Control allocation w/ smoothed saturation

ADVANTAGES1 · dim 18

SolvSRK 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 →

QP / pseudoinverse allocation with smooth saturation in y

Drone dynamics & autonomy

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 _pd_controller(y, sp=None, mass=None):
    """Cascaded PD: pos error → desired attitude → torques. Returns (T, tau_x, tau_y, tau_z).

    BA-2 (2026-04-29): added optional ``mass`` kwarg so the
    factory variants in ``make_rhs_A3`` / ``make_rhs_A7`` can override
    the module-global ``MASS`` for the hover-thrust feed-forward term.
    THRUST_MAX is held at the parent airframe's value (39.24 N) since it
    represents the physical thrust ceiling; airframe-mass perturbations
    in {0.8..1.2} kg stay well within this envelope.
    """
    if sp is None:
        sp = HOVER_SP
    eff_mass = MASS if mass is None else mass
    px, py, pz = y[0], y[1], y[2]
    vx, vy, vz = y[3], y[4], y[5]
    phi, theta, psi = y[6], y[7], y[8]
    p, q, r = y[9], y[10], y[11]

    ax_d = KP_POS * (sp[0] - px) + KD_POS * (sp[3] - vx)
    ay_d = KP_POS * (sp[1] - py) + KD_POS * (sp[4] - vy)
    az_d = KP_POS * (sp[2] - pz) + KD_POS * (sp[5] - vz)

    T_des = eff_mass * (G + az_d)
    phi_des = (1.0 / G) * (ax_d * np.sin(psi) - ay_d * np.cos(psi))
    theta_des = (1.0 / G) * (ax_d * np.cos(psi) + ay_d * np.sin(psi))

    tau_x = KP_ATT * np.arctan2(np.sin(phi_des - phi), np.cos(phi_des - phi)) - KD_ATT * p
    tau_y = KP_ATT * np.arctan2(np.sin(theta_des - theta), np.cos(theta_des - theta)) - KD_ATT * q
    tau_z = KP_YAW * np.arctan2(np.sin(-psi), np.cos(-psi)) - KD_YAW * r

    T_des = np.clip(T_des, 0.0, THRUST_MAX)
    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)
    return T_des, tau_x, tau_y, tau_z

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 _smooth_sat(x, lim):
    return lim * np.tanh(x / lim)

def rhs_B5(t, y):
    body = y[:12]
    alloc = y[12:18]

    T, tx, ty, tz = _pd_controller(body)
    cmd = np.array([T, tx, ty, tz, tx * 0.5, ty * 0.5])
    saturated = _smooth_sat(cmd, _ALLOC_SAT)
    d_alloc = (saturated - alloc) / _ALLOC_TAU

    T_eff, tx_eff, ty_eff, tz_eff = alloc[0], alloc[1], alloc[2], alloc[3]
    d_body = _quad12(body, T_eff, tx_eff, ty_eff, tz_eff)

    return np.concatenate([d_body, d_alloc])
Parameters
  • CD = 0.1
  • G = 9.81
  • HOVER_SP = [0, 0, 5, 0, 0, 0]
  • IXX = 0.0082
  • IYY = 0.0082
  • IZZ = 0.0148
  • KD_ATT = 2.5
  • KD_POS = 4
  • KD_YAW = 1.5
  • KP_ATT = 8
  • KP_POS = 6
  • KP_YAW = 4
  • MASS = 1
  • THRUST_MAX = 39.24
  • TORQUE_CLIP = 2
  • _ALLOC_SAT = [39.24, 2, 2, 0.5, 2, 2]
  • _ALLOC_TAU = 0.05
  • sp = None
  • mass = None
Initial condition
y(0) = [1.5, 0, 5, 0, 0, 0, …] [shape=(18,), min=0, max=9.81]
Horizon
t ∈ [0, 60]

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: low

Default noise: none

Recommendation snapshot

Clean best: SciPy Radau

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: Control allocation w/ smoothed saturation (control-allocation-w-smoothed-saturation)

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

#SolverSurvivalSCDnfevWallScore
1SciPy RadauSciPy
100%
13.110,407494 ms0.930
2SolvSRK
100%
12.214,208270 ms0.910
3TRBDF2external
100%
12.17,5226.08 s0.908
4FBDFexternal
100%
11.25,7705.87 s0.886
5Vern7external
100%
10.912,7525.05 s0.877
6Vern9external
100%
10.520,7385.61 s0.868
7SciPy LSODASciPy
100%
10.35,131157 ms0.864
8SciPy DOP853SciPy
100%
9.713,418441 ms0.849
9CVODE Adamsexternal
100%
8.75,273189 ms0.826
10SciPy RK23SciPy
100%
8.415,803565 ms0.819
11CVODE BDFexternal
100%
8.38,494297 ms0.816
12SciPy RK45SciPy
100%
8.012,464414 ms0.810
13Tsit5external
100%
7.813,1702.33 s0.803
14SciPy BDFSciPy
100%
7.712,170788 ms0.803

At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%, SCD 12.2.

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 →

SolvScout · free

Profile your problem for free

This page shows one published benchmark cell. SolvScout fingerprints your ODE, compares it to the full corpus, and recommends a solver with the same survival / precision / speed ranking you see here — including when a SciPy arm wins.

SolvSRK · 30-day trial

Run the winner on your machine

SolvSRK is the stiffness-adaptive integrator behind the SolvSRK column in these tables. Create an account, activate a machine, and take a 30-day trial — same binary you'd ship after purchase.

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_control_allocation_w_smoothed_saturation_2026,
  title        = {Resonix Evidence Portal: Control allocation w/ smoothed saturation},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/control-allocation-w-smoothed-saturation}},
  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