doi:10.1155/2009/897023 Research Article Prototyping Advanced Control Systems on FPGA
Bibliographic record
Abstract
In advanced digital control and mechatronics, FPGA-based systems on a chip (SoCs) promise to supplant older technologies, such as microcontrollers and DSPs. However, the tackling of FPGA technology by control specialists is complicated by the need for skilled hardware/software partitioning and design in order to match the performance requirements of more and more complex algorithms while minimizing cost. Currently, without adequate software support to provide a straightforward design flow, the amount of time and efforts required is prohibitive. In this paper, we discuss our choice, adaptation, and use of a rapid prototyping platform and design flow suitable for the design of on-chip motion controllers and other SoCs with a need for analog interfacing. The platform consists of a customized FPGA design for the Amirix AP1000 PCI FPGA board coupled with a multichannel analog I/O daughter card. The design flow uses Xilinx System Generator in Matlab/Simulink for system design and test, and Xilinx Platform Studio for SoC integration. This approach has been applied to the analysis, design, and hardware implementation of a vector controller for 3-phase AC induction motors. It also has contributed to the development of CMC’s MEMS prototyping platform, now used by several Canadian laboratories. Copyright © 2009 Stéphane Simard et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.778 | 0.753 |
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".