Active Flatness Control of Membrane Structures Using Fuzzy Logic Integrated Genetic Algorithm
Bibliographic record
Abstract
Membrane structures are attracting attention as excellent candidates for lightweight large space structures, which can be utilized to improve the performance and reduce the cost of space exploration and earth observation missions. Membrane structures can be stowed to a small volume during launch and function as large structures after deployed. Membrane structures to be used in large synthetic aperture radar (SAR) satellites will have flatness issue subject to thermal disturbance in the space environment. Active shape control is a vital technology in maintaining flatness of membrane structures, therefore to ensure functionality of the antenna, in the time-varying environment. In this research, multiple shape memory alloy (SMA) actuators around the boundary of a rectangular membrane are used to apply tension forces to membrane structures to compensate for wrinkle effects. The dynamics of membrane structures is nonlinear and computationally expensive, hence unfeasible to be used in real-time active flatness control. As a parallel direct searching method, genetic algorithm (GA) is used search optimal tension force combination on a high dimensional nonlinear surface. Due to large number of tension forces to search, robust and mature convergence is more difficult to attain. In order to increase responsiveness and convergence of genetic algorithm, adaptive regulation of genetic algorithm parameter is important. Rather than heuristic adaptive rules, fuzzy logic integrated genetic algorithm (FLIGA) is proposed and designed in this paper. Fuzzy logic rules are incorporated in an adaptive genetic algorithm to regulate control parameters, such as mutation rate and crossover rate. Through numerical and experimental validation, it is demonstrated that FLIGA can expedite its search process and prevent premature convergence.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".