Stable Adaptive Control of Seismically Excited Nonlinear Structures
Bibliographic record
Abstract
This paper presents a robust direct adaptive control scheme for the active control of nonlinear base isolated buildings subjected to near-fault earthquakes. The control architecture is based on the premise of direct adaptive control, where the system is made to follow a desired trajectory without the need of an identifier. The control force is calculated using a single hidden layer nonlinearly parameterized neural network in conjunction with a Proportional-Derivative (PD) type controller. Stable tuning laws for the free parameters of the nonlinearly parameterized network are derived based on Lyapunov theory. To achieve good performance and to ensure that the network parameters remain bounded, initialization of the weights is required. A perturbed model is used for the initialization purposes in order to simulate the uncertainty typical of the mathematical models of civil engineering structures. The initialized parameters provide a starting point for the subsequent online adaptation of the controller under earthquake excitations. 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, issues that have previously not received the attention they deserve in a majority of the neural network based structural control approaches available in the literature. The robustness of the controller is investigated under actuator failure conditions. Simulations are performed on a full-scale nonlinear three-dimensional base isolated benchmark structure incorporating lateral-torsion superstructure behavior and bi-axial interaction of the nonlinear bearings in the isolation layer. Results are presented in terms of a comprehensive set of performance indices to reflect the tradeoffs in performance commonly associated with structural control methods.
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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".