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Record W2305990993 · doi:10.1109/icecs.2015.7440344

Implementation of multiple PID controllers on FPGA

2015· article· en· W2305990993 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsYork University
Fundersnot available
KeywordsPID controllerField-programmable gate arrayComputer scienceControl theory (sociology)Control (management)Control engineeringEmbedded systemTemperature controlEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.689
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.304
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations8
Published2015
Admission routes1
Has abstractyes

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