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Refactored matrix inversions (see #85 and #91) #101

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Merged
slivingston merged 14 commits into from
Dec 31, 2016
Merged

Refactored matrix inversions (see #85 and #91) #101

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4d70d00
Replaced a loop with a map
mp4096 Jun 13, 2016
8285cbf
Removed redundant numpy.linalg.solve calls
mp4096 Jun 13, 2016
29e7f95
Numerical improvements in statefbk.py
mp4096 Jun 13, 2016
443a7e3
Replaced `inv` with `solve` in mateqns
mp4096 Jul 3, 2016
af36e84
Added a test case for the reachable canon. form
mp4096 Jul 3, 2016
b9e4455
Replaced `inv` with `solve` in canonical forms class
mp4096 Jul 3, 2016
1fad4c6
Added a check for unreachable systems to canonical
mp4096 Jul 3, 2016
863538f
Replaced `inv` with `solve` and LSQ in modelsimp
mp4096 Jul 3, 2016
d5cb4f1
Fixed "compatability" typo
mp4096 Jul 3, 2016
6a2d96f
Removed rank check with a det in a statefbk test
mp4096 Jul 12, 2016
7b8c9f0
Added singularity check to the residualization method
mp4096 Jul 13, 2016
860cbe8
Added a unit test for residualization of unstable systems
mp4096 Jul 13, 2016
9a33509
Refactored code to be more pythonic
mp4096 Jul 13, 2016
71fef4b
Check eigenvalues' real part using NumPy
mp4096 Aug 25, 2016
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Numerical improvements in statefbk.py 
* `numpy.linalg.solve` instead of matrix inversion.
* Better controllability matrix rank check.
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@mp4096
mp4096 committed Nov 20, 2016
commit 29e7f950d9979ae87def54f4feb0920584ffa952
7 changes: 4 additions & 3 deletions control/statefbk.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,7 +127,7 @@ def acker(A, B, poles):

# Make sure the system is controllable
ct = ctrb(A, B)
if sp.linalg.det(ct) == 0:
if np.linalg.matrix_rank(ct) != a.shape[0]:
raise ValueError("System not reachable; pole placement invalid")

# Compute the desired characteristic polynomial
Expand All @@ -138,7 +138,7 @@ def acker(A, B, poles):
pmat = p[n-1]*a**0
for i in np.arange(1,n):
pmat = pmat + p[n-i-1]*a**i
K = sp.linalg.inv(ct) * pmat
K = np.linalg.solve(ct, pmat)

K = K[-1][:] # Extract the last row
return K
Expand Down Expand Up @@ -242,7 +242,8 @@ def lqr(*args, **keywords):
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));
# We assume that R is positive definite and, hence, invertible
K = np.linalg.solve(R, np.dot(B.T, X) + N.T);
S = X;
E = w[0:nstates];

Expand Down