Soft Computing Based Sliding Surface Adjustment of Second Order Sliding Mode Controllers: An Application to Ship Steering Model
Bibliographic record
Abstract
This work introduces a novel second-order sliding mode (SOSM) controller for the control of dynamicuncertain systems based on fuzzy logic. An efficient sliding surface design approach for improving controllerperformance is to use time-varying sliding surfaces. The proposed controller incorporates second-order slidingmode control, fuzzy logic control, and adaptive control benefits. The proposed controller ensures the system'sreaching conditions, stability, and robustness. The proposed controller is also well-suited for straightforwarddesign and implementation. In order to rotate the sliding surface in a way that improves the trackingperformance of the system under control, this control strategy uses a time-varying slope in the sliding surfacefunction and a straightforward two-input single-output fuzzy logic controller. The proposed controller isstudied with a ship steering model in comparison with a conventional second-order sliding mode controllerwith a fixed sliding surface. Simulations of the ship steering system using MATLAB/SIMULINK show thatthe proposed controller outperforms the typical second-order sliding mode controller.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".