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Module « scipy.linalg »

Fonction solve_continuous_are - module scipy.linalg

Signature de la fonction solve_continuous_are

def solve_continuous_are(a, b, q, r, e=None, s=None, balanced=True) 

Description

help(scipy.linalg.solve_continuous_are)

Solves the continuous-time algebraic Riccati equation (CARE).

The CARE is defined as

.. math::

      X A + A^H X - X B R^{-1} B^H X + Q = 0

The limitations for a solution to exist are :

    * All eigenvalues of :math:`A` on the right half plane, should be
      controllable.

    * The associated hamiltonian pencil (See Notes), should have
      eigenvalues sufficiently away from the imaginary axis.

Moreover, if ``e`` or ``s`` is not precisely ``None``, then the
generalized version of CARE

.. math::

      E^HXA + A^HXE - (E^HXB + S) R^{-1} (B^HXE + S^H) + Q = 0

is solved. When omitted, ``e`` is assumed to be the identity and ``s``
is assumed to be the zero matrix with sizes compatible with ``a`` and
``b``, respectively.

Parameters
----------
a : (M, M) array_like
    Square matrix
b : (M, N) array_like
    Input
q : (M, M) array_like
    Input
r : (N, N) array_like
    Nonsingular square matrix
e : (M, M) array_like, optional
    Nonsingular square matrix
s : (M, N) array_like, optional
    Input
balanced : bool, optional
    The boolean that indicates whether a balancing step is performed
    on the data. The default is set to True.

Returns
-------
x : (M, M) ndarray
    Solution to the continuous-time algebraic Riccati equation.

Raises
------
LinAlgError
    For cases where the stable subspace of the pencil could not be
    isolated. See Notes section and the references for details.

See Also
--------
solve_discrete_are : Solves the discrete-time algebraic Riccati equation

Notes
-----
The equation is solved by forming the extended hamiltonian matrix pencil,
as described in [1]_, :math:`H - \lambda J` given by the block matrices ::

    [ A    0    B ]             [ E   0    0 ]
    [-Q  -A^H  -S ] - \lambda * [ 0  E^H   0 ]
    [ S^H B^H   R ]             [ 0   0    0 ]

and using a QZ decomposition method.

In this algorithm, the fail conditions are linked to the symmetry
of the product :math:`U_2 U_1^{-1}` and condition number of
:math:`U_1`. Here, :math:`U` is the 2m-by-m matrix that holds the
eigenvectors spanning the stable subspace with 2-m rows and partitioned
into two m-row matrices. See [1]_ and [2]_ for more details.

In order to improve the QZ decomposition accuracy, the pencil goes
through a balancing step where the sum of absolute values of
:math:`H` and :math:`J` entries (after removing the diagonal entries of
the sum) is balanced following the recipe given in [3]_.

.. versionadded:: 0.11.0

References
----------
.. [1]  P. van Dooren , "A Generalized Eigenvalue Approach For Solving
   Riccati Equations.", SIAM Journal on Scientific and Statistical
   Computing, Vol.2(2), :doi:`10.1137/0902010`

.. [2] A.J. Laub, "A Schur Method for Solving Algebraic Riccati
   Equations.", Massachusetts Institute of Technology. Laboratory for
   Information and Decision Systems. LIDS-R ; 859. Available online :
   http://hdl.handle.net/1721.1/1301

.. [3] P. Benner, "Symplectic Balancing of Hamiltonian Matrices", 2001,
   SIAM J. Sci. Comput., 2001, Vol.22(5), :doi:`10.1137/S1064827500367993`

Examples
--------
Given `a`, `b`, `q`, and `r` solve for `x`:

>>> import numpy as np
>>> from scipy import linalg
>>> a = np.array([[4, 3], [-4.5, -3.5]])
>>> b = np.array([[1], [-1]])
>>> q = np.array([[9, 6], [6, 4.]])
>>> r = 1
>>> x = linalg.solve_continuous_are(a, b, q, r)
>>> x
array([[ 21.72792206,  14.48528137],
       [ 14.48528137,   9.65685425]])
>>> np.allclose(a.T.dot(x) + x.dot(a)-x.dot(b).dot(b.T).dot(x), -q)
True

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