Robust direct adaptive controller for the nonlinear highway bridge benchmark
Bibliographic record
Abstract
This paper presents a direct adaptive control scheme for the active control of the nonlinear highway bridge benchmark. The controller is based on the premise of direct adaptive control, where-in the system response is made to follow a desired trajectory. The principal problem of the unavailability of the correct network output is inferred from the observed structure behavior. The control force in this paper is calculated using a single hidden layer nonlinearly parameterized neural network in conjunction with a proportional-derivative type controller. The neural network is utilized to approximate the nonlinear control law, that is known to exist, and not the system nonlinearities. Stable tuning laws for the free parameters of the nonlinearly parameterized network are derived based on Lyapunov theory. Set in the framework of adaptive control, the proposed control architecture addresses important issues related to the stability of the closed loop system and parameter bounds. Performance of the proposed control scheme is evaluated on the recently proposed nonlinear highway bridge benchmark, incorporating nonlinear isolation bearings and nonlinear structural elements. Results are presented in terms of a well-defined set of performance indices. The results show that the proposed controller scheme can achieve good response reductions in the structure, without the need for the exact description of the nonlinearities, or extensive structural system information. Copyright © 2009 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".