Fuzzy‐sliding mode control of nonlinear smart base‐isolated building under earthquake excitation
Bibliographic record
Abstract
Summary In this paper, the effectiveness of the fuzzy sliding mode control strategy on three‐dimensional benchmark building with smart base isolation under seismic excitation has been examined. One of the appropriate control theories for such this nonlinear system is the sliding mode control theory; discontinuous sliding mode theory has weakness such as chattering phenomena. In this paper, we used a combination of fuzzy logic and sliding mode theory for the deletion of this defect. The proposed control theory has been scrutinized by applying on lately developed nonlinear three‐dimensional base‐isolated benchmark building. This building because of the three‐dimensional nature, coalescing of lateral and torsional responses, continuity of responses of the superstructure, and base is modeled with three degrees of freedom on every floor. In this building eight actuators assigned only at the base level and in the two directions (x, y). In other words, 16 actuators are located underneath the structure. Furthermore, the base isolation system has been modeled by considering lateral coupled equations for a better examination of the performance of the system. The results indicate that reduction of control performance is remarkable. Also, utilizing proposed control theory can decrease the responses of building in two main directions and, particularly, in the rotational degree of freedom.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".