Modeling and Robust Discrete-Time Sliding-Mode Control Design for a Fluid Power Electrohydraulic Actuator (EHA) System
Bibliographic record
Abstract
This paper studies the design of a robust discrete-time sliding-mode control (DT-SMC) for a high precision electrohydraulic actuator (EHA) system. Nonlinear friction in the hydraulic actuator can greatly influence the performance and accuracy of the hydraulic actuators, and it is difficult to accurately model the nonlinear friction characteristics. In this paper, it is proposed to characterize the frictions as an uncertainty in the system matrices. Indeed, the effects of variations of the nonlinear friction coefficients are considered as norm-bounded uncertainties that span a bounded region to cover a wide range of the real actuator friction. For such a discrete-time dynamic model, for the EHA system with system uncertainty matrices and a nonlinear term, a sufficient condition for existence of stable sliding surfaces is proposed by using the linear matrix inequality approach. Based on this existence condition, a DT-SMC is developed such that the reaching motion satisfies the discrete-time sliding mode reaching condition for uncertain systems. Simulation and experimental studies on the EHA system illustrate the effectiveness and applicability of the proposed method.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".