::Matrix<double, 2, 2>&)>,
Eigen::Matrix<double, 2, 2>>(
[&](units::second_t t, const Eigen::Matrix<double, 2, 2>&) {
return Eigen::Matrix<double, 2, 2>(
(contA * t.value()).exp() * contQ *
(contA.transpose() * t.value()).exp());
frc::Matrixd<2, 2> discQIntegrated = frc::RungeKuttaTimeVarying<
std::function<frc::Matrixd<2, 2>(units::second_t,
const frc::Matrixd<2, 2>&)>,
frc::Matrixd<2, 2>>(
[&](units::second_t t, const frc::Matrixd<2, 2>&) {
return frc::Matrixd<2, 2>((contA * t.value()).exp() * contQ *
(contA.transpose() * t.value()).exp());
},
0_s, Eigen::Matrix<double, 2, 2>::Zero(), dt);
0_s, frc::Matrixd<2, 2>::Zero(), dt);
Eigen::Matrix<double, 2, 2> discA;
Eigen::Matrix<double, 2, 2> discQ;
frc::Matrixd<2, 2> discA;
frc::Matrixd<2, 2> discQ;
frc::DiscretizeAQ<2>(contA, contQ, dt, &discA, &discQ);
EXPECT_LT((discQIntegrated - discQ).norm(), 1e-3)
@@ -113,14 +110,14 @@ TEST(DiscretizationTest, DiscretizeFastModelAQ) {
// Test that the Taylor series discretization produces nearly identical results.
TEST(DiscretizationTest, DiscretizeSlowModelAQTaylor) {
Eigen::Matrix<double, 2, 2> contA{{0, 1}, {0, 0}};
Eigen::Matrix<double, 2, 2> contQ{{1, 0}, {0, 1}};
frc::Matrixd<2, 2> contA{{0, 1}, {0, 0}};
frc::Matrixd<2, 2> contQ{{1, 0}, {0, 1}};
constexpr auto dt = 1_s;
Eigen::Matrix<double, 2, 2> discQTaylor;
Eigen::Matrix<double, 2, 2> discA;
Eigen::Matrix<double, 2, 2> discATaylor;
frc::Matrixd<2, 2> discQTaylor;
frc::Matrixd<2, 2> discA;
frc::Matrixd<2, 2> discATaylor;
// Continuous Q should be positive semidefinite
Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> esCont{contQ};
@@ -128,16 +125,15 @@ TEST(DiscretizationTest, DiscretizeSlowModelAQTaylor) {
EXPECT_GE(esCont.eigenvalues()[i], 0);
}
Eigen::Matrix<double, 2, 2> discQIntegrated = frc::RungeKuttaTimeVarying<
std::function<Eigen::Matrix<double, 2, 2>(
units::second_t, const Eigen::Matrix<double, 2, 2>&)>,
Eigen::Matrix<double, 2, 2>>(
[&](units::second_t t, const Eigen::Matrix<double, 2, 2>&) {
return Eigen::Matrix<double, 2, 2>(
(contA * t.value()).exp() * contQ *
(contA.transpose() * t.value()).exp());
frc::Matrixd<2, 2> discQIntegrated = frc::RungeKuttaTimeVarying<
std::function<frc::Matrixd<2, 2>(units::second_t,
const frc::Matrixd<2, 2>&)>,
frc::Matrixd<2, 2>>(
[&](units::second_t t, const frc::Matrixd<2, 2>&) {
return frc::Matrixd<2, 2>((contA * t.value()).exp() * contQ *
(contA.transpose() * t.value()).exp());
},
0_s, Eigen::Matrix<double, 2, 2>::Zero(), dt);
0_s, frc::Matrixd<2, 2>::Zero(), dt);
frc::DiscretizeA<2>(contA, dt, &discA);
frc::DiscretizeAQTaylor<2>(contA, contQ, dt, &discATaylor, &discQTaylor);
@@ -157,14 +153,14 @@ TEST(DiscretizationTest, DiscretizeSlowModelAQTaylor) {
// Test that the Taylor series discretization produces nearly identical results.
TEST(DiscretizationTest, DiscretizeFastModelAQTaylor) {
Eigen::Matrix<double, 2, 2> contA{{0, 1}, {0, -1500}};
Eigen::Matrix<double, 2, 2> contQ{{0.0025, 0}, {0, 1}};
frc::Matrixd<2, 2> contA{{0, 1}, {0, -1500}};
frc::Matrixd<2, 2> contQ{{0.0025, 0}, {0, 1}};
constexpr auto dt = 5_ms;
Eigen::Matrix<double, 2, 2> discQTaylor;
Eigen::Matrix<double, 2, 2> discA;
Eigen::Matrix<double, 2, 2> discATaylor;
frc::Matrixd<2, 2> discQTaylor;
frc::Matrixd<2, 2> discA;
frc::Matrixd<2, 2> discATaylor;
// Continuous Q should be positive semidefinite
Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> esCont(contQ);
@@ -172,16 +168,15 @@ TEST(DiscretizationTest, DiscretizeFastModelAQTaylor) {
EXPECT_GE(esCont.eigenvalues()[i], 0);
}
Eigen::Matrix<double, 2, 2> discQIntegrated = frc::RungeKuttaTimeVarying<
std::function<Eigen::Matrix<double, 2, 2>(
units::second_t, const Eigen::Matrix<double, 2, 2>&)>,
Eigen::Matrix<double, 2, 2>>(
[&](units::second_t t, const Eigen::Matrix<double, 2, 2>&) {
return Eigen::Matrix<double, 2, 2>(
(contA * t.value()).exp() * contQ *
(contA.transpose() * t.value()).exp());
frc::Matrixd<2, 2> discQIntegrated = frc::RungeKuttaTimeVarying<
std::function<frc::Matrixd<2, 2>(units::second_t,
const frc::Matrixd<2, 2>&)>,
frc::Matrixd<2, 2>>(
[&](units::second_t t, const frc::Matrixd<2, 2>&) {
return frc::Matrixd<2, 2>((contA * t.value()).exp() * contQ *
(contA.transpose() * t.value()).exp());
},
0_s, Eigen::Matrix<double, 2, 2>::Zero(), dt);
0_s, frc::Matrixd<2, 2>::Zero(), dt);
frc::DiscretizeA<2>(contA, dt, &discA);
frc::DiscretizeAQTaylor<2>(contA, contQ, dt, &discATaylor, &discQTaylor);
@@ -201,10 +196,10 @@ TEST(DiscretizationTest, DiscretizeFastModelAQTaylor) {
// Test that DiscretizeR() works
TEST(DiscretizationTest, DiscretizeR) {
Eigen::Matrix<double, 2, 2> contR{{2.0, 0.0}, {0.0, 1.0}};
Eigen::Matrix<double, 2, 2> discRTruth{{4.0, 0.0}, {0.0, 2.0}};
frc::Matrixd<2, 2> contR{{2.0, 0.0}, {0.0, 1.0}};
frc::Matrixd<2, 2> discRTruth{{4.0, 0.0}, {0.0, 2.0}};
Eigen::Matrix<double, 2, 2> discR = frc::DiscretizeR<2>(contR, 500_ms);
frc::Matrixd<2, 2> discR = frc::DiscretizeR<2>(contR, 500_ms);
EXPECT_LT((discRTruth - discR).norm(), 1e-10)
<< "Expected these to be nearly equal:\ndiscR:\n"