Globally Fixed-Time High-Order Sliding Mode Control for New Sliding Mode Systems Subject to Mismatched Terms and Its Application
Bibliographic record
Abstract
High-order sliding mode (HOSM) control is used for output regulation of uncertain systems with known relative degree. Yet, the traditional HOSM algorithm can only achieve finite-time output regulation for standard integrator sliding mode systems, whose settling time depends on system initial conditions. In this article, first, we extend the standard sliding mode system to a new sliding mode system subject to mismatched terms. The use of the new sliding mode system can reduce the uncertainties in input channel and/or relax the well-defined relative degree assumption. Second, the conventional constant upper bounds assumption is relaxed to time-varying functions, which enables us to obtain a globally convergent controller. Finally, for the new sliding mode system under the new global assumption, we propose a novel fixed-time HOSM controller whose settling time can be predefined and is free of system initial conditions. In addition, strict Lyapunov analysis is provided to show that the new sliding mode system under the proposed controller and the new global assumption is globally fixed-time stable. An application to buck converter is given to show the effectiveness of the proposed controller.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".