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Repeated Poles

The above summary of state-space diagonalization works as stated when the modes (poles) of the system are distinct. When there are two or more resonant modes corresponding to the same ``natural frequency'' (eigenvalue of $ A$ ), then there are two further subcases: If the eigenvectors corresponding to the repeated eigenvalue (pole) are linearly independent, then the modes are independent and can be treated as distinct (the system can be diagonalized). Otherwise, we say the equal modes are coupled.

The coupled-repeated-poles situation is detected when the matrix of eigenvectors V returned by the eig matlab function [e.g., by saying [V,D] = eig(A)] turns out to be singular. Singularity of V can be defined as when its condition number [cond(V)] exceeds some threshold, such as 1E7. In this case, the linearly dependent eigenvectors can be replaced by so-called generalized eigenvectors [58]. Use of that similarity transformation then produces a ``block diagonalized'' system instead of a diagonalized system, and one of the blocks along the diagonal will be a $ k\times k$ matrix corresponding to the pole repeated $ k$ times.

Connecting with the discussion regarding repeated poles in §6.8.5, the $ k\times k$ Jordan block corresponding to a pole repeated $ k$ times plays exactly the same role of repeated poles encountered in a partial-fraction expansion, giving rise to terms in the impulse response proportional to $ n\lambda ^n$ , $ n^2\lambda ^n$ , and so on, up to $ n^{k-1}\lambda ^n$ , where $ \lambda$ denotes the repeated pole itself (i.e., the repeated eigenvalue of the state-transition matrix $ A$ ).

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``Introduction to Digital Filters with Audio Applications'', by Julius O. Smith III, (September 2007 Edition)
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Center for Computer Research in Music and Acoustics (CCRMA),   Stanford University