Fault tolerance evaluation of model-free controllers with application to unmanned aerial vehicles
Bibliographic record
Abstract
Although important improvements in the area of robust control of nonlinear systems have been presented in the literature, most of the developed controllers suffer from complexity and large dependency on accurate mathematical formulation of the models. Recently, model-free robust control techniques were introduced and have shown good performance when applied to multi-input–multi-output systems. The model-free approach is characterized by the nonuse of any prior knowledge about the underlying structure and/or associated parameters of the dynamical system. Therefore, the major criteria for assessing the effectiveness of these controllers are related to their ability to handle unknown inputs and disturbances, as well as achieving the desired tracking performance in presence of faults and malfunctions. This work considers the development of robust fault-tolerant controllers based on the model-free approach and their application to multirotor unmanned aerial vehicles’ (UAVs) systems. The different controllers based on intelligent proportional-derivative (iPD), intelligent backstepping (iBackstepping), and adaptive control are compared in terms of performance, ease of implementation and parameters tuning. The simulated results, tested on Matlab/Simulink on a full nonlinear model of a hexarotor UAV, validate the theoretical advantages of the adaptive approach with respect to multiple criteria such as improved tracking performance in case of existence of actuators faults when compared to the iPD and iBackstepping control methods at the cost of increased complexity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".