Fault/Damage Tolerant Control of a Quadrotor Helicopter UAV using Model Reference Adaptive Control and Gain-Scheduled PID
Bibliographic record
Abstract
In this paper, two useful approaches to Fault Tolerant Control (FTC) for a quadrotor helicopter Unmanned Aerial Vehicle (UAV) in the presence of fault(s) in one or more actuators during flight have been investigated and experimentally tested based on a Model Reference Adaptive Control (MRAC) and a Gain-Scheduled Proportional-IntegralDerivative (GS-PID) control. A Linear Quadratic Regulator (LQR) controller is used in cooperation with the MRAC and the GS-PID to control the pitch and roll attitudes of the helicopter. Unlike the MRAC, the GS-PID is used only to control the helicopter in height control mode. MRAC is used to control the helicopter in both height control as well as trajectory control. For damage tolerant control the MRAC is evaluated based on partial damage of one of propellers during flight. Finally, the experimental flight testing results of both controllers are presented for the fault tolerant control performance comparison in the presence of actuator faults in the quadrotor UAV.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".