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[wpimath] Add core State-space classes (#2614)

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Co-authored-by: Tyler Veness <calcmogul@gmail.com>
Co-authored-by: Claudius Tewari <cttewari@gmail.com>
Co-authored-by: Declan Freeman-Gleason <declanfreemangleason@gmail.com>
This commit is contained in:
Matt
2020-08-14 23:40:33 -07:00
committed by GitHub
parent e5b84e2f87
commit 3b283ab9aa
84 changed files with 11747 additions and 174 deletions
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wpimath/src/test/native/cpp/system/NumericalJacobianTest.cpp Normal file
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/*----------------------------------------------------------------------------*/
/* Copyright (c) 2019-2020 FIRST. All Rights Reserved. */
/* Open Source Software - may be modified and shared by FRC teams. The code */
/* must be accompanied by the FIRST BSD license file in the root directory of */
/* the project. */
/*----------------------------------------------------------------------------*/
#include <gtest/gtest.h>
#include "frc/system/NumericalJacobian.h"
Eigen::Matrix<double, 4, 4> A = (Eigen::Matrix<double, 4, 4>() << 1, 2, 4, 1, 5,
2, 3, 4, 5, 1, 3, 2, 1, 1, 3, 7)
.finished();
Eigen::Matrix<double, 4, 2> B =
(Eigen::Matrix<double, 4, 2>() << 1, 1, 2, 1, 3, 2, 3, 7).finished();
// Function from which to recover A and B
Eigen::Matrix<double, 4, 1> AxBuFn(const Eigen::Matrix<double, 4, 1>& x,
const Eigen::Matrix<double, 2, 1>& u) {
return A * x + B * u;
}
// Test that we can recover A from AxBuFn() pretty accurately
TEST(NumericalJacobianTest, Ax) {
Eigen::Matrix<double, 4, 4> newA = frc::NumericalJacobianX<4, 4, 2>(
AxBuFn, Eigen::Matrix<double, 4, 1>::Zero(),
Eigen::Matrix<double, 2, 1>::Zero());
EXPECT_TRUE(newA.isApprox(A));
}
// Test that we can recover B from AxBuFn() pretty accurately
TEST(NumericalJacobianTest, Bu) {
Eigen::Matrix<double, 4, 2> newB = frc::NumericalJacobianU<4, 4, 2>(
AxBuFn, Eigen::Matrix<double, 4, 1>::Zero(),
Eigen::Matrix<double, 2, 1>::Zero());
EXPECT_TRUE(newB.isApprox(B));
}
Eigen::Matrix<double, 3, 4> C =
(Eigen::Matrix<double, 3, 4>() << 1, 2, 4, 1, 5, 2, 3, 4, 5, 1, 3, 2)
.finished();
Eigen::Matrix<double, 3, 2> D =
(Eigen::Matrix<double, 3, 2>() << 1, 1, 2, 1, 3, 2).finished();
// Function from which to recover C and D
Eigen::Matrix<double, 3, 1> CxDuFn(const Eigen::Matrix<double, 4, 1>& x,
const Eigen::Matrix<double, 2, 1>& u) {
return C * x + D * u;
}
// Test that we can recover C from CxDuFn() pretty accurately
TEST(NumericalJacobianTest, Cx) {
Eigen::Matrix<double, 3, 4> newC = frc::NumericalJacobianX<3, 4, 2>(
CxDuFn, Eigen::Matrix<double, 4, 1>::Zero(),
Eigen::Matrix<double, 2, 1>::Zero());
EXPECT_TRUE(newC.isApprox(C));
}
// Test that we can recover D from CxDuFn() pretty accurately
TEST(NumericalJacobianTest, Du) {
Eigen::Matrix<double, 3, 2> newD = frc::NumericalJacobianU<3, 4, 2>(
CxDuFn, Eigen::Matrix<double, 4, 1>::Zero(),
Eigen::Matrix<double, 2, 1>::Zero());
EXPECT_TRUE(newD.isApprox(D));
}