SMA-Based Tuning of PI Controller Using Takagi-Sugeno Fuzzy Observers for an Electromechanical System with Variable Parameters
Bibliographic record
Abstract
This paper proposes a conventional control structure with two Takagi-Sugeno Fuzzy Observers (TSFOs) for estimating the angular speed, the overall moment of inertia and the roller radius for an electromechanical system with variable parameters. The described TSFOs are used to estimate states in a complex and nonlinear mechanism, namely strip winding system, which has the capability to wrap a strip with invariable linear speed on a roller knowing that the angular speed and the overall moment of inertia are being modified by the variable roller radius. This paper derives the conditions for the stability of the control system and the observer development, which are represented as linear matrix inequalities. The effectiveness of TSFOs is evaluated with regard to achieving a specific rate of convergence. The conventional control structure employs, in conjunction with the two TSFOs a Proportional-Integral controller with parameters optimally tuned using a metaheuristic slime mould algorithm that solves the optimization problems with objective functions described as the integrals of squared control errors multiplied by time. The control system efficacy is demonstrated and validated through digital simulation results focusing on a comparative analysis of the two TSFOs with the optimally tuned parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".