Cross Gramian

In control theory, the cross Gramian (${\displaystyle W_{X}}$, also referred to by ${\displaystyle W_{CO}}$) is a Gramian matrix used to determine how controllable and observable a linear system is.[1][2]

For the stable time-invariant linear system

${\displaystyle {\dot {x}}=Ax+Bu\,}$

${\displaystyle y=Cx\,}$

the cross Gramian is defined as:

${\displaystyle W_{X}:=\int _{0}^{\infty }e^{At}BCe^{At}dt\,}$

and thus also given by the solution to the Sylvester equation:

${\displaystyle AW_{X}+W_{X}A=-BC\,}$

This means the cross Gramian is not strictly a Gramian matrix, since it is generally neither positive semi-definite nor symmetric.

The triple ${\displaystyle (A,B,C)}$ is controllable and observable, and hence minimal, if and only if the matrix ${\displaystyle W_{X}}$ is nonsingular, (i.e. ${\displaystyle W_{X}}$ has full rank, for any ${\displaystyle t>0}$).

If the associated system ${\displaystyle (A,B,C)}$ is furthermore symmetric, such that there exists a transformation ${\displaystyle J}$ with

${\displaystyle AJ=JA^{T}\,}$

${\displaystyle B=JC^{T}\,}$

then the absolute value of the eigenvalues of the cross Gramian equal Hankel singular values:[3]

${\displaystyle |\lambda (W_{X})|={\sqrt {\lambda (W_{C}W_{O})}}.\,}$

Thus the direct truncation of the Eigendecomposition of the cross Gramian allows model order reduction (see ) without a balancing procedure as opposed to balanced truncation.

The cross Gramian has also applications in decentralized control, sensitivity analysis, and the inverse scattering transform.[4][5]

See also

• Controllability Gramian
• Observability Gramian

References

1. Fortuna, Luigi; Frasca, Mattia (2012). Optimal and Robust Control: Advanced Topics with MATLAB. CRC Press. pp. 83–. ISBN 9781466501911. Retrieved 29 April 2013.
2. Antoulas, Athanasios C. (2005). Approximation of Large-Scale Dynamical Systems. SIAM. doi:10.1137/1.9780898718713. ISBN 9780898715293.
3. Fernando, K.; Nicholson, H. (February 1983). "On the structure of balanced and other principal representations of SISO systems". IEEE Transactions on Automatic Control. 28 (2): 228–231. doi:10.1109/tac.1983.1103195. ISSN 0018-9286.
4. Himpe, C. (2018). "emgr -- The Empirical Gramian Framework". Algorithms. 11 (7): 91. doi:10.3390/a11070091.
5. Blower, G.; Newsham, S. (2021). "Tau functions for linear systems" (PDF). Operator theory advances and applications: IWOTA Lisbon 2019.