Fault-Tolerant Control of Quadrotor Helicopter Using Gain-Scheduled PID and Model Reference Adaptive Control
Bibliographic record
Abstract
This paper presents development, implementation, experimental testing and comparison of two useful control approaches to a quadrotor Unmanned Aerial Vehicle (UAV) test-bed available at Concordia University for the purpose of enhancing reliability, safety and Fault-Tolerant Control (FTC) of the UAV. A Gain-Scheduled Proportional-Integral-Derivative (GS-PID) controller and a Model Reference Adaptive Control (MRAC) scheme are the main control techniques investigated in this paper based on their wide and popular applications in many engineering systems. Controllers are designed and implemented on the on-board single-chip micro-computer in order to keep the desired height of the helicopter in both normal (faultfree) and faulty flight conditions. In the MRAC case, in addition to height control, some other faulty cases are also considered for the trajectory tracking control with typical trajectories such as square shape trajectory. Finally, comparison results based on experimental testing of the two types of controllers on the UAV test-bed are presented. From the operational point of view, MRAC showed a promising performance for handling the fault imposed to all actuators as well as good fault-free control performance. On the other hand, the GS-PID controller with a linear transition between modes was able to react in a faster way than MRAC to maintain good control of the quadrotors height if the controller reaction/switching time is kept as short as possible. In other words, the GS-PID showed stronger fault-tolerant control capability than MRAC to keep the height of the helicopter if the switching time of the GS-PID controller gains after fault occurrence is kept close to zero.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 teacher head, 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".