Boundary Layer-Based Event-Triggered Sliding Mode Controller for Nonlinear Systems with Uncertainty
Bibliographic record
Abstract
While maintaining the stability of a nonlinear system is a difficult and complicated issue, it becomes even more complicated when considering the uncertainty of system parameters.To accomplish robust stability of such systems and satisfactory performance, it is required to continuously monitor the system measurements and provide periodic control actions, which is impractical in systems with limited communication bandwidth or processing capacity.This paper considers the use of event-triggered sliding mode control (ET-SMC) in controlling a nonlinear system that has the appealing property of updating the computed control action only if an event indicates that the control error exceeds a tolerable limit.Which notably saves computations in the control task while ensuring equivalent performance.First, the ET approach is used in combination with continuous time sliding mode control (CT-SMC).Next, the ET approach is used in combination with discrete time sliding mode control (DT-SMC).Then, a comparison is held between the two controllers to show the superiority of adding the ET technique to the designed controllers (CT-SMC and DT-SMC).In both cases, the well-known Zeno Phenomenon is a possible challenge that should be avoided.In this paper, waiting for a fixed amount of time after each sampling instant and checking a state-dependent criterion is the technique used in the transmission conditions to prevent the occurrence of the Zeno phenomenon.The simulation results demonstrate the effect of adding the ET strategy to the SMC in both continuous and discrete cases.The ET-based controllers developed in this study reduce the number of samples required to generate control action by approximately 98% as compared to the classical sliding mode control schemes.In addition to maintaining robust stability and satisfactory system performance in the presence of uncertainty.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".