A new motion control hardware architecture with FPGA-based IC design for robotic manipulators
Bibliographic record
Abstract
In this paper, a new motion control hardware architecture is proposed for improved motion performance of robotic manipulators during high speed motion. The main idea is to remove the servo control loop from the DSP (digital signal processor) to a FPGA (field programmable gate array), and utilize the high speed hardwired logic of the FPGA to enhance the computation capability and relieve the computing load on the DSP. The control algorithm is partitioned into a linear portion and a nonlinear portion. The linear portion with position/velocity feedback represents the major control loop and is implemented in the FPGA. The nonlinear portion acts as dynamic compensation to the linear portion to perform complex modeling related calculations, and is implemented in the DSP. A new FPGA-based motion control IC is designed to realize this new control hardware structure. Experiments were conducted on a Yamaha robot manipulator to compare new control architecture and the existing one, when the same control algorithm was used. Experimental results demonstrate that the proposed new control architecture exhibits much improved motion performance especially during high-speed motions
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".