Rotor / motor / ESC dynamics

ADVANTAGES2 · dim 16

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 →

Motor, ESC, back-EMF states stacked on body; multiscale S1–S3

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 rhs_A2(t, y):
    body, omegas = y[:12], y[12:16]
    T_total = _CT * np.sum(omegas**2)
    react_torque = _CQ * (omegas[0]**2 - omegas[1]**2 + omegas[2]**2 - omegas[3]**2)

    T_cmd, tx_cmd, ty_cmd, tz_cmd = _pd_controller(body)
    d_body = _quad12(body, T_total, tx_cmd, ty_cmd, tz_cmd)
    d_body[11] += react_torque / IZZ  # gyroscopic yaw from rotor imbalance

    omega_des = np.sqrt(np.clip(T_cmd / (4.0 * _CT), 0, 1e6))
    V_cmd = (_KB * omega_des + 1.0) * np.ones(4)
    d_omega = (_KT * V_cmd - _KB * omegas - _CQ * omegas * np.abs(omegas)) / _TAU_MOTOR

    return np.concatenate([d_body, d_omega])
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
  • _CQ = 1.5e-07
  • _CT = 1e-05
  • _KB = 0.01
  • _KT = 0.012
  • _TAU_MOTOR = 0.02
  • sp = None
  • mass = None
Initial condition
y(0) = [0, 0, 5, 0, 0, 0, …] [shape=(16,), min=0, max=500]
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: 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: Rotor / motor / ESC dynamics (rotor-motor-esc-dynamics)

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%
9.91,90066 ms0.855
2SciPy DOP853SciPy
100%
9.437410 ms0.842
3SciPy LSODASciPy
100%
9.03158 ms0.833
4Tsit5external
100%
8.2468811 ms0.814
5CVODE Adamsexternal
100%
8.11,09233 ms0.812
6SciPy BDFSciPy
100%
7.955124 ms0.808
7SciPy RK23SciPy
100%
7.82,43865 ms0.806
8SciPy RK45SciPy
100%
7.752414 ms0.803
9SolvSRK
100%
7.654715 ms0.800
10CVODE BDFexternal
100%
7.641215 ms0.799

At Clean, best balanced arm is SciPy Radau · SolvSRK survival 100%, SCD 7.6.

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 →

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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_rotor_motor_esc_dynamics_2026,
  title        = {Resonix Evidence Portal: Rotor / motor / ESC dynamics},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/rotor-motor-esc-dynamics}},
  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