Padé-delayed attitude feedback
ADVANTAGES1 · dim 22SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →
Latency modeled as Padé approximant states stacked on attitude loop
Problem definition
Canonical benchmark implementation
Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.
Show canonical RHS excerpt
def _pade2_coeffs(tau):
"""2nd-order Padé: (1 - s*tau/2 + s²*tau²/12) / (1 + s*tau/2 + s²*tau²/12)."""
a1 = tau / 2.0
a2 = tau**2 / 12.0
return a1, a2
def _body_forces(T, phi, theta, psi):
"""Thrust-to-inertial force components."""
cp, sp = np.cos(phi), np.sin(phi)
ct, st = np.cos(theta), np.sin(theta)
cy, sy = np.cos(psi), np.sin(psi)
Fx = T * (cy * st * cp + sy * sp)
Fy = T * (sy * st * cp - cy * sp)
Fz = T * ct * cp
return Fx, Fy, Fz
def _euler_kinematics(phi, theta, p, q, r):
"""Euler-angle rates from body rates. Returns (dphi, dtheta, dpsi)."""
cp, sp = np.cos(phi), np.sin(phi)
theta_c = np.clip(theta, -1.39, 1.39)
tan_th = np.tan(theta_c)
cos_th = np.cos(theta_c)
sec_th = 1.0 / cos_th if abs(cos_th) > 1e-12 else 1e12 * np.sign(cos_th)
dphi = p + q * sp * tan_th + r * cp * tan_th
dtheta = q * cp - r * sp
dpsi = (q * sp + r * cp) * sec_th
return dphi, dtheta, dpsi
def _quad12(y, T, tau_x, tau_y, tau_z, mass=None):
"""Core 12-state quadrotor dynamics. Returns d[0:12].
BA-2 (2026-04-29): added optional ``mass`` kwarg so the
factory variants can override the module-global ``MASS`` for the
translational acceleration / drag terms.
"""
eff_mass = MASS if mass is None else mass
phi, theta, psi = y[6], y[7], y[8]
p, q, r = y[9], y[10], y[11]
Fx, Fy, Fz = _body_forces(T, phi, theta, psi)
d = np.empty(12)
d[0] = y[3]; d[1] = y[4]; d[2] = y[5]
d[3] = (Fx - CD * y[3]) / eff_mass
d[4] = (Fy - CD * y[4]) / eff_mass
d[5] = (Fz - CD * y[5]) / eff_mass - G
d[6], d[7], d[8] = _euler_kinematics(phi, theta, p, q, r)
d[9] = (tau_x + (IYY - IZZ) * q * r) / IXX
d[10] = (tau_y + (IZZ - IXX) * p * r) / IYY
d[11] = (tau_z + (IXX - IYY) * p * q) / IZZ
return d
def rhs_B6(t, y):
body = y[:12]
delay_states = y[12:22].reshape(5, 2)
delayed_feedback = np.zeros(5)
d_delay = np.zeros((5, 2))
channels = [body[6], body[7], body[8], body[9], body[10]] # phi,theta,psi,p,q
for i in range(5):
a1, a2 = _pade2_coeffs(_PADE_DELAYS[i])
x1, x2 = delay_states[i]
u = channels[i]
if a2 > 1e-15:
d_delay[i, 0] = x2
d_delay[i, 1] = (u - x1 - a1 * x2) / a2
else:
d_delay[i, 0] = (u - x1) / max(a1, 1e-12)
d_delay[i, 1] = 0.0
delayed_feedback[i] = x1
phi_d, theta_d, psi_d = delayed_feedback[0], delayed_feedback[1], delayed_feedback[2]
p_d, q_d = delayed_feedback[3], delayed_feedback[4]
r_rate = body[11]
T_cmd = MASS * G
tau_x = KP_ATT * (-phi_d) - KD_ATT * p_d
tau_y = KP_ATT * (-theta_d) - KD_ATT * q_d
tau_z = KP_YAW * (-psi_d) - KD_YAW * r_rate
tau_x = np.clip(tau_x, -TORQUE_CLIP, TORQUE_CLIP)
tau_y = np.clip(tau_y, -TORQUE_CLIP, TORQUE_CLIP)
tau_z = np.clip(tau_z, -TORQUE_CLIP * 0.25, TORQUE_CLIP * 0.25)
d_body = _quad12(body, T_cmd, tau_x, tau_y, tau_z)
return np.concatenate([d_body, d_delay.ravel()])- Parameters
- CD = 0.1
- G = 9.81
- IXX = 0.0082
- IYY = 0.0082
- IZZ = 0.0148
- KD_ATT = 2.5
- KD_YAW = 1.5
- KP_ATT = 8
- KP_YAW = 4
- MASS = 1
- TORQUE_CLIP = 2
- _PADE_DELAYS = [0.02, 0.02, 0.05, 0.005, 0.005]
- mass = None
- Initial condition
- y(0) = [0, 0, 5, 0, 0, 0, …] [shape=(22,), min=0, max=5]
- Horizon
- t ∈ [0, 30]
Canonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.
Fingerprint
Spread: high
Default noise: low
Recommendation snapshot
Clean best: Vern9
Noisy best: SolvSRK
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
Versions & freeze
Methodology →- Freeze
- 2026-08-13
- libsolvsrk
- 2.3.0
- SciPy
- 1.14
- SUNDIALS
- CVODE (bundled backend)
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Padé-delayed attitude feedback (pade-delayed-attitude-feedback)
Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.
Self-reported by Resonix Labs · not independently verified
Results matrix
Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.
Objective
Best overall trade-off of survival, precision, and speed.
Noise level
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | Vern9external | 100% | 11.4 | 90,786 | 9.79 s | 0.891 |
| 2 | Vern7external | 100% | 10.8 | 54,942 | 7.85 s | 0.876 |
| 3 | SciPy RadauSciPy | 100% | 10.6 | 5,219 | 259 ms | 0.871 |
| 4 | SolvSRK | 100% | 10.4 | 4,895 | 93 ms | 0.866 |
| 5 | SciPy DOP853SciPy | 100% | 9.9 | 47,894 | 1.52 s | 0.855 |
| 6 | Tsit5external | 100% | 9.0 | 55,572 | 8.25 s | 0.833 |
| 7 | SciPy RK45SciPy | 100% | 8.8 | 53,576 | 1.75 s | 0.829 |
| 8 | SciPy RK23SciPy | 100% | 8.6 | 35,498 | 1.25 s | 0.824 |
| 9 | FBDFexternal | 100% | 7.8 | 1,899 | 5.41 s | 0.805 |
| 10 | SciPy LSODASciPy | 100% | 7.5 | 2,272 | 68 ms | 0.797 |
| 11 | CVODE BDFexternal | 100% | 7.2 | 1,042 | 47 ms | 0.791 |
| 12 | SciPy BDFSciPy | 100% | 7.1 | 1,767 | 111 ms | 0.787 |
| 13 | CVODE Adamsexternal | 100% | 6.7 | 3,490 | 125 ms | 0.778 |
| 14 | TRBDF2external | 100% | 5.0 | 3,433 | 5.57 s | 0.737 |
At Clean, best balanced arm is Vern9 · SolvSRK survival 100%, SCD 10.4.
Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →
Cite this page
Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is - a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.
@misc{resonix_evidence_pade_delayed_attitude_feedback_2026,
title = {Resonix Evidence Portal: Padé-delayed attitude feedback},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/pade-delayed-attitude-feedback}},
note = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}Related
TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels
Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology
Self-reported by Resonix Labs · not independently verified · Verification status