DARE.h

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// Copyright (c) FIRST and other WPILib contributors.
// Open Source Software; you can modify and/or share it under the terms of
// the WPILib BSD license file in the root directory of this project.
 
#pragma once
 
#include <string_view>
 
#include <Eigen/Cholesky>
#include <Eigen/Core>
#include <Eigen/LU>
#include <wpi/expected>
 
#include "frc/StateSpaceUtil.h"
 
namespace frc {
 
/**
 * Errors the DARE solver can encounter.
 */
enum class DAREError {
  /// Q was not symmetric.
  QNotSymmetric,
  /// Q was not positive semidefinite.
  QNotPositiveSemidefinite,
  /// R was not symmetric.
  RNotSymmetric,
  /// R was not positive definite.
  RNotPositiveDefinite,
  /// (A, B) pair was not stabilizable.
  ABNotStabilizable,
  /// (A, C) pair where Q = CᵀC was not detectable.
  ACNotDetectable,
};
 
/**
 * Converts the given DAREError enum to a string.
 */
constexpr std::string_view to_string(const DAREError& error) {
  switch (error) {
    case DAREError::QNotSymmetric:
      return "Q was not symmetric.";
    case DAREError::QNotPositiveSemidefinite:
      return "Q was not positive semidefinite.";
    case DAREError::RNotSymmetric:
      return "R was not symmetric.";
    case DAREError::RNotPositiveDefinite:
      return "R was not positive definite.";
    case DAREError::ABNotStabilizable:
      return "(A, B) pair was not stabilizable.";
    case DAREError::ACNotDetectable:
      return "(A, C) pair where Q = CᵀC was not detectable.";
  }
 
  return "";
}
 
namespace detail {
 
/**
 * Computes the unique stabilizing solution X to the discrete-time algebraic
 * Riccati equation:
 *
 *   AᵀXA − X − AᵀXB(BᵀXB + R)⁻¹BᵀXA + Q = 0
 *
 * This internal function skips expensive precondition checks for increased
 * performance. The solver may hang if any of the following occur:
 * <ul>
 *   <li>Q isn't symmetric positive semidefinite</li>
 *   <li>R isn't symmetric positive definite</li>
 *   <li>The (A, B) pair isn't stabilizable</li>
 *   <li>The (A, C) pair where Q = CᵀC isn't detectable</li>
 * </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.
 * @return Solution to the DARE.
 */
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 SDA algorithm on p. 5 of [1] (initial A, G, H are from (4)).
  //
  // [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
 
  // A₀ = A
  // G₀ = BR⁻¹Bᵀ
  // H₀ = Q
  StateMatrix A_k = A;
  StateMatrix G_k = B * R_llt.solve(B.transpose());
  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
    // Hₖ₊₁ = Hₖ + V₁ᵀHₖAₖ
    // Aₖ₊₁ = AₖV₁
    G_k += A_k * V_2 * A_k.transpose();
    H_k1 = H_k + V_1.transpose() * H_k * A_k;
    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;
}
 
}  // 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.
 * @param checkPreconditions Whether to check preconditions (30% less time if
 *   user is sure precondtions are already met).
 * @return Solution to the DARE on success, or DAREError on failure.
 */
template <int States, int Inputs>
wpi::expected<Eigen::Matrix<double, States, States>, DAREError> 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,
    bool checkPreconditions = true) {
  if (checkPreconditions) {
    // Require R be symmetric
    if ((R - R.transpose()).norm() > 1e-10) {
      return wpi::unexpected{DAREError::RNotSymmetric};
    }
  }
 
  // Require R be positive definite
  auto R_llt = R.llt();
  if (R_llt.info() != Eigen::Success) {
    return wpi::unexpected{DAREError::RNotPositiveDefinite};
  }
 
  if (checkPreconditions) {
    // Require Q be symmetric
    if ((Q - Q.transpose()).norm() > 1e-10) {
      return wpi::unexpected{DAREError::QNotSymmetric};
    }
 
    // 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()) {
      return wpi::unexpected{DAREError::QNotPositiveSemidefinite};
    }
 
    // Require (A, B) pair be stabilizable
    if (!IsStabilizable<States, Inputs>(A, B)) {
      return wpi::unexpected{DAREError::ABNotStabilizable};
    }
 
    // Require (A, C) pair be detectable where Q = CᵀC
    //
    // Q = CᵀC = PᵀLDLᵀP
    // C = √(D)LᵀP
    Eigen::Matrix<double, States, States> C =
        Q_ldlt.vectorD().cwiseSqrt().asDiagonal() *
        Eigen::Matrix<double, States, States>{Q_ldlt.matrixL().transpose()} *
        Q_ldlt.transpositionsP();
 
    if (!IsDetectable<States, States>(A, C)) {
      return wpi::unexpected{DAREError::ACNotDetectable};
    }
  }
 
  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 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
 
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.
@param checkPreconditions Whether to check preconditions (30% less time if user
  is sure precondtions are already met).
@return Solution to the DARE on success, or DAREError on failure.
*/
template <int States, int Inputs>
wpi::expected<Eigen::Matrix<double, States, States>, DAREError> 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,
    bool checkPreconditions = true) {
  if (checkPreconditions) {
    // Require R be symmetric
    if ((R - R.transpose()).norm() > 1e-10) {
      return wpi::unexpected{DAREError::RNotSymmetric};
    }
  }
 
  // Require R be positive definite
  auto R_llt = R.llt();
  if (R_llt.info() != Eigen::Success) {
    return wpi::unexpected{DAREError::RNotPositiveDefinite};
  }
 
  // 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());
 
  if (checkPreconditions) {
    // Require Q be symmetric
    if ((Q_2 - Q_2.transpose()).norm() > 1e-10) {
      return wpi::unexpected{DAREError::QNotSymmetric};
    }
 
    // 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_2.ldlt();
    if (Q_ldlt.info() != Eigen::Success ||
        (Q_ldlt.vectorD().array() < 0.0).any()) {
      return wpi::unexpected{DAREError::QNotPositiveSemidefinite};
    }
 
    // Require (A, B) pair be stabilizable
    if (!IsStabilizable<States, Inputs>(A_2, B)) {
      return wpi::unexpected{DAREError::ABNotStabilizable};
    }
 
    // Require (A, C) pair be detectable where Q = CᵀC
    //
    // Q = CᵀC = PᵀLDLᵀP
    // C = √(D)LᵀP
    Eigen::Matrix<double, States, States> C =
        Q_ldlt.vectorD().cwiseSqrt().asDiagonal() *
        Eigen::Matrix<double, States, States>{Q_ldlt.matrixL().transpose()} *
        Q_ldlt.transpositionsP();
 
    if (!IsDetectable<States, States>(A_2, C)) {
      return wpi::unexpected{DAREError::ACNotDetectable};
    }
  }
 
  return detail::DARE<States, Inputs>(A_2, B, Q_2, R_llt);
}
 
}  // namespace frc