Lyapunov‐based adaptive state‐space controller for liquid level control of a coupled tank system with unknown model
Bibliographic record
Abstract
Abstract In this paper, a Lyapunov‐based adaptive state space controller is designed to realize liquid level control in a coupled tank system. The proposed controller is a kind of adaptive control system that combines the principles of state space representation with Lyapunov stability theory to estimate unknown parameters and control a dynamic system. This approach ensures that the system remains stable even when the exact model parameters are unknown or change over time. Moreover, in order to test the performance of the proposed controller, proportional integral (PI) + feedforward (FF) controller is also applied to the same system under the same conditions and the results are compared. The results show that the proposed controller outperforms the PI + FF controller in terms of system response parameters such as rise time, percent overshoot, and settling time. On the other hand, when the error performance metrics integral squared error (ISE), integral time‐weighted squared error (ITSE), integral time‐weighted absolute error (ITAE), and integral absolute error (IAE) are evaluated, it is seen that the same situation is also realized here, and the proposed controller follows the reference signals with lower error values in real‐time studies for different reference signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".