Adaptive control of nonlinear smart base-isolated buildings using Gaussian kernel functions
Bibliographic record
Abstract
In this paper, a direct adaptive control scheme using Gaussian kernel functions is presented for the active control of nonlinear base-isolated buildings. The control scheme is based on direct adaptive control where the system response is made to follow a desired trajectory. The number of kernel functions is adaptively estimated using a growing and pruning strategy which results in the reduction of the computational overhead. Stable adaptive parameter update laws for Gaussian kernels are derived using Lyapunov approach. Performance of the proposed control scheme is evaluated on the recently developed nonlinear three-dimensional base-isolated benchmark structure. The analytical model of the benchmark structure is highly complex due its three-dimensional nature incorporating lateral and torsional responses, the biaxial interaction of the nonlinear bearings at the isolation layer, and strong coupling between the isolation level forces and the superstructure responses. Control action is provided by eight actuators distributed at the isolation level in each principal direction of the structure, and utilizing the state information corresponding to the base of the structure only. Results are presented using a comprehensive set of the performance indices to realistically quantify the trade-offs associated with the control of nonlinear base-isolated buildings. The main advantages of the adaptive controller presented in this paper are: (i) the control algorithm does not require estimating the system parameters, specifically, mass, stiffness and damping, (ii) the exact nature of the nonlinear dynamics need not be known, and (iii) the control synthesis is noniterative, and on-line. Copyright © 2007 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 | 0.000 |
| 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 | 0.001 | 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".