Regulation and Tracking Control of Omnidirectional Rotation for Spherical Motors
Bibliographic record
Abstract
Spherical motors capable of omnidirectional rotations in a ball joint provide a highly dexterous actuator system yet also present great challenges in developing a high-performance regulation and tracking controller due to the complex rotor dynamics. This article presents a complementaryH2-H∞(C-H2-H∞) control method for precisely controlling the multidegree of freedom (DOF) orientation of spherical motors in presence of external disturbances as well as model uncertainties and inaccuracies. In order to deal with the tradeoff between the robustness and control performance resulted from conventional control methods, the proposed controller featured with a 2-DOF structure presents anH2controller for a nominal plant and then complements it with an additional regulator designed inH∞sense to assure robustness. Unlike traditionalH∞or mixedH2andH∞controller, theH∞regulation is conducted online in accordance with the monitored modeling mismatch in a way that does not adversely degrade the performance delivered byH2control, hence, improving the conservativeness of the total control performance and leading to significant improvements of tracking accuracy and response time at same time. Both numerical simulations and experiments on a spherical motor testbed are conducted to validate the superior performance of the proposed controller versus conventional control methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".