Adaptive Observer-Based Integrated Fault Diagnosis and Fault-Tolerant Control Systems Against Actuator Faults and Saturation
Bibliographic record
Abstract
A challenging problem on observer-based, integrated fault diagnosis and fault-tolerant control for linear systems subject to actuator faults and control input constraints is studied in this paper. An adaptive observer approach is used for the joint state-fault magnitude estimation, and a feedback controller is designed to stabilize the closed-loop system without violating the actuator limits in the presence of actuator faults. Matrix inequality conditions are provided for computation of design parameters of the observer and the feedback controller, and the admissible initial conditions and estimation errors are bounded by invariant ellipsoidal sets. The design results are closely related to the fault magnitude and variation rate, and a necessary condition on the admissible fault magnitudes dependent on the control limits is directly obtained from the design process. The proposed design framework allows a direct application of the pole placement method to obtain stabilization results. To improve the system performance, a nonlinear programming-based optimization algorithm is proposed to compute an optimized feedback gain, whereas the one obtained by pole placement can be taken as an initial feasible solution for nonlinear optimization. Numerical studies with two flight control systems demonstrate the effectiveness of proposed design techniques.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".