Direct adaptive neural controller for the active control of earthquake-excited nonlinear base-isolated buildings
Bibliographic record
Abstract
This paper presents a nonlinearly parameterized controller for the adaptive control of base-isolated buildings subjected to a set of near-fault earthquakes. The control scheme is based on discrete direct adaptive control, wherein the system response is minimized under parameter uncertainties. Stable tuning laws for the controller parameters are derived using the Lyapunov approach. The controller utilizes a linear combination of nonlinear basis functions, and estimates the desired control force online. The measurements that are necessary to generate the control force to reduce the system responses under earthquake excitations are developed based on the adaptive systems theory. The main novelty in this paper is to approximate the nonlinear control law using a nonlinearly parameterized neural network, without an explicit training phase. A perturbed model is used to initialize the controller parameters in order to simulate the uncertainty in the mathematical modeling that typically exists in representing civil structures. Performance of the proposed control scheme is evaluated on a full-scale nonlinear three-dimensional (3-D) base-isolated benchmark structure. The lateral-torsion superstructure behavior and the bi-axial interaction of the nonlinear bearings are incorporated. The results show that the proposed controller scheme can achieve good response reductions for a wide range of near-fault earthquakes, without a corresponding increase in the superstructure response. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 | 0.001 | 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".