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[wpimath] Add static matrix support to DARE solver (#5536)

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Using static matrices where possible results in a 2x performance
improvement.
This commit is contained in:
Tyler Veness
2023-08-14 09:15:58 -07:00
committed by GitHub
parent 394cfeadbd
commit 03764dfe93
9 changed files with 354 additions and 436 deletions
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359
wpimath/src/main/native/include/frc/DARE.h
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@@ -4,86 +4,88 @@
#pragma once
#include <wpi/SymbolExports.h>
#include <stdexcept>
#include <string>
#include "Eigen/Cholesky"
#include "Eigen/Core"
#include "Eigen/LU"
#include "frc/StateSpaceUtil.h"
#include "frc/fmt/Eigen.h"
// Works cited:
//
// [1] E. K.-W. Chu, H.-Y. Fan, W.-W. Lin & C.-S. Wang "Structure-Preserving
// Algorithms for Periodic Discrete-Time Algebraic Riccati Equations",
// International Journal of Control, 77:8, 767-788, 2004.
// DOI: 10.1080/00207170410001714988
namespace frc {
namespace detail {
/**
* Computes the unique stabilizing solution X to the discrete-time algebraic
* Riccati equation:
*
* AᵀXA − X − AᵀXB(BᵀXB + R)⁻¹BᵀXA + Q = 0
* Checks the preconditions of A, B, and Q for the DARE solver.
*
* @tparam States Number of states.
* @tparam Inputs Number of inputs.
* @param A The system matrix.
* @param B The input matrix.
* @param Q The state cost matrix.
* @param R The input cost matrix.
* @throws std::invalid_argument if Q isn't symmetric positive semidefinite.
* @throws std::invalid_argument if R isn't symmetric positive definite.
* @throws std::invalid_argument if the (A, B) pair isn't stabilizable.
* @throws std::invalid_argument if the (A, C) pair where Q = CᵀC isn't
* detectable.
*/
WPILIB_DLLEXPORT
Eigen::MatrixXd DARE(const Eigen::Ref<const Eigen::MatrixXd>& A,
const Eigen::Ref<const Eigen::MatrixXd>& B,
const Eigen::Ref<const Eigen::MatrixXd>& Q,
const Eigen::Ref<const Eigen::MatrixXd>& R);
template <int States, int Inputs>
void CheckDARE_ABQ(const Eigen::Matrix<double, States, States>& A,
const Eigen::Matrix<double, States, Inputs>& B,
const Eigen::Matrix<double, States, States>& Q) {
// Require Q be symmetric
if ((Q - Q.transpose()).norm() > 1e-10) {
std::string msg = fmt::format("Q isn't symmetric!\n\nQ =\n{}\n", Q);
throw std::invalid_argument(msg);
}
/**
Computes the unique stabilizing solution X to the discrete-time algebraic
Riccati equation:
// Require Q be positive semidefinite
//
// If Q is a symmetric matrix with a decomposition LDLᵀ, the number of
// positive, negative, and zero diagonal entries in D equals the number of
// positive, negative, and zero eigenvalues respectively in Q (see
// https://en.wikipedia.org/wiki/Sylvester's_law_of_inertia).
//
// Therefore, D having no negative diagonal entries is sufficient to prove Q
// is positive semidefinite.
auto Q_ldlt = Q.ldlt();
if (Q_ldlt.info() != Eigen::Success ||
(Q_ldlt.vectorD().array() < 0.0).any()) {
std::string msg =
fmt::format("Q isn't positive semidefinite!\n\nQ =\n{}\n", Q);
throw std::invalid_argument(msg);
}
AᵀXA − X − (AᵀXB + N)(BᵀXB + R)⁻¹(BᵀXA + Nᵀ) + Q = 0
// Require (A, B) pair be stabilizable
if (!IsStabilizable<States, Inputs>(A, B)) {
std::string msg = fmt::format(
"The (A, B) pair isn't stabilizable!\n\nA =\n{}\nB =\n{}\n", A, B);
throw std::invalid_argument(msg);
}
This overload of the DARE is useful for finding the control law uₖ that
minimizes the following cost function subject to xₖ₊₁ = Axₖ + Buₖ.
// Require (A, C) pair be detectable where Q = CᵀC
{
Eigen::Matrix<double, States, States> C =
Eigen::Matrix<double, States, States>{Q_ldlt.matrixL()} *
Q_ldlt.vectorD().cwiseSqrt().asDiagonal();
@verbatim
∞ [xₖ]ᵀ[Q N][xₖ]
J = Σ [uₖ] [Nᵀ R][uₖ] ΔT
k=0
@endverbatim
This is a more general form of the following. The linear-quadratic regulator
is the feedback control law uₖ that minimizes the following cost function
subject to xₖ₊₁ = Axₖ + Buₖ:
@verbatim
∞
J = Σ (xₖᵀQxₖ + uₖᵀRuₖ) ΔT
k=0
@endverbatim
This can be refactored as:
@verbatim
∞ [xₖ]ᵀ[Q 0][xₖ]
J = Σ [uₖ] [0 R][uₖ] ΔT
k=0
@endverbatim
@param A The system matrix.
@param B The input matrix.
@param Q The state cost matrix.
@param R The input cost matrix.
@param N The state-input cross cost matrix.
@throws std::invalid_argument if Q − NR⁻¹Nᵀ isn't symmetric positive
semidefinite.
@throws std::invalid_argument if R isn't symmetric positive definite.
@throws std::invalid_argument if the (A, B) pair isn't stabilizable.
@throws std::invalid_argument if the (A, C) pair where Q = CᵀC isn't detectable.
*/
WPILIB_DLLEXPORT
Eigen::MatrixXd DARE(const Eigen::Ref<const Eigen::MatrixXd>& A,
const Eigen::Ref<const Eigen::MatrixXd>& B,
const Eigen::Ref<const Eigen::MatrixXd>& Q,
const Eigen::Ref<const Eigen::MatrixXd>& R,
const Eigen::Ref<const Eigen::MatrixXd>& N);
namespace internal {
if (!IsDetectable<States, States>(A, C)) {
std::string msg = fmt::format(
"The (A, C) pair where Q = CᵀC isn't detectable!\n\nA =\n{}\nQ "
"=\n{}\n",
A, Q);
throw std::invalid_argument(msg);
}
}
}
/**
* Computes the unique stabilizing solution X to the discrete-time algebraic
@@ -101,16 +103,77 @@ namespace internal {
* </ul>
* Only use this function if you're sure the preconditions are met.
*
* @tparam States Number of states.
* @tparam Inputs Number of inputs.
* @param A The system matrix.
* @param B The input matrix.
* @param Q The state cost matrix.
* @param R The input cost matrix.
* @param R_llt The LLT decomposition of the input cost matrix.
*/
WPILIB_DLLEXPORT
Eigen::MatrixXd DARE(const Eigen::Ref<const Eigen::MatrixXd>& A,
const Eigen::Ref<const Eigen::MatrixXd>& B,
const Eigen::Ref<const Eigen::MatrixXd>& Q,
const Eigen::Ref<const Eigen::MatrixXd>& R);
template <int States, int Inputs>
Eigen::Matrix<double, States, States> DARE(
const Eigen::Matrix<double, States, States>& A,
const Eigen::Matrix<double, States, Inputs>& B,
const Eigen::Matrix<double, States, States>& Q,
const Eigen::LLT<Eigen::Matrix<double, Inputs, Inputs>>& R_llt) {
using StateMatrix = Eigen::Matrix<double, States, States>;
// Implements the SDA algorithm on page 5 of [1].
// A₀ = A
StateMatrix A_k = A;
// G₀ = BR⁻¹Bᵀ
//
// See equation (4) of [1].
StateMatrix G_k = B * R_llt.solve(B.transpose());
// H₀ = Q
//
// See equation (4) of [1].
StateMatrix H_k;
StateMatrix H_k1 = Q;
do {
H_k = H_k1;
// W = I + GₖHₖ
StateMatrix W = StateMatrix::Identity(H_k.rows(), H_k.cols()) + G_k * H_k;
auto W_solver = W.lu();
// Solve WV₁ = Aₖ for V₁
StateMatrix V_1 = W_solver.solve(A_k);
// Solve V₂Wᵀ = Gₖ for V₂
//
// We want to put V₂Wᵀ = Gₖ into Ax = b form so we can solve it more
// efficiently.
//
// V₂Wᵀ = Gₖ
// (V₂Wᵀ)ᵀ = Gₖᵀ
// WV₂ᵀ = Gₖᵀ
//
// The solution of Ax = b can be found via x = A.solve(b).
//
// V₂ᵀ = W.solve(Gₖᵀ)
// V₂ = W.solve(Gₖᵀ)ᵀ
StateMatrix V_2 = W_solver.solve(G_k.transpose()).transpose();
// Gₖ₊₁ = Gₖ + AₖV₂Aₖᵀ
G_k += A_k * V_2 * A_k.transpose();
// Hₖ₊₁ = Hₖ + V₁ᵀHₖAₖ
H_k1 = H_k + V_1.transpose() * H_k * A_k;
// Aₖ₊₁ = AₖV₁
A_k *= V_1;
// while |Hₖ₊₁ − Hₖ| > ε |Hₖ₊₁|
} while ((H_k1 - H_k).norm() > 1e-10 * H_k1.norm());
return H_k1;
}
/**
Computes the unique stabilizing solution X to the discrete-time algebraic
@@ -155,18 +218,166 @@ performance. The solver may hang if any of the following occur:
</ul>
Only use this function if you're sure the preconditions are met.
@tparam States Number of states.
@tparam Inputs Number of inputs.
@param A The system matrix.
@param B The input matrix.
@param Q The state cost matrix.
@param R_llt The LLT decomposition of the input cost matrix.
@param N The state-input cross cost matrix.
*/
template <int States, int Inputs>
Eigen::Matrix<double, States, States> DARE(
const Eigen::Matrix<double, States, States>& A,
const Eigen::Matrix<double, States, Inputs>& B,
const Eigen::Matrix<double, States, States>& Q,
const Eigen::LLT<Eigen::Matrix<double, Inputs, Inputs>>& R_llt,
const Eigen::Matrix<double, Inputs, Inputs>& N) {
// This is a change of variables to make the DARE that includes Q, R, and N
// cost matrices fit the form of the DARE that includes only Q and R cost
// matrices.
//
// This is equivalent to solving the original DARE:
//
// A₂ᵀXA₂ − X − A₂ᵀXB(BᵀXB + R)⁻¹BᵀXA₂ + Q₂ = 0
//
// where A₂ and Q₂ are a change of variables:
//
// A₂ = A − BR⁻¹Nᵀ and Q₂ = Q − NR⁻¹Nᵀ
return detail::DARE<States, Inputs>(A - B * R_llt.solve(N.transpose()), B,
Q - N * R_llt.solve(N.transpose()),
R_llt);
}
} // namespace detail
/**
* Computes the unique stabilizing solution X to the discrete-time algebraic
* Riccati equation:
*
* AᵀXA − X − AᵀXB(BᵀXB + R)⁻¹BᵀXA + Q = 0
*
* @tparam States Number of states.
* @tparam Inputs Number of inputs.
* @param A The system matrix.
* @param B The input matrix.
* @param Q The state cost matrix.
* @param R The input cost matrix.
* @throws std::invalid_argument if Q isn't symmetric positive semidefinite.
* @throws std::invalid_argument if R isn't symmetric positive definite.
* @throws std::invalid_argument if the (A, B) pair isn't stabilizable.
* @throws std::invalid_argument if the (A, C) pair where Q = CᵀC isn't
* detectable.
*/
template <int States, int Inputs>
Eigen::Matrix<double, States, States> DARE(
const Eigen::Matrix<double, States, States>& A,
const Eigen::Matrix<double, States, Inputs>& B,
const Eigen::Matrix<double, States, States>& Q,
const Eigen::Matrix<double, Inputs, Inputs>& R) {
// Require R be symmetric
if ((R - R.transpose()).norm() > 1e-10) {
std::string msg = fmt::format("R isn't symmetric!\n\nR =\n{}\n", R);
throw std::invalid_argument(msg);
}
// Require R be positive definite
auto R_llt = R.llt();
if (R_llt.info() != Eigen::Success) {
std::string msg = fmt::format("R isn't positive definite!\n\nR =\n{}\n", R);
throw std::invalid_argument(msg);
}
detail::CheckDARE_ABQ<States, Inputs>(A, B, Q);
return detail::DARE<States, Inputs>(A, B, Q, R_llt);
}
/**
Computes the unique stabilizing solution X to the discrete-time algebraic
Riccati equation:
AᵀXA − X − (AᵀXB + N)(BᵀXB + R)⁻¹(BᵀXA + Nᵀ) + Q = 0
This overload of the DARE is useful for finding the control law uₖ that
minimizes the following cost function subject to xₖ₊₁ = Axₖ + Buₖ.
@verbatim
∞ [xₖ]ᵀ[Q N][xₖ]
J = Σ [uₖ] [Nᵀ R][uₖ] ΔT
k=0
@endverbatim
This is a more general form of the following. The linear-quadratic regulator
is the feedback control law uₖ that minimizes the following cost function
subject to xₖ₊₁ = Axₖ + Buₖ:
@verbatim
∞
J = Σ (xₖᵀQxₖ + uₖᵀRuₖ) ΔT
k=0
@endverbatim
This can be refactored as:
@verbatim
∞ [xₖ]ᵀ[Q 0][xₖ]
J = Σ [uₖ] [0 R][uₖ] ΔT
k=0
@endverbatim
@tparam States Number of states.
@tparam Inputs Number of inputs.
@param A The system matrix.
@param B The input matrix.
@param Q The state cost matrix.
@param R The input cost matrix.
@param N The state-input cross cost matrix.
@throws std::invalid_argument if Q − NR⁻¹Nᵀ isn't symmetric positive
semidefinite.
@throws std::invalid_argument if R isn't symmetric positive definite.
@throws std::invalid_argument if the (A, B) pair isn't stabilizable.
@throws std::invalid_argument if the (A, C) pair where Q = CᵀC isn't detectable.
*/
WPILIB_DLLEXPORT
Eigen::MatrixXd DARE(const Eigen::Ref<const Eigen::MatrixXd>& A,
const Eigen::Ref<const Eigen::MatrixXd>& B,
const Eigen::Ref<const Eigen::MatrixXd>& Q,
const Eigen::Ref<const Eigen::MatrixXd>& R,
const Eigen::Ref<const Eigen::MatrixXd>& N);
template <int States, int Inputs>
Eigen::Matrix<double, States, States> DARE(
const Eigen::Matrix<double, States, States>& A,
const Eigen::Matrix<double, States, Inputs>& B,
const Eigen::Matrix<double, States, States>& Q,
const Eigen::Matrix<double, Inputs, Inputs>& R,
const Eigen::Matrix<double, States, Inputs>& N) {
// Require R be symmetric
if ((R - R.transpose()).norm() > 1e-10) {
std::string msg = fmt::format("R isn't symmetric!\n\nR =\n{}\n", R);
throw std::invalid_argument(msg);
}
// Require R be positive definite
auto R_llt = R.llt();
if (R_llt.info() != Eigen::Success) {
std::string msg = fmt::format("R isn't positive definite!\n\nR =\n{}\n", R);
throw std::invalid_argument(msg);
}
// This is a change of variables to make the DARE that includes Q, R, and N
// cost matrices fit the form of the DARE that includes only Q and R cost
// matrices.
//
// This is equivalent to solving the original DARE:
//
// A₂ᵀXA₂ − X − A₂ᵀXB(BᵀXB + R)⁻¹BᵀXA₂ + Q₂ = 0
//
// where A₂ and Q₂ are a change of variables:
//
// A₂ = A − BR⁻¹Nᵀ and Q₂ = Q − NR⁻¹Nᵀ
Eigen::Matrix<double, States, States> A_2 =
A - B * R_llt.solve(N.transpose());
Eigen::Matrix<double, States, States> Q_2 =
Q - N * R_llt.solve(N.transpose());
detail::CheckDARE_ABQ<States, Inputs>(A_2, B, Q_2);
return detail::DARE<States, Inputs>(A_2, B, Q_2, R_llt);
}
} // namespace internal
} // namespace frc