Fault-Tolerant Trajectory Tracking Control of a Quadrotor Helicopter Using Gain-Scheduled PID and Model Reference Adaptive Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Based on two successfully and widely used control techniques in many industrial applications under normal (fault-free) operation conditions, the Gain-Scheduled Proportional-Integral-Derivative (GS-PID) control and Model Reference Adaptive Control (MRAC) strategies have been extended, implemented, and experimentally tested on a quadrotor helicopter Unmanned Aerial Vehicle (UAV) test- bed available at Concordia University, for the purpose of investigation of these two typical and different control techniques as two useful Fault-Tolerant Control (FTC) approaches. Controllers are designed and implemented in order to track the desired trajectory of the helicopter in both normal and faulty scenarios of the flight. A Linear Quadratic Regulator (LQR) with integral action controller is also used to control the pitch and roll motion of the quadrotor helicopter. Square trajectory, together with specified autonomous and safe taking-off and landing path, is considered as the testing trajectory and the experimental flight testing results with both GS-PID and MRAC are presented and compared with tracking performance under partial loss of control power due to fault/damage in the propeller of the quadrotor UAV. The performance of both controllers showed to be good. Although GS-PID is easier for development and implementation, MRAC showed to be more robust to faults and noises, and is friendly to be applied to the quadrotor UAV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it