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Validate discrete gain crossovers on the unit circle #1273

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Validate discrete gain crossovers on the unit circle #1273

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37 changes: 34 additions & 3 deletions control/margins.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,9 +134,40 @@ def _poly_z_mag1_crossing(num, den, num_inv_zp, den_inv_zq, p_q, dt, epsw):
z = np.roots(np.polysub(p1, p2))
eps = np.finfo(float).eps**(1 / len(p2))
z, w = _z_filter(z, dt, eps)
z = z[w > epsw]
w = w[w > epsw]
return z, w
above_cutoff = w > epsw
z = z[above_cutoff]
theta = w[above_cutoff] * dt

# The radial tolerance grows with degree. Verify the gain at the
# candidate frequency on the unit circle before reporting a crossover.
def magnitudes(angle):
point = np.exp(1j * angle)
return np.abs(np.polyval(num, point)), np.abs(np.polyval(den, point))

def magnitude_difference(angle):
num_mag, den_mag = magnitudes(angle)
return num_mag - den_mag

tol = np.sqrt(np.finfo(float).eps)
num_mag, den_mag = magnitudes(theta)

# Refine inaccurate near-circle roots locally using the frequency
# response. The search radius is in radians, independent of degree.
radius = np.sqrt(tol)
near_circle = np.abs(np.abs(z) - 1) < radius
inaccurate = ~np.isclose(num_mag, den_mag, atol=0, rtol=tol)
for i in np.flatnonzero(near_circle & inaccurate):
lo = max(0., theta[i] - radius)
hi = min(np.pi, theta[i] + radius)
ends = magnitude_difference(np.array([lo, hi]))
if np.all(np.isfinite(ends)) and np.sign(ends[0]) != np.sign(ends[1]):
theta[i] = sp.optimize.brentq(magnitude_difference, lo, hi)
num_mag[i], den_mag[i] = magnitudes(theta[i])

w = theta / dt
valid = (den_mag > 0) & np.isfinite(den_mag) & (w > epsw) & (theta < np.pi)
valid &= np.isclose(num_mag, den_mag, atol=0, rtol=tol)
return np.exp(1j * theta[valid]), w[valid]


def _poly_z_wstab(num, den, num_inv_zp, den_inv_zq, p_q, dt, epsw):
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38 changes: 38 additions & 0 deletions control/tests/margin_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -373,6 +373,44 @@ def test_stability_margins_discrete(cnum, cden, dt,
out = stability_margins(tf)
assert_allclose(out, ref, rtol=rtol)


@pytest.mark.parametrize("method", ["poly", "best"])
def test_discrete_gain_crossovers_on_unit_circle(method):
n = 17
num = np.full(n, 1 / n)
loop = tf(num, np.r_[1, -num], dt=1)

_, pm, _, _, wgc, _ = stability_margins(loop, method=method, returnall=True)

assert_allclose(wgc, [0.1064535], rtol=1e-6)
assert_allclose(pm, [70.2119], rtol=1e-6)
assert_allclose(np.abs(loop(np.exp(1j * wgc))), 1)


@pytest.mark.parametrize("method", ["poly", "best"])
def test_discrete_gain_below_unity(method):
# |z**8 - 0.5| >= 0.5 on the unit circle, so |L(z)| <= 0.5.
loop = tf([0.25], np.r_[1, np.zeros(7), -0.5], dt=1)

_, pm, _, _, wgc, _ = stability_margins(loop, method=method, returnall=True)

assert len(wgc) == len(pm) == 0


def test_discrete_gain_crossover_refinement():
# For L(z) = k * ((z + 1) / (z - a))**n, solve |L(z)| = 1
# directly in cos(theta) to check the numerically sensitive root.
n, a, k, dt = 10, 0.2, 1000, 0.2
loop = tf(k * np.poly([-1.] * n), np.poly([a] * n), dt=dt)
power = k**(2 / n)
theta = np.arccos((1 + a * a - 2 * power) / (2 * (power + a)))

_, _, _, _, wgc, _ = stability_margins(loop, method="poly", returnall=True)

assert_allclose(wgc * dt, [theta], atol=1e-8, rtol=0)
assert_allclose(np.abs(loop(np.exp(1j * wgc * dt))), 1, rtol=1e-8)


def test_siso_disk_margin():
# Frequencies of interest
omega = np.logspace(-1, 2, 1001)
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