Aug 6, 2024 · Jul 11, 2024
diff --git a/control/modelsimp.py b/control/modelsimp.py
 from .statesp import StateSpace
 from .statefbk import gram

 __all__ = ['hsvd', 'balred', 'modred', 'era', 'markov', 'minreal']
 __all__ = ['hankel_singular_values', 'balanced_reduction', 'model_reduction',
           'minimal_realization', 'eigensys_realization', 'markov', 'hsvd',
           'balred', 'modred', 'minreal', 'era']


 # Hankel Singular Value Decomposition
 #
 # The following returns the Hankel singular values, which are singular values
 # of the matrix formed by multiplying the controllability and observability
 # Gramians
 defhsvd(sys):
 defhankel_singular_values(sys):
    """Calculate the Hankel singular values.

    Parameters
    return hsv[::-1]


 defmodred(sys, ELIM, method='matchdc'):
 defmodel_reduction(sys, ELIM, method='matchdc'):
    """
    Model reduction of `sys` by eliminating the states in `ELIM` using a given
    method.
    return rsys


 defbalred(sys, orders, method='truncate', alpha=None):
 defbalanced_reduction(sys, orders, method='truncate', alpha=None):
    """Balanced reduced order model of sys of a given order.
    States are eliminated based on Hankel singular value.
    If sys has unstable modes, they are removed, the
        return rsys


 defminreal(sys, tol=None, verbose=True):
 defminimal_realization(sys, tol=None, verbose=True):
    '''
    Eliminates uncontrollable or unobservable states in state-space
    models or cancelling pole-zero pairs in transfer functions. The
    return sysr


 defera(YY, m, n, nin, nout, r):
 defeigensys_realization(YY, m, n, nin, nout, r):
    """Calculate an ERA model of order `r` based on the impulse-response data
    `YY`.


    # Return the first m Markov parameters
    return H if transpose else np.transpose(H)

 # Function aliases
 hsvd = hankel_singular_values
 balred = balanced_reduction
 modred = model_reduction
 minreal = minimal_realization
 era = eigensys_realization
diff --git a/doc/control.rst b/doc/control.rst
 .. autosummary::
   :toctree: generated/

 minreal
 balred
 hsvd
 modred
 era
 minimal_realization
 balanced_reduction
 hankel_singular_values
 model_reduction
 eigensys_realization
    markov

 Nonlinear system support
Original file line number	Diff line number	Diff line change
Expand Up		@@ -49,15 +49,17 @@
		from .statesp import StateSpace
		from .statefbk import gram

		__all__ = ['hsvd', 'balred', 'modred', 'era', 'markov', 'minreal']
		__all__ = ['hankel_singular_values', 'balanced_reduction', 'model_reduction',
		'minimal_realization', 'eigensys_realization', 'markov', 'hsvd',
		'balred', 'modred', 'minreal', 'era']


		# Hankel Singular Value Decomposition
		#
		# The following returns the Hankel singular values, which are singular values
		# of the matrix formed by multiplying the controllability and observability
		# Gramians
		defhsvd(sys):
		defhankel_singular_values(sys):
		"""Calculate the Hankel singular values.

		Parameters
Expand DownExpand Up		@@ -106,7 +108,7 @@ def hsvd(sys):
		return hsv[::-1]


		defmodred(sys, ELIM, method='matchdc'):
		defmodel_reduction(sys, ELIM, method='matchdc'):
		"""
		Model reduction of `sys` by eliminating the states in `ELIM` using a given
		method.
Expand DownExpand Up		@@ -216,7 +218,7 @@ def modred(sys, ELIM, method='matchdc'):
		return rsys


		defbalred(sys, orders, method='truncate', alpha=None):
		defbalanced_reduction(sys, orders, method='truncate', alpha=None):
		"""Balanced reduced order model of sys of a given order.
		States are eliminated based on Hankel singular value.
		If sys has unstable modes, they are removed, the
Expand DownExpand Up		@@ -340,7 +342,7 @@ def balred(sys, orders, method='truncate', alpha=None):
		return rsys


		defminreal(sys, tol=None, verbose=True):
		defminimal_realization(sys, tol=None, verbose=True):
		'''
		Eliminates uncontrollable or unobservable states in state-space
		models or cancelling pole-zero pairs in transfer functions. The
Expand DownExpand Up		@@ -368,7 +370,7 @@ def minreal(sys, tol=None, verbose=True):
		return sysr


		defera(YY, m, n, nin, nout, r):
		defeigensys_realization(YY, m, n, nin, nout, r):
		"""Calculate an ERA model of order `r` based on the impulse-response data
		`YY`.

Expand DownExpand Up		@@ -556,3 +558,10 @@ def markov(Y, U, m=None, transpose=False):

		# Return the first m Markov parameters
		return H if transpose else np.transpose(H)

		# Function aliases
		hsvd = hankel_singular_values
		balred = balanced_reduction
		modred = model_reduction
		minreal = minimal_realization
		era = eigensys_realization
Original file line number	Diff line number	Diff line change
Expand Up		@@ -133,11 +133,11 @@ Model simplification tools
		.. autosummary::
		:toctree: generated/

		minreal
		balred
		hsvd
		modred
		era
		minimal_realization
		balanced_reduction
		hankel_singular_values
		model_reduction
		eigensys_realization
		markov

		Nonlinear system support
Expand Down