scipy.linalg.solve_discrete_are#

scipy.linalg.solve_discrete_are(a, b, q, r, e=None, s=None, balanced=True)[source]#

Solves the discrete-time algebraic Riccati equation (DARE).

The DARE is defined as

\[A^HXA - X - (A^HXB) (R + B^HXB)^{-1} (B^HXA) + Q = 0\]

The limitations for a solution to exist are :

All eigenvalues of \(A\) outside the unit disc, should be controllable.

The associated symplectic pencil (See Notes), should have eigenvalues sufficiently away from the unit circle.

Moreover, if e and s are not both precisely None, then the generalized version of DARE

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

is solved. When omitted, e is assumed to be the identity and s is assumed to be the zero matrix.

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: Square matrix
e(M, M) array_like, optional: Nonsingular square matrix
s(M, N) array_like, optional: Input
balancedbool: 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 discrete 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_continuous_are: Solves the continuous algebraic Riccati equation

Notes

The equation is solved by forming the extended symplectic matrix pencil, as described in [1], \(H - \lambda J\) given by the block matrices

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

and using a QZ decomposition method.

In this algorithm, the fail conditions are linked to the symmetry of the product \(U_2 U_1^{-1}\) and condition number of \(U_1\). Here, \(U\) 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 \(H\) and \(J\) rows/cols (after removing the diagonal entries) is balanced following the recipe given in [3]. If the data has small numerical noise, balancing may amplify their effects and some clean up is required.

New in version 0.11.0.

References

[1] (1,2)

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 as la
>>> a = np.array([[0, 1], [0, -1]])
>>> b = np.array([[1, 0], [2, 1]])
>>> q = np.array([[-4, -4], [-4, 7]])
>>> r = np.array([[9, 3], [3, 1]])
>>> x = la.solve_discrete_are(a, b, q, r)
>>> x
array([[-4., -4.],
       [-4.,  7.]])
>>> R = la.solve(r + b.T.dot(x).dot(b), b.T.dot(x).dot(a))
>>> np.allclose(a.T.dot(x).dot(a) - x - a.T.dot(x).dot(b).dot(R), -q)
True