Optimization of Sliding Mode and Back-Stepping Controllers for AMB Systems Using Gorilla Troops Algorithm
Bibliographic record
Abstract
An active magnetic bearing (AMB) is a frictionless bearing used in high-speed motors and other electromechanical products.Due to its open loop instability, utilization of controller is essential to stabilize the system.In this paper, a comparative study between sliding mode control (SMC) and back-stepping control (BSC) are presented for AMB systems.These two controller techniques have been applied for various dynamical systems to obtain stable control systems.On the basis of avoiding the chattering in the SMC design, the power rate reaching is introduced in the design of the control action of SMC.In terms of BSC design, Lyapunov-stability theorem is utilized to derive the control low of the controller.A gorilla troops optimization (GTO) has been applied to tune the adjustable parameters of the proposed controllers.According to the computer simulation based on MATLAB software, the results indicate a superior performance and improved in the system response of the SMC as compared to the BSC controller.In addition, the SMC strategy has a good disturbance rejection capability as compared to the BSC strategy.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".