Implementation of multiple PID controllers on FPGA
Bibliographic record
Abstract
Proportional Integral Control (PID) is one of the most widely used control techniques. Its main advantages are simplicity of design and ease of implementation. Although many other control techniques have been proposed and used, the PID controller is the workhorse of the industry. Usually, PID controllers are implemented on microcontrollers. However, with the increase of the use of FPGA's and especially when we require a large number of controllers controlling the same plant (although many processes), FPGA's seem as a very good alternative. One point though, today's FPGA chips run at a frequency of 50-100 MHz or even more for high-end chips. That is very high frequency than what is required for most PID controllers. Our goal is to utilize the FPGA chip resources to implement multiple PID controllers in the same chip. In this paper, we present a technique to implement multiple PID controllers on the same FPGA chip using the computational resources required by only 1 PID core.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".