Fault-Geometry-Aware Control Allocation for Emergency Quadrotor Landing under Single-Motor Loss of Effectiveness
Organizations: Department of CSE, Sidd ಮಾಗಾ Institute of Technology, Visvesvaraya Technological University, Tumakuru, Karnataka, India · Department of Economics, St. Joseph’s University, Bengaluru, Karnataka, India
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-lite), 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 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 routing 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 with a within-model 95% bootstrap interval of approximately [0.4955, 0.4956][cite: 11]. One-factor-at-a-time plant perturbations shift by up 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 , 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 distinguish motor-conditioned allocator selection from sensitivity to plant-model mismatch[cite: 11].
Figures & tables
| Experiment | Configuration | Replication | Trials |
| Complementarity | 4 motors 3 allocators, | 30 matched/motor | 360 |
| Fresh holdout | frozen routing | 30/motor | 120 |
| Integrated supervisor | frozen routing, randomized order | 30/motor | 120 |
| Nominal M2 boundary | 7 values | 30/ | 210 |
| OFAT sensitivity | 10 plants 7 local | 30/ | 2100 |
| M2 routing stability | 11 plants PINV/CEM | 30 paired | 660 |
Quadrotor Control
Quadrotor control studies how to stabilize, guide, and adapt aerial robots under demanding flight conditions and physical constraints.
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