Fault-Geometry-Aware Control Allocation for Emergency Quadrotor Landing under Single-Motor Loss of Effectiveness
Overview
This research compares three control allocators—bounded weighted least squares (PINV), a finite-candidate residual allocator (QP-lite), and a Cross-Entropy Method-tuned variant—for emergency quadrotor landing following single-motor loss of effectiveness. Using Crazyflie software-in-the-loop simulations, the study determines that the Cross-Entropy Method approach is selected for motors M1, M3, and M4, while PINV performs better for motor M2. The policy achieves 117/120 safe first-contact touchdowns on fresh seeds and 115/120 in a randomized-order integrated-supervisor evaluation. Sensitivity analysis reveals that one-factor-at-a-time plant perturbations can shift the nominal effectiveness threshold by up to 0.05495 for thrust coefficient and 0.04974 for mass, with the most frequently violated first-contact criterion changing from vertical speed to angular rate under certain conditions.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“arXiv:2611.08259v1 Announce Type: new Abstract: Partial single-motor loss of effectiveness (LoE) changes the remaining control-authority geometry of a quadrotor and can change which control allocator performs best during emergency landing[cite: 11]. We compare three allocators in Crazyflie software-in-the-loop simulation: bounded weighted least squares (reported as PINV), a finite-candidate residual allocator (QP-lit”
Exact source · revision 1Source owner not reported
Reported quantity · safe first-contact touchdowns: 117 other · reported trials · Differing source assertions
“g map, the policy achieves 117/120 safe first-contact touchdowns on fresh seeds and 115/120 in a separate randomized-order integrated-supervisor evaluation[cite: 11]. For the critical M2/PINV branch, Firth regression gives a nominal $\eta_{50}\approx0.4956$ with a within-model 95% bootstrap interval of approximately [0.4955, 0.4956][cite: 11]. One-factor-at-a-time $\pm10\%$ plant perturbations shift $\eta_{50}$ by up”
Exact source · revision 1Source owner not reported
Reported quantity · safe first-contact touchdowns: 115 other · reported trials · Differing source assertions
“g map, the policy achieves 117/120 safe first-contact touchdowns on fresh seeds and 115/120 in a separate randomized-order integrated-supervisor evaluation[cite: 11]. For the critical M2/PINV branch, Firth regression gives a nominal $\eta_{50}\approx0.4956$ with a within-model 95% bootstrap interval of approximately [0.4955, 0.4956][cite: 11]. One-factor-at-a-time $\pm10\%$ plant perturbations shift $\eta_{50}$ by up”
Exact source · revision 1Source owner not reported
Reported quantity · total fresh seeds trials: 120 other · reported trials
“g map, the policy achieves 117/120 safe first-contact touchdowns on fresh seeds and 115/120 in a separate randomized-order integrated-supervisor evaluation[cite: 11]. For the critical M2/PINV branch, Firth regression gives a nominal $\eta_{50}\approx0.4956$ with a within-model 95% bootstrap interval of approximately [0.4955, 0.4956][cite: 11]. One-factor-at-a-time $\pm10\%$ plant perturbations shift $\eta_{50}$ by up”
Exact source · revision 1Source owner not reported
Reported quantity · integrated-supervisor evaluation trials: 120 other · reported trials
“g map, the policy achieves 117/120 safe first-contact touchdowns on fresh seeds and 115/120 in a separate randomized-order integrated-supervisor evaluation[cite: 11]. For the critical M2/PINV branch, Firth regression gives a nominal $\eta_{50}\approx0.4956$ with a within-model 95% bootstrap interval of approximately [0.4955, 0.4956][cite: 11]. One-factor-at-a-time $\pm10\%$ plant perturbations shift $\eta_{50}$ by up”
Exact source · revision 1Source owner not reported
Reported quantity · tested plant states for PINV vs CEM comparison: 11 other · Basis not reported
“to 0.05495 for thrust coefficient and 0.04974 for mass; the most frequent violated first-contact criterion also changes from vertical speed in many conditions to angular rate for mass -10% and thrust-coefficient +10%[cite: 11]. At fixed $\eta=0.496$, PINV has a higher M2 safe count than frozen CEM in 9/11 tested plant states and ties in 2/11; the two ties are both 0/30[cite: 11]. The experiments therefore distinguis”
Exact source · revision 1Source owner not reported
Reported quantity · ties between PINV and CEM: 2 other · Basis not reported
“to 0.05495 for thrust coefficient and 0.04974 for mass; the most frequent violated first-contact criterion also changes from vertical speed in many conditions to angular rate for mass -10% and thrust-coefficient +10%[cite: 11]. At fixed $\eta=0.496$, PINV has a higher M2 safe count than frozen CEM in 9/11 tested plant states and ties in 2/11; the two ties are both 0/30[cite: 11]. The experiments therefore distinguis”
Exact source · revision 1Source owner not reported
Reported quantity · failed M2 safe count in ties: 0 other · reported trials
“to 0.05495 for thrust coefficient and 0.04974 for mass; the most frequent violated first-contact criterion also changes from vertical speed in many conditions to angular rate for mass -10% and thrust-coefficient +10%[cite: 11]. At fixed $\eta=0.496$, PINV has a higher M2 safe count than frozen CEM in 9/11 tested plant states and ties in 2/11; the two ties are both 0/30[cite: 11]. The experiments therefore distinguis”
Exact source · revision 1Source owner not reported
Reported quantity · remaining effectiveness threshold: 0.496 other · Basis not reported
“e), and the same residual allocator with scoring parameters tuned offline by the Cross-Entropy Method (CEM)[cite: 11]. For this study, fault geometry is represented by the known failed-motor index[cite: 11]. At $\eta=0.496$ remaining effectiveness, matched development trials select CEM for M1, M3, and M4 and PINV for M2; the M3 selection is a one-trial margin (29/30 versus 28/30)[cite: 11]. After freezing this routin”
Exact source · revision 1Source owner not reported
Evidence dependency changes (1)
- 2026-11-09T21:43:26.033Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalFault-Geometry-Aware Control Allocation for Emergency Quadrotor Landing under Single-Motor Loss of Effectiveness
This research compares three control allocators—bounded weighted least squares (PINV), a finite-candidate residual allocator (QP-lite), and a Cross-Entropy Method-tuned variant—for emergency quadrotor landing following single-motor loss of effectiveness. Using Crazyflie software-in-the-loop simulations, the study determines that the Cross-Entropy Method approach is selected for motors M1, M3, and M4, while PINV performs better for motor M2. The policy achieves 117/120 safe first-contact touchdowns on fresh seeds and 115/120 in a randomized-order integrated-supervisor evaluation. Sensitivity analysis reveals that one-factor-at-a-time plant perturbations can shift the nominal effectiveness threshold by up to 0.05495 for thrust coefficient and 0.04974 for mass, with the most frequently violated first-contact criterion changing from vertical speed to angular rate under certain conditions.
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