An Alternative Nonlinear Lyapunov Redesign Velocity Controller for an Electrohydraulic Drive
Bibliographic record
Abstract
This research aims at developing control law strategies that improve the performances and the robustness of electrohydraulic servosystems (EHSS) operation while considering easy implementation. To address the strongly nonlinear nature of the EHSS, a number of control algorithms based on backstepping approach is intensively used in the literature. The main contribution of this paper is to consider an alternative approach to synthetize a Lyapunov redesign nonlinear EHSS velocity controller. The proposed control law design is based on an appropriate choice of the control lyapunov function (clf), the extension of the Sontag formula and the construction of a nonlinear observer. The clf includes all the three system variable states in a positive define function. The Sontag formula is used in the time derivative of our clf in order to ensure an asymptotic stabilizing controller for regulating and tracking objectives. A nonlinear observer is developed in order to bring to the proposed controller the estimated values of the first and the second time output derivatives. The design, the tuning implementation and the performances of the proposed controller are compared to those of its equivalent backstepping controller. It is shown that the proposed controller is easier to design with simple implementation tuning while the backstepping controller has several complex design steps and implementation tuning issue. Moreover, the best performances especially under disturbance in the viscous damping are achieved with 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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".