A Novel 2-DoF Reluctance Electromagnetic Precision Motion System: Design, Modeling, and Control
Bibliographic record
Abstract
This study presents the design, modeling, and control of a novel two-degree-of-freedom (2-DoF) electromagnetic actuator (EA) system proposed for high-precision motion applications. The proposed 2-DoF EA integrates a reluctance actuator (RA) for translational motion and two solenoid actuators (SAs) with permanent magnet (PM) plungers for rotational motion. This configuration enables simultaneous and independent control of translational (x) and rotational (θ) motions, offering a compact solution for 2-DoF precision positioning tasks. In the proposed 2-DoF EA, translational motion (x) is primarily achieved through the force generated by the RA. In contrast, rotational motion (θ) is governed by the torque resulting from the differential forces produced by the two SAs. A comprehensive electromechanical model is formulated to describe the system as a multi-input, multi-output (MIMO) system. The model presents a coupling between thexandθmotions, particularly through the RA-generated force, which depends on translational and rotational motion. Based on the developed model, a feedforward controller is designed to linearize the system’s dynamic behavior and compensate for the nonlinear coupling effects. Subsequently, a feedback controller is implemented to improve the trajectory tracking accuracy under various motion profiles. An experimental prototype of the proposed 2-DoF EA is presented to investigate the concept of this design and for experimental validation. In the proposed prototype, a flexure hinge mechanism is used to restrain the force generated by RA, opposite the negative stiffness of RA, and release the mover to its initial position. The experimental design demonstrates the feasibility of the proposed 2-DoF EA design to achieve both translational and rotational motion. The experimental results show steady-state errors of approximately ±7 μm inxtranslation and ±0.3 mrad inθrotation.
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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) | 0.000 | 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 | 0.001 |
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".