zpk_algorithms

wield.control.algorithms.statespace.dense.zpk_algorithms

Classes

TupleABCDE

alias of ABCDE

Functions

DSS_c2r(A, B, C, D, E[, with_imag])
ZPK2ss_cheby_companion(Zc, Zr, Pc, Pr, k[, ...])	This is a construction of a ADBC statespace using a Chebychev companion matrix.
ZPKdict(zdict, pdict, k[, convention, ...])	Create a statespace from dictionaries of poles and zeros separated into real and imaginary parts orientation: Whether the A matrix is "upper" or "lower" triangular real Schur form
ZPKdict_chainE(zdict, pdict, k[, ...])	Create a statespace from dictionaries of poles and zeros separated into real and imaginary parts orientation: Whether the A matrix is "upper" or "lower" triangular real Schur form
chebcompanion2(c1, c2)
chebcompanion_scaled(c[, scale])	Return the scaled companion matrix of c.
poly2ss(num, den[, rescale_has, rescale_do, ...])
split_chain(ABCDEs)
ss2p(A[, E, fmt])
ss2zp(A, B, C, D[, E, idx_in, idx_out, ...])
ss2zpk(A, B, C, D[, E, idx_in, idx_out, ...])
zpk2cDSS(z, p, k[, rescale, mode])	Create a complex descriptor statespace from zpk.
zpk2rDSS(z, p, k, **kwargs)
zpk_cascade(zr, zc, pr, pc, k[, convention])
zpk_rc([Zc, Zr, Pc, Pr, k, convention, ...])	Adapter method to convert ZPK into statespace using the known methods
zpkdict_cascade(zdict, pdict, k[, ...])	Create a statespace from a cascade of ZPKs.
zpkdict_cascade_sm(zdict, pdict, k[, ...])	Create a statespace from a cascade of ZPKs.

Details

DSS_c2r(A, B, C, D, E, with_imag=False)

ZPK2ss_cheby_companion(Zc, Zr, Pc, Pr, k, orientation='upper')

This is a construction of a ADBC statespace using a Chebychev companion matrix.

ZPKdict(zdict, pdict, k, convention='scipy', orientation='lower')

Create a statespace from dictionaries of poles and zeros separated into real and imaginary parts orientation: Whether the A matrix is “upper” or “lower” triangular real Schur form

ZPKdict_chainE(zdict, pdict, k, convention='scipy', orientation='lower')

Create a statespace from dictionaries of poles and zeros separated into real and imaginary parts orientation: Whether the A matrix is “upper” or “lower” triangular real Schur form

chebcompanion2(c1, c2)

chebcompanion_scaled(c, scale=None)

Return the scaled companion matrix of c.

The basis polynomials are scaled so that the companion matrix is symmetric when c is a Chebyshev basis polynomial. This provides better eigenvalue estimates than the unscaled case and for basis polynomials the eigenvalues are guaranteed to be real if numpy.linalg.eigvalsh is used to obtain them.

Parameters:

c (array_like) – 1-D array of Chebyshev series coefficients ordered from low to high degree.

Returns:

mat – Scaled companion matrix of dimensions (deg, deg).

Return type:

ndarray

Notes

Added in version 1.7.0.

poly2ss(num, den, rescale_has=None, rescale_do=None, mode='CCF')

split_chain(ABCDEs)

ss2p(A, E=None, fmt='scipy')

ss2zp(A, B, C, D, E=None, idx_in=None, idx_out=None, Q_rank_cutoff=1e-15, Q_rank_cutoff_unstable=None, fmt='scipy', allow_MIMO=False)

ss2zpk(A, B, C, D, E=None, idx_in=None, idx_out=None, Q_rank_cutoff=1e-15, Q_rank_cutoff_unstable=None, F_match_Hz=None, fmt='scipy')

zpk2cDSS(z, p, k, rescale=None, mode='CCF')

Create a complex descriptor statespace from zpk. The real part cane then be extracted.

NOT USED.

zpk2rDSS(z, p, k, **kwargs)

zpk_cascade(zr, zc, pr, pc, k, convention='scipy')

zpk_rc(Zc=[], Zr=[], Pc=[], Pr=[], k=1, convention='scipy', orientation='lower', method='chain_poly')

Adapter method to convert ZPK into statespace using the known methods

zpkdict_cascade(zdict, pdict, k, convention='scipy', bad_sort=False)

Create a statespace from a cascade of ZPKs.

This one is a bit simpler than the other one, and appears to work as well. And has the most simple sort

zpkdict_cascade_sm(zdict, pdict, k, convention='scipy', bad_sort=False)

Create a statespace from a cascade of ZPKs.

This one is a bit simpler than the other one, and appears to work as well. And has the most simple sort