Equivalent Time-Invariant Modelling of Electrohydraulic Actuators with Application to Robust Control Synthesis
Bibliographic record
Abstract
An important aspect of robust control development around hydraulic actuators is establishing a set of equivalent linear time-invariant (LTI) models that describe the dynamics of the system over the desired envelope of operation. The nonlinearities inherent in the hydraulic functions must be recast into an equivalent linear form in order to make the robust control problem amenable to solution by linear techniques. This paper develops a simple model-based approach for evaluating equivalent LTI frequency response functions of an electrohydraulic actuator by Fourier transformation of acceptable actuator input-output data. The efficacy of the numerical procedure is compared with two other available methods, namely small-signal analysis and Golubev's least-squares approach. It is shown that the proposed approach can describe large signal effects and at the same time properly characterize the features of the hydraulic actuator frequency response that are important for robust control design, without the need for a priori information about the asymptotic behaviour or structure of the equivalent LTI transfer function. The applicability of the proposed numerical technique towards development of practical controllers for fluid power systems is demonstrated by the results of a typical robust control design example for an experimental electrohydraulic positioning system.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".