Adaptive Position Control of an Electrohydraulic Servo System With Load Disturbance Rejection and Friction Compensation
Bibliographic record
Abstract
Electrohydraulic servo systems (EHSS) are used for several engineering applications, and in particular, for efficient handling of heavy loads. These systems are characterized by pronounced nonlinearities and are also subject to parameter variations during operation, friction effects, and variable loads. Several studies have addressed the nonlinear nature of EHSS; however, only a few control schemes explicitly address friction and load disturbance effects along with parameter variations. Fuzzy and/or sliding mode versions of feedback linearizing controllers have been used to compensate for the external loads disturbances in the control of EHSS. However, real-time implementations issues limit the use of these techniques. While adaptive control using a feedback-linearization based controller structure has been shown to be effective in the presence of parameter variations, load and friction effects are typically not considered. In this paper, we present a nonlinear adaptive feedback linearizing position controller for an EHSS, which is robust to parameter uncertainty while achieving load disturbances rejection/attenuation and friction compensation. The adaptation law is derived using a Lyapunov approach. Simulation results using the proposed controller are compared to those using a nonadaptive feedback linearizing controller as well as a proportional-integral-derivative (PID) controller, in the presence of torque load disturbance, friction, and uncertainty in the hydraulic parameters. These results show improved tracking performance with the proposed controller. To address implementation concerns, simulation results with noise effects and valve saturation are also presented.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".