Adaptive Fault-Tolerant Control for a 2-Body Point Absorber Wave Energy Converter Against Actuator Faults: An Iterative Learning Control Approach
Bibliographic record
Abstract
In this paper, the design issue of adaptive fault-tolerant control (FTC) is investigated for a class of continuous-time 2-body point absorber wave energy converter (WEC) systems against actuator faults based on the iterative learning approach. The actuator faults considered in this paper contain both the lock-in-place and the loss of effectiveness faults, simultaneously. The WEC dynamic equations, including two moving parts (i.e., the float and the spar), are firstly transformed into a state-space model. Then, a group of novel iterative learning based adaptive multiple controllers are developed to decrease the tracking error between the measurement output and the desired output, and two novel adaptive laws are designed to cope with two types of actuator faults. Based on the theories mentioned above, a novel algorithm is provided to present the operation flows of both adaptive laws and iterative learning. Furthermore, a sufficient condition is obtained with the aid of proper Lyapunov function, such that the related closed-loop faulty-WEC system is asymptotically stable with a guaranteed$H_{\infty }$performance index. Finally, an example with a set of physical parameters of a WEC dynamic model is worked out to verify the applicability and effectiveness of the proposed iterative learning based adaptive FTC strategy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".