Optimal energy-saving controller design for an electro-hydrostatic actuator integrated into a wheel loader
Bibliographic record
Abstract
Electrifying non-road mobile machinery is vital for reducing emissions and improving energy efficiency. Replacing conventional hydraulic systems with electro-hydrostatic actuators is a key step in this transition. However, developing controllers that ensure dynamic performance, energy efficiency, and thermal management remain challenging. This paper proposes an optimal energy-saving controller, LQFFRO, for an EHA integrated into a wheel loader. The LQFFRO minimizes energy consumption and improves thermal performance while maintaining precise trajectory tracking under varying loads. Simulation results show that LQFFRO achieves tracking accuracy comparable to a PID controller, with position errors within 3 mm, while significantly reducing peak current and voltage. It also lowers the electric motor’s temperature by 2.5°C and reduces energy consumption by 0.395 kJ per cycle, leading to annual energy savings exceeding 2.84 million kJ compared to PID. Lyapunov-based analysis confirms the closed-loop system’s robustness under disturbances. These results confirm that LQFFRO controller effectively balances dynamic performance, energy efficiency, and thermal stability, contributing to the reliable and sustainable electrification of non-road mobile machinery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".