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[wpimath] Support dynamic matrix sizes in StateSpaceUtil (#7942)
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@@ -123,6 +123,22 @@ constexpr Matrixd<N, N> MakeCostMatrix(const std::array<double, N>& costs) {
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return result;
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}
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/**
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* Creates a cost matrix from the given vector for use with LQR.
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*
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* The cost matrix is constructed using Bryson's rule. The inverse square of
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* each element in the input is placed on the cost matrix diagonal. If a
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* tolerance is infinity, its cost matrix entry is set to zero.
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*
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* @param costs A possibly variable length container. For a Q matrix, its
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* elements are the maximum allowed excursions of the states from
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* the reference. For an R matrix, its elements are the maximum
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* allowed excursions of the control inputs from no actuation.
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* @return State excursion or control effort cost matrix.
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*/
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WPILIB_DLLEXPORT Eigen::MatrixXd MakeCostMatrix(
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const std::span<const double> costs);
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/**
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* Creates a covariance matrix from the given vector for use with Kalman
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* filters.
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@@ -152,6 +168,21 @@ constexpr Matrixd<N, N> MakeCovMatrix(const std::array<double, N>& stdDevs) {
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return result;
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}
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/**
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* Creates a covariance matrix from the given vector for use with Kalman
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* filters.
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*
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* Each element is squared and placed on the covariance matrix diagonal.
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*
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* @param stdDevs A possibly variable length container. For a Q matrix, its
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* elements are the standard deviations of each state from how
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* the model behaves. For an R matrix, its elements are the
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* standard deviations for each output measurement.
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* @return Process noise or measurement noise covariance matrix.
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*/
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WPILIB_DLLEXPORT Eigen::MatrixXd MakeCovMatrix(
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const std::span<const double> stdDevs);
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template <std::same_as<double>... Ts>
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Vectord<sizeof...(Ts)> MakeWhiteNoiseVector(Ts... stdDevs) {
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std::random_device rd;
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@@ -200,6 +231,17 @@ Vectord<N> MakeWhiteNoiseVector(const std::array<double, N>& stdDevs) {
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return result;
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}
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/**
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* Creates a vector of normally distributed white noise with the given noise
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* intensities for each element.
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*
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* @param stdDevs A possibly variable length container whose elements are the
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* standard deviations of each element of the noise vector.
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* @return White noise vector.
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*/
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WPILIB_DLLEXPORT Eigen::VectorXd MakeWhiteNoiseVector(
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const std::span<const double> stdDevs);
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/**
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* Converts a Pose2d into a vector of [x, y, theta].
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*
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@@ -311,6 +353,10 @@ bool IsDetectable(const Matrixd<States, States>& A,
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return IsStabilizable<States, Outputs>(A.transpose(), C.transpose());
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}
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extern template WPILIB_DLLEXPORT bool
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IsDetectable<Eigen::Dynamic, Eigen::Dynamic>(const Eigen::MatrixXd& A,
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const Eigen::MatrixXd& C);
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/**
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* Converts a Pose2d into a vector of [x, y, theta].
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*
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@@ -341,12 +387,17 @@ constexpr Vectord<Inputs> ClampInputMaxMagnitude(const Vectord<Inputs>& u,
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const Vectord<Inputs>& umin,
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const Vectord<Inputs>& umax) {
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Vectord<Inputs> result;
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for (int i = 0; i < Inputs; ++i) {
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for (int i = 0; i < u.rows(); ++i) {
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result(i) = std::clamp(u(i), umin(i), umax(i));
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}
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return result;
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}
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extern template WPILIB_DLLEXPORT Eigen::VectorXd
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ClampInputMaxMagnitude<Eigen::Dynamic>(const Eigen::VectorXd& u,
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const Eigen::VectorXd& umin,
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const Eigen::VectorXd& umax);
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/**
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* Renormalize all inputs if any exceeds the maximum magnitude. Useful for
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* systems such as differential drivetrains.
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@@ -366,4 +417,9 @@ Vectord<Inputs> DesaturateInputVector(const Vectord<Inputs>& u,
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}
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return u;
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}
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extern template WPILIB_DLLEXPORT Eigen::VectorXd
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DesaturateInputVector<Eigen::Dynamic>(const Eigen::VectorXd& u,
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double maxMagnitude);
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} // namespace frc
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