Generic Modeling and Control of an Open-Circuit Piston Pump—Part II: Control Strategies and Designs
Bibliographic record
Abstract
Hydromechanical compensators are often integrated with piston-type pumps to produce various control behavior, for example, pressure, load-sensing, power, or torque control. Various hydromechanical mechanisms (e.g., spring forces and load pressure) are found in the industry to ensure the desired effect of the system outputs: swash angle, discharge pressure, and input torque following the reference inputs. In a companion paper (Part I of this paper), a generic linearized state-space model is derived to investigate the pump dynamics and determine the design criteria and parameters. In the study, the state-space equations are used to propose and define the generic compensating control pump to conduct the similar strategies as hydromechanical pumps do. The different control purposes (pressure/flow/power compensating) are accomplished by only manipulating the generic regulate inputs given by an electrical proportional control valve. In the open-circuit pump, the generic controllers are proposed to generate these inputs by using one unique mechanical and electronic architecture to establish various purposes of flow, pressure, torque desired control, and even more control objectives. The controller is developed in accordance with the state-space representation and by following the models of the hydromechanical compensators that can facilitate the correlation verification. The proposed controllers are able to offer more intelligent and cost-saving control strategies for open-circuit piston pumps. To achieve the similar performance as hydromechanical compensators do and implement the comparative study, control gains and settings in the controller can be determined from ones that hydromechanical compensators have. The difference is that electronic signals (swash plate position, pressure, etc.) work as feedbacks together with other control gains to regulate the input signals. For the different control purposes, control gains are able to be set conveniently for the various control operating conditions with combining the certain feedbacks on the same hardware platform that will be value efficient and capable of control intelligence. In the paper, results of predictions made by the model are presented and compared with some experimental data of hydromechanical designs. Further work on the advanced model-based control and estimation is anticipated to be addressed.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".