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https://github.com/wpilibsuite/allwpilib
synced 2026-06-20 00:51:42 +00:00
[wpimath] Rewrite DARE solver (#5328)
I timed the DARE unit tests, and the new solver is 0 to 100% faster in all cases (that is, it's at least as fast as Drake's and up to 2x faster in some cases). The new solver is also much simpler, takes less time to compile, and drops the libwpimath.so size from 325 MB to 301 MB. I think most of the compilation time is coming from the eigenvalue decompositions used to enforce argument preconditions.
This commit is contained in:
Tyler Veness
265
wpimath/src/main/native/cpp/DARE.cpp
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265
wpimath/src/main/native/cpp/DARE.cpp
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// Copyright (c) FIRST and other WPILib contributors.
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// Open Source Software; you can modify and/or share it under the terms of
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// the WPILib BSD license file in the root directory of this project.
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#include "frc/DARE.h"
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#include <cassert>
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#include <stdexcept>
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#include <string>
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#include "Eigen/Cholesky"
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#include "Eigen/Core"
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#include "Eigen/Eigenvalues"
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#include "Eigen/QR"
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#include "frc/fmt/Eigen.h"
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// Works cited:
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//
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// [1] E. K.-W. Chu, H.-Y. Fan, W.-W. Lin & C.-S. Wang "Structure-Preserving
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// Algorithms for Periodic Discrete-Time Algebraic Riccati Equations",
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// International Journal of Control, 77:8, 767-788, 2004.
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// DOI: 10.1080/00207170410001714988
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namespace frc {
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namespace {
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/**
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* Returns true if (A, B) is a stabilizable pair.
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*
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* (A, B) is stabilizable if and only if the uncontrollable eigenvalues of A, if
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* any, have absolute values less than one, where an eigenvalue is
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* uncontrollable if rank([λI - A, B]) < n where n is the number of states.
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*
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* @param A System matrix.
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* @param B Input matrix.
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*/
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bool IsStabilizable(const Eigen::Ref<const Eigen::MatrixXd>& A,
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const Eigen::Ref<const Eigen::MatrixXd>& B) {
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Eigen::EigenSolver<Eigen::MatrixXd> es{A};
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for (int i = 0; i < A.rows(); ++i) {
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if (es.eigenvalues()[i].real() * es.eigenvalues()[i].real() +
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es.eigenvalues()[i].imag() * es.eigenvalues()[i].imag() <
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1) {
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continue;
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}
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Eigen::MatrixXcd E{A.rows(), A.rows() + B.cols()};
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E << es.eigenvalues()[i] * Eigen::MatrixXcd::Identity(A.rows(), A.rows()) -
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A,
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B;
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Eigen::ColPivHouseholderQR<Eigen::MatrixXcd> qr{E};
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if (qr.rank() < A.rows()) {
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return false;
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}
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}
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return true;
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}
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/**
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* Returns true if (A, C) is a detectable pair.
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*
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* (A, C) is detectable if and only if the unobservable eigenvalues of A, if
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* any, have absolute values less than one, where an eigenvalue is unobservable
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* if rank([λI - A; C]) < n where n is the number of states.
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*
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* @param A System matrix.
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* @param C Output matrix.
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*/
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bool IsDetectable(const Eigen::Ref<const Eigen::MatrixXd>& A,
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const Eigen::Ref<const Eigen::MatrixXd>& C) {
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return IsStabilizable(A.transpose(), C.transpose());
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}
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/**
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* Returns true if all the matrix's eigenvalues are greater than or equal to
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* zero.
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*
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* @param A The matrix.
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*/
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bool IsPositiveSemidefinite(const Eigen::Ref<const Eigen::MatrixXd>& A) {
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Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es{A};
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for (int i = 0; i < A.rows(); ++i) {
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if (es.eigenvalues()[i] < 0) {
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return false;
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}
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}
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return true;
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}
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/**
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* Returns true if all the matrix's eigenvalues are greater than zero.
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*
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* @param A The matrix.
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*/
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bool IsPositiveDefinite(const Eigen::Ref<const Eigen::MatrixXd>& A) {
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Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> es{A};
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for (int i = 0; i < A.rows(); ++i) {
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if (es.eigenvalues()[i] <= 0) {
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return false;
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}
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}
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return true;
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}
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} // namespace
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Eigen::MatrixXd DARE(const Eigen::Ref<const Eigen::MatrixXd>& A,
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const Eigen::Ref<const Eigen::MatrixXd>& B,
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const Eigen::Ref<const Eigen::MatrixXd>& Q,
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const Eigen::Ref<const Eigen::MatrixXd>& R) {
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// These are unused if assertions aren't compiled in
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[[maybe_unused]] int states = A.rows();
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[[maybe_unused]] int inputs = B.cols();
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// Check argument dimensions
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assert(A.rows() == states && A.cols() == states);
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assert(B.rows() == states && B.cols() == inputs);
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assert(Q.rows() == states && Q.cols() == states);
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assert(R.rows() == inputs && R.cols() == inputs);
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// Require the number of inputs be less than or equal to the number of states
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if (inputs > states) {
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std::string msg = fmt::format(
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"Number of inputs ({}) is greater than number of states ({})!", inputs,
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states);
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throw std::invalid_argument(msg);
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}
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// Require Q be symmetric
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if ((Q - Q.transpose()).norm() > 1e-10) {
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std::string msg =
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fmt::format("Q isn't symmetric!\n\nQ =\n{}\n", Eigen::MatrixXd{Q});
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throw std::invalid_argument(msg);
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}
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// Require Q be positive semidefinite
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if (!IsPositiveSemidefinite(Q)) {
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std::string msg = fmt::format("Q isn't positive semidefinite!\n\nQ =\n{}\n",
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Eigen::MatrixXd{Q});
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throw std::invalid_argument(msg);
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}
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// Require R be symmetric
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if ((R - R.transpose()).norm() > 1e-10) {
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std::string msg =
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fmt::format("R isn't symmetric!\n\nR =\n{}\n", Eigen::MatrixXd{R});
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throw std::invalid_argument(msg);
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}
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// Require R be positive definite
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if (!IsPositiveDefinite(R)) {
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std::string msg = fmt::format("R isn't positive definite!\n\nR =\n{}\n",
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Eigen::MatrixXd{R});
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throw std::invalid_argument(msg);
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}
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// Require (A, B) pair be stabilizable
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if (!IsStabilizable(A, B)) {
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std::string msg =
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fmt::format("The (A, B) pair isn't stabilizable!\n\nA =\n{}\nB =\n{}\n",
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Eigen::MatrixXd{A}, Eigen::MatrixXd{B});
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throw std::invalid_argument(msg);
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}
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// Require (A, C) pair be detectable where Q = CᵀC
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{
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Eigen::LDLT<Eigen::MatrixXd> ldlt{Q};
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Eigen::MatrixXd C = Eigen::MatrixXd{ldlt.matrixL()} *
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ldlt.vectorD().cwiseSqrt().asDiagonal();
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if (!IsDetectable(A, C)) {
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std::string msg = fmt::format(
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"The (A, C) pair where Q = CᵀC isn't detectable!\n\nA =\n{}\nQ "
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"=\n{}\n",
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Eigen::MatrixXd{A}, Eigen::MatrixXd{Q});
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throw std::invalid_argument(msg);
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}
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}
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// Implements the SSCA algorithm on page 12 of [1].
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// A₀ = A
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Eigen::MatrixXd A_k = A;
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Eigen::MatrixXd A_k1 = A;
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// G₀ = BR⁻¹Bᵀ
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//
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// See equation (4) of [1].
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Eigen::MatrixXd G_k = B * R.llt().solve(B.transpose());
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Eigen::MatrixXd I = Eigen::MatrixXd::Identity(A.rows(), A.cols());
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// H₀ = Q
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//
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// See equation (4) of [1].
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Eigen::MatrixXd H_k = Q;
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Eigen::MatrixXd H_k1 = Q;
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do {
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A_k = A_k1;
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H_k = H_k1;
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// W = I + HₖGₖ
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Eigen::MatrixXd W = I + H_k * G_k;
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// W is symmetric positive definite, so the LLT decomposition would work
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// here and is faster than the householder QR decomposition [2]. However,
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// it's not accurate enough. Experimentation showed that so many iterations
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// of iterative refinement [3] were required to fix the accuracy that the
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// total system solve time was much higher than householder QR.
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//
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// [2] https://eigen.tuxfamily.org/dox/group__TutorialLinearAlgebra.html
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// [3] https://en.wikipedia.org/wiki/Iterative_refinement
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auto W_solver = W.householderQr();
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// Solve WV₁ = Aₖᵀ for V₁
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Eigen::MatrixXd V_1 = W_solver.solve(A_k.transpose());
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// Solve WV₂ = Hₖ for V₂
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Eigen::MatrixXd V_2 = W_solver.solve(H_k);
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// Aₖ₊₁ = V₁ᵀAₖ
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A_k1 = V_1.transpose() * A_k;
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// Gₖ₊₁ = Gₖ + AₖGₖV₁
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G_k += A_k * G_k * V_1;
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// Hₖ₊₁ = Hₖ + AₖᵀV₂Aₖ
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H_k1 = H_k + A_k.transpose() * V_2 * A_k;
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// while |Hₖ₊₁ − Hₖ| > ε |Hₖ₊₁|
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} while ((H_k1 - H_k).norm() > 1e-10 * H_k1.norm());
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return H_k1;
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}
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Eigen::MatrixXd DARE(const Eigen::Ref<const Eigen::MatrixXd>& A,
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const Eigen::Ref<const Eigen::MatrixXd>& B,
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const Eigen::Ref<const Eigen::MatrixXd>& Q,
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const Eigen::Ref<const Eigen::MatrixXd>& R,
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const Eigen::Ref<const Eigen::MatrixXd>& N) {
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// Check argument dimensions
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assert(N.rows() == B.rows() && N.cols() == B.cols());
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// This is a change of variables to make the DARE that includes Q, R, and N
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// cost matrices fit the form of the DARE that includes only Q and R cost
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// matrices.
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//
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// This is equivalent to solving the original DARE:
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//
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// A₂ᵀXA₂ − X − A₂ᵀXB(BᵀXB + R)⁻¹BᵀXA₂ + Q₂ = 0
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//
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// where A₂ and Q₂ are a change of variables:
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//
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// A₂ = A − BR⁻¹Nᵀ and Q₂ = Q − NR⁻¹Nᵀ
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return DARE(A - B * R.llt().solve(N.transpose()), B,
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Q - N * R.llt().solve(N.transpose()), R);
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}
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} // namespace frc
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@@ -12,47 +12,51 @@
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#include "Eigen/Core"
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#include "Eigen/Eigenvalues"
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#include "Eigen/QR"
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#include "drake/math/discrete_algebraic_riccati_equation.h"
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#include "edu_wpi_first_math_WPIMathJNI.h"
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#include "frc/DARE.h"
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#include "frc/trajectory/TrajectoryUtil.h"
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#include "unsupported/Eigen/MatrixFunctions"
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using namespace wpi::java;
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namespace {
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/**
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* Returns true if (A, B) is a stabilizable pair.
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*
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* (A, B) is stabilizable if and only if the uncontrollable eigenvalues of A, if
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* any, have absolute values less than one, where an eigenvalue is
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* uncontrollable if rank(λI - A, B) < n where n is the number of states.
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* uncontrollable if rank([λI - A, B]) < n where n is the number of states.
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*
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* @param A System matrix.
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* @param B Input matrix.
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*/
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bool check_stabilizable(const Eigen::Ref<const Eigen::MatrixXd>& A,
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const Eigen::Ref<const Eigen::MatrixXd>& B) {
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int states = B.rows();
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int inputs = B.cols();
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bool IsStabilizable(const Eigen::Ref<const Eigen::MatrixXd>& A,
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const Eigen::Ref<const Eigen::MatrixXd>& B) {
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Eigen::EigenSolver<Eigen::MatrixXd> es{A};
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for (int i = 0; i < states; ++i) {
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for (int i = 0; i < A.rows(); ++i) {
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if (es.eigenvalues()[i].real() * es.eigenvalues()[i].real() +
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es.eigenvalues()[i].imag() * es.eigenvalues()[i].imag() <
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1) {
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continue;
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}
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Eigen::MatrixXcd E{states, states + inputs};
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E << es.eigenvalues()[i] * Eigen::MatrixXcd::Identity(states, states) - A,
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Eigen::MatrixXcd E{A.rows(), A.rows() + B.cols()};
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E << es.eigenvalues()[i] * Eigen::MatrixXcd::Identity(A.rows(), A.rows()) -
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A,
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B;
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Eigen::ColPivHouseholderQR<Eigen::MatrixXcd> qr{E};
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if (qr.rank() < states) {
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if (qr.rank() < A.rows()) {
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return false;
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}
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}
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return true;
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}
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} // namespace
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std::vector<double> GetElementsFromTrajectory(
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const frc::Trajectory& trajectory) {
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std::vector<double> elements;
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@@ -97,11 +101,11 @@ extern "C" {
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/*
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* Class: edu_wpi_first_math_WPIMathJNI
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* Method: discreteAlgebraicRiccatiEquation
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* Method: dare
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* Signature: ([D[D[D[DII[D)V
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*/
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JNIEXPORT void JNICALL
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Java_edu_wpi_first_math_WPIMathJNI_discreteAlgebraicRiccatiEquation
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Java_edu_wpi_first_math_WPIMathJNI_dare
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(JNIEnv* env, jclass, jdoubleArray A, jdoubleArray B, jdoubleArray Q,
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jdoubleArray R, jint states, jint inputs, jdoubleArray S)
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{
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@@ -124,8 +128,7 @@ Java_edu_wpi_first_math_WPIMathJNI_discreteAlgebraicRiccatiEquation
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Rmat{nativeR, inputs, inputs};
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try {
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Eigen::MatrixXd result =
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drake::math::DiscreteAlgebraicRiccatiEquation(Amat, Bmat, Qmat, Rmat);
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Eigen::MatrixXd result = frc::DARE(Amat, Bmat, Qmat, Rmat);
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env->ReleaseDoubleArrayElements(A, nativeA, 0);
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env->ReleaseDoubleArrayElements(B, nativeB, 0);
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@@ -205,7 +208,7 @@ Java_edu_wpi_first_math_WPIMathJNI_isStabilizable
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Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
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B{nativeB, states, inputs};
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bool isStabilizable = check_stabilizable(A, B);
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bool isStabilizable = IsStabilizable(A, B);
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env->ReleaseDoubleArrayElements(aSrc, nativeA, 0);
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env->ReleaseDoubleArrayElements(bSrc, nativeB, 0);
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