Active fault‐tolerant control system design with trajectory re‐planning against actuator faults and saturation: Application to a quadrotor unmanned aerial vehicle
Bibliographic record
Abstract
SUMMARY During the past 30 years, various fault‐tolerant control (FTC) methods have been developed to address actuator or component faults for various systems with or without tracking control objectives. However, very few FTC strategies establish a relation between the post‐fault reference trajectory to track and the remaining resources in the system after fault occurrence. This is an open problem that is not well considered in the literature. The main contribution of this paper is in the design of a reconfigurable FTC and trajectory planning scheme with emphasis on online decision making using differential flatness. In the fault‐free case and on the basis of the available actuator resources, the reference trajectories are synthesized so as to drive the system as fast as possible to its desired setpoint without violating system constraints. In the fault case, the proposed active FTC system (AFTCS) consists in synthesizing a reconfigurable feedback control along with a modified reference trajectories once an actuator fault has been diagnosed by a fault detection and diagnosis scheme, which uses a parameter‐estimation‐based unscented Kalman filter. Benefited with the integration of trajectory re‐planning using the flatness concept and the compensation‐based reconfigurable controller, both faults and saturation in actuators can be handled effectively with the proposed AFTCS design. Advantages and limitations of the proposed AFTCS are illustrated using an experimental quadrotor unmanned aerial vehicle testbed.Copyright © 2013 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".