Adaptive Fault-Tolerant Control for a 2-Body Point Absorber Wave Energy Converter Against Actuator Faults: An Iterative Learning Control Approach
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
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> 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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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