Position / velocity tracking

ADVANTAGES1 · dim 24

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 →

Double-integrator outer loop + inner attitude; wind via A.6

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 _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_B2(t, y):
    body = y[:12]
    pos_int = y[12:18]
    att_int = y[18:24]

    sp = HOVER_SP.copy()
    sp[0] = 5.0 * min(t / 30.0, 1.0)

    pos_err = sp[:3] - body[:3]
    vel_err = sp[3:] - body[3:6]

    ax_d = KP_POS * pos_err[0] + KD_POS * vel_err[0] + _KI_POS * pos_int[0]
    ay_d = KP_POS * pos_err[1] + KD_POS * vel_err[1] + _KI_POS * pos_int[1]
    az_d = KP_POS * pos_err[2] + KD_POS * vel_err[2] + _KI_POS * pos_int[2]

    psi = body[8]
    T_des = 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))

    phi, theta = body[6], body[7]
    p, q, r_rate = body[9], body[10], body[11]
    e_att = np.array([phi_des - phi, theta_des - theta, -psi])
    tau = KP_ATT * e_att - KD_ATT * np.array([p, q, r_rate]) + _KI_ATT * att_int[:3]
    tau = np.clip(tau, -TORQUE_CLIP, TORQUE_CLIP)
    T_des = np.clip(T_des, 0.0, THRUST_MAX)

    d_body = _quad12(body, T_des, tau[0], tau[1], tau[2])
    d_pos_int = np.clip(np.concatenate([pos_err, vel_err]), -_KI_POS_CLIP, _KI_POS_CLIP)
    d_att_int = np.clip(np.concatenate([e_att, -np.array([p, q, r_rate])]), -_INT_CLIP, _INT_CLIP)

    return np.concatenate([d_body, d_pos_int, d_att_int])
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
  • KP_ATT = 8
  • KP_POS = 6
  • MASS = 1
  • THRUST_MAX = 39.24
  • TORQUE_CLIP = 2
  • _INT_CLIP = 1
  • _KI_ATT = 1.5
  • _KI_POS = 0.5
  • _KI_POS_CLIP = 2
  • mass = None
Initial condition
y(0) = [0, 0, 5, 0, 0, 0, …] [shape=(24,), min=0, max=5]
Horizon
t ∈ [0, 120]

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: SolvSRK

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: Position / velocity tracking (position-velocity-tracking)

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
1SolvSRK
100%
11.55,530109 ms0.892
2SciPy RadauSciPy
100%
10.84,220215 ms0.876
3Vern9external
100%
10.6129,81013.25 s0.872
4Vern7external
100%
9.878,3229.84 s0.852
5SciPy LSODASciPy
100%
9.14,155138 ms0.836
6CVODE BDFexternal
100%
8.91,57069 ms0.830
7SciPy RK23SciPy
100%
8.644,1621.69 s0.824
8SciPy DOP853SciPy
100%
8.668,3182.44 s0.824
9Tsit5external
100%
8.478,97210.53 s0.818
10FBDFexternal
100%
8.21,5245.55 s0.814
11SciPy BDFSciPy
100%
8.23,731205 ms0.814
12SciPy RK45SciPy
100%
8.076,6822.74 s0.809
13CVODE Adamsexternal
100%
7.84,891185 ms0.805
14TRBDF2external
100%
6.83,6325.89 s0.782

At Clean, best balanced arm is SolvSRK.

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_position_velocity_tracking_2026,
  title        = {Resonix Evidence Portal: Position / velocity tracking},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/position-velocity-tracking}},
  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