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[python-control-discuss] SF.net SVN: python-control:[127] branches/control-0.4a/src
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From: <lpa...@us...> - 2011-02-10 18:37:19
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Revision: 127
http://python-control.svn.sourceforge.net/python-control/?rev=127&view=rev
Author: lpadilla
Date: 2011-02-10 18:37:09 +0000 (Thu, 10 Feb 2011)
Log Message:
-----------
A few changes to unittests and usage of slycot routines.
Modified Paths:
--------------
branches/control-0.4a/src/TestSlycot.py
branches/control-0.4a/src/modelsimp.py
branches/control-0.4a/src/statefbk.py
branches/control-0.4a/src/statesp.py
branches/control-0.4a/src/xferfcn.py
Modified: branches/control-0.4a/src/TestSlycot.py
===================================================================
--- branches/control-0.4a/src/TestSlycot.py 2011-02-10 17:32:53 UTC (rev 126)
+++ branches/control-0.4a/src/TestSlycot.py 2011-02-10 18:37:09 UTC (rev 127)
@@ -8,19 +8,30 @@
class TestSlycot(unittest.TestCase):
+ """TestSlycot compares transfer function and state space conversions for
+ various numbers of inputs,outputs and states.
+ 1. Usually passes for SISO systems of any state dim, occasonally, there will be a dimension mismatch if the original randomly generated ss system is not minimal because td04ad returns a minimal system.
+
+ 2. For small systems with many inputs, n<<m, the tests fail because td04ad returns a minimal ss system which has fewer states than the original system. It is typical for systems with many more inputs than states to have extraneous states.
+
+ 3. For systems with larger dimensions, n~>5 and with 2 or more outputs the conversion to statespace (td04ad) intermittently results in an equivalent realization of higher order than the original tf order. We think this has to do with minimum realization tolerances in the Fortran. The algorithm doesn't recognize that two denominators are identical and so it creates a system with nearly duplicate eigenvalues and double the state dimension. This should not be a problem in the python-control usage because the common_den() method finds repeated roots within a tolerance that we specify.
+
+ Matlab: Matlab seems to force its statespace system output to have order less than or equal to the order of denominators provided, avoiding the problem of very large state dimension we describe in 3. It does however, still have similar problems with pole/zero cancellation such as we encounter in 2, where a statespace system may have fewer states than the original order of transfer function.
+ """
def setUp(self):
"""Define some test parameters."""
- self.numTests = 1
- self.maxStates = 10 #seems to fail rarely with 4, sometimes with 5, frequently with 6. Could it be a problem with the subsystems?
- self.maxIO = 10
-
+ self.numTests = 5
+ self.maxStates = 10
+ self.maxI = 1
+ self.maxO = 1
+
def testTF(self):
""" Directly tests the functions tb04ad and td04ad through direct comparison of transfer function coefficients.
Similar to TestConvert, but tests at a lower level.
"""
for states in range(1, self.maxStates):
- for inputs in range(1, self.maxIO+1):
- for outputs in range(1, self.maxIO+1):
+ for inputs in range(1, self.maxI+1):
+ for outputs in range(1, self.maxO+1):
for testNum in range(self.numTests):
ssOriginal = matlab.rss(states, inputs, outputs)
@@ -34,23 +45,25 @@
tfOriginal_Actrb, tfOriginal_Bctrb, tfOriginal_Cctrb, tfOrigingal_nctrb, tfOriginal_index,\
tfOriginal_dcoeff, tfOriginal_ucoeff = tb04ad('R',states,inputs,outputs,\
- ssOriginal.A,ssOriginal.B,ssOriginal.C,ssOriginal.D,tol1=1e-10)
+ ssOriginal.A,ssOriginal.B,ssOriginal.C,ssOriginal.D,tol1=0.0)
ssTransformed_nr, ssTransformed_A, ssTransformed_B, ssTransformed_C, ssTransformed_D\
- = td04ad('R',inputs,outputs,tfOriginal_index,tfOriginal_dcoeff,tfOriginal_ucoeff,tol=1e-8)
+ = td04ad('R',inputs,outputs,tfOriginal_index,tfOriginal_dcoeff,tfOriginal_ucoeff,tol=0.0)
tfTransformed_Actrb, tfTransformed_Bctrb, tfTransformed_Cctrb, tfTransformed_nctrb,\
tfTransformed_index, tfTransformed_dcoeff, tfTransformed_ucoeff = tb04ad('R',\
ssTransformed_nr,inputs,outputs,ssTransformed_A, ssTransformed_B, ssTransformed_C,\
- ssTransformed_D,tol1=1e-10)
- print 'size(Trans_A)=',ssTransformed_A.shape
- print 'Trans_nr=',ssTransformed_nr
- print 'tfOrig_index=',tfOriginal_index
- print 'tfOrig_ucoeff=',tfOriginal_ucoeff
- print 'tfOrig_dcoeff=',tfOriginal_dcoeff
- print 'tfTrans_index=',tfTransformed_index
- print 'tfTrans_ucoeff=',tfTransformed_ucoeff
- print 'tfTrans_dcoeff=',tfTransformed_dcoeff
+ ssTransformed_D,tol1=0.0)
+ #print 'size(Trans_A)=',ssTransformed_A.shape
+ print '===== Transformed SS =========='
+ print matlab.ss(ssTransformed_A, ssTransformed_B, ssTransformed_C, ssTransformed_D)
+ #print 'Trans_nr=',ssTransformed_nr
+ #print 'tfOrig_index=',tfOriginal_index
+ #print 'tfOrig_ucoeff=',tfOriginal_ucoeff
+ #print 'tfOrig_dcoeff=',tfOriginal_dcoeff
+ #print 'tfTrans_index=',tfTransformed_index
+ #print 'tfTrans_ucoeff=',tfTransformed_ucoeff
+ #print 'tfTrans_dcoeff=',tfTransformed_dcoeff
#Compare the TF directly, must match
#numerators
np.testing.assert_array_almost_equal(tfOriginal_ucoeff,tfTransformed_ucoeff,decimal=3)
@@ -61,24 +74,24 @@
"""Compare the bode reponses of the SS systems and TF systems to the original SS
They generally are different realizations but have same freq resp.
Currently this test may only be applied to SISO systems.
-
+ """
for states in range(1,self.maxStates):
for testNum in range(self.numTests):
- for inputs in range(1,self.maxIO+1):
- for outputs in range(1,self.maxIO+1):
+ for inputs in range(1,1):
+ for outputs in range(1,1):
ssOriginal = matlab.rss(states, inputs, outputs)
tfOriginal_Actrb, tfOriginal_Bctrb, tfOriginal_Cctrb, tfOrigingal_nctrb, tfOriginal_index,\
tfOriginal_dcoeff, tfOriginal_ucoeff = tb04ad('R',states,inputs,outputs,\
- ssOriginal.A,ssOriginal.B,ssOriginal.C,ssOriginal.D,tol1=1e-10)
+ ssOriginal.A,ssOriginal.B,ssOriginal.C,ssOriginal.D,tol1=0.0)
ssTransformed_nr, ssTransformed_A, ssTransformed_B, ssTransformed_C, ssTransformed_D\
- = td04ad('R',inputs,outputs,tfOriginal_index,tfOriginal_dcoeff,tfOriginal_ucoeff,tol=1e-8)
+ = td04ad('R',inputs,outputs,tfOriginal_index,tfOriginal_dcoeff,tfOriginal_ucoeff,tol=0.0)
tfTransformed_Actrb, tfTransformed_Bctrb, tfTransformed_Cctrb, tfTransformed_nctrb,\
tfTransformed_index, tfTransformed_dcoeff, tfTransformed_ucoeff = tb04ad('R',\
ssTransformed_nr,inputs,outputs,ssTransformed_A, ssTransformed_B, ssTransformed_C,\
- ssTransformed_D,tol1=1e-10)
+ ssTransformed_D,tol1=0.0)
numTransformed = np.array(tfTransformed_ucoeff)
denTransformed = np.array(tfTransformed_dcoeff)
@@ -88,21 +101,21 @@
ssTransformed = matlab.ss(ssTransformed_A,ssTransformed_B,ssTransformed_C,ssTransformed_D)
for inputNum in range(inputs):
for outputNum in range(outputs):
- #[ssOriginalMag,ssOriginalPhase,freq] = matlab.bode(ssOriginal,Plot=False)
+ [ssOriginalMag,ssOriginalPhase,freq] = matlab.bode(ssOriginal,Plot=False)
[tfOriginalMag,tfOriginalPhase,freq] = matlab.bode(matlab.tf(numOriginal[outputNum][inputNum],denOriginal[outputNum]),Plot=False)
- #[ssTransformedMag,ssTransformedPhase,freq] = matlab.bode(ssTransformed,freq,Plot=False)
+ [ssTransformedMag,ssTransformedPhase,freq] = matlab.bode(ssTransformed,freq,Plot=False)
[tfTransformedMag,tfTransformedPhase,freq] = matlab.bode(matlab.tf(numTransformed[outputNum][inputNum],denTransformed[outputNum]),freq,Plot=False)
- print 'numOrig=',numOriginal[outputNum][inputNum]
- print 'denOrig=',denOriginal[outputNum]
- print 'numTrans=',numTransformed[outputNum][inputNum]
- print 'denTrans=',denTransformed[outputNum]
- #np.testing.assert_array_almost_equal(ssOriginalMag,tfOriginalMag,decimal=3)
- #np.testing.assert_array_almost_equal(ssOriginalPhase,tfOriginalPhase,decimal=3)
- #np.testing.assert_array_almost_equal(ssOriginalMag,ssTransformedMag,decimal=3)
- #np.testing.assert_array_almost_equal(ssOriginalPhase,ssTransformedPhase,decimal=3)
- #np.testing.assert_array_almost_equal(tfOriginalMag,tfTransformedMag,decimal=3)
+ #print 'numOrig=',numOriginal[outputNum][inputNum]
+ #print 'denOrig=',denOriginal[outputNum]
+ #print 'numTrans=',numTransformed[outputNum][inputNum]
+ #print 'denTrans=',denTransformed[outputNum]
+ np.testing.assert_array_almost_equal(ssOriginalMag,tfOriginalMag,decimal=3)
+ np.testing.assert_array_almost_equal(ssOriginalPhase,tfOriginalPhase,decimal=3)
+ np.testing.assert_array_almost_equal(ssOriginalMag,ssTransformedMag,decimal=3)
+ np.testing.assert_array_almost_equal(ssOriginalPhase,ssTransformedPhase,decimal=3)
+ np.testing.assert_array_almost_equal(tfOriginalMag,tfTransformedMag,decimal=3)
np.testing.assert_array_almost_equal(tfOriginalPhase,tfTransformedPhase,decimal=2)
- """
+
#These are here for once the above is made into a unittest.
def suite():
return unittest.TestLoader().loadTestsFromTestCase(TestSlycot)
Modified: branches/control-0.4a/src/modelsimp.py
===================================================================
--- branches/control-0.4a/src/modelsimp.py 2011-02-10 17:32:53 UTC (rev 126)
+++ branches/control-0.4a/src/modelsimp.py 2011-02-10 18:37:09 UTC (rev 127)
@@ -222,19 +222,10 @@
raise ControlSlycot("can't find slycot subroutine ab09ad")
job = 'B' # balanced (B) or not (N)
equil = 'N' # scale (S) or not (N)
- ordsel = 'F' # fixed truncation level (F) or find the truncation level given tol (A)
n = np.size(sys.A,0)
m = np.size(sys.B,1)
p = np.size(sys.C,0)
- nr = orders
- tol = 0.
- out = ab09ad(dico,job,equil,ordsel, n, m, p, nr, sys.A, sys.B, sys.C,tol)
- Ar = out[0][0:nr,0:nr]
- Br = out[1][0:nr,0:m]
- Cr = out[2][0:p,0:nr]
- hsv = out[3]
- iwarn = out[4]
- info = out[5]
+ Nr, Ar, Br, Cr, hsv = ab09ad(dico,job,equil,n,m,p,sys.A,sys.B,sys.C,nr=orders,tol=0.0)
rsys = StateSpace(Ar, Br, Cr, sys.D)
else:
Modified: branches/control-0.4a/src/statefbk.py
===================================================================
--- branches/control-0.4a/src/statefbk.py 2011-02-10 17:32:53 UTC (rev 126)
+++ branches/control-0.4a/src/statefbk.py 2011-02-10 18:37:09 UTC (rev 127)
@@ -177,7 +177,7 @@
sb02mt(nstates, ninputs, B, R, A, Q, N, jobl='N');
# Call the SLICOT function
- X,rcond,w,S,U = sb02md(nstates, A_b, G, Q_b, 'C')
+ X,rcond,w,S,U,A_inv = sb02md(nstates, A_b, G, Q_b, 'C')
# Now compute the return value
K = np.dot(np.linalg.inv(R), (np.dot(B.T, X) + N.T));
@@ -288,10 +288,10 @@
if e.real >= 0:
raise ValueError, "Oops, the system is unstable!"
if type=='c':
- trana = 'T'
+ tra = 'T'
C = -np.dot(sys.B,sys.B.transpose())
elif type=='o':
- trana = 'N'
+ tra = 'N'
C = -np.dot(sys.C.transpose(),sys.C)
else:
raise ValueError, "Oops, neither observable, nor controllable!"
@@ -305,7 +305,7 @@
n = sys.states
U = np.zeros((n,n))
A = sys.A
- out = sb03md(n, C, A, U, dico, 'X', 'N', trana)
- gram = out[0]
+ X,scale,sep,ferr,w = sb03md(n, C, A, U, dico, job='X', fact='N', trana=tra)
+ gram = X
return gram
Modified: branches/control-0.4a/src/statesp.py
===================================================================
--- branches/control-0.4a/src/statesp.py 2011-02-10 17:32:53 UTC (rev 126)
+++ branches/control-0.4a/src/statesp.py 2011-02-10 18:37:09 UTC (rev 127)
@@ -163,7 +163,6 @@
useless = []
# Search for useless states.
- tol = 1e-16
for i in range(self.states):
if (all(self.A[i, :] == zeros((1, self.states))) and
all(self.B[i, :] == zeros((1, self.inputs)))):
@@ -474,7 +473,7 @@
is still buggy! Advise converting state space sys back to tf to verify the transformation was correct."
#print num
#print shape(num)
- ssout = td04ad('R',sys.inputs, sys.outputs, index, den, num,tol=1e-8)
+ ssout = td04ad('R',sys.inputs, sys.outputs, index, den, num,tol=0.0)
states = ssout[0]
return StateSpace(ssout[1][:states, :states],
Modified: branches/control-0.4a/src/xferfcn.py
===================================================================
--- branches/control-0.4a/src/xferfcn.py 2011-02-10 17:32:53 UTC (rev 126)
+++ branches/control-0.4a/src/xferfcn.py 2011-02-10 18:37:09 UTC (rev 127)
@@ -212,7 +212,6 @@
"""
# Beware: this is a shallow copy. This should be okay.
- tol = 1e-16
data = [self.num, self.den]
for p in range(len(data)):
for i in range(self.outputs):
@@ -717,7 +716,7 @@
print "Warning: state space to transfer function conversion by tb04ad \
is still buggy!"
tfout = tb04ad('R',sys.states, sys.inputs, sys.outputs, sys.A, sys.B, sys.C,
- sys.D,tol1=1e-10)
+ sys.D,tol1=0.0)
# Preallocate outputs.
num = [[[] for j in range(sys.inputs)] for i in range(sys.outputs)]
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