Optimization of controller gains for FPGA-based multivariable motion controller using response surface methodology
Bibliographic record
Abstract
Field Programmable Gate Arrays (FPGA) have become increasingly popular in recent years for control applications. Using contemporary FPGA technology, a powerful virtual processor can be synthesized and integrated with custom hardware to create a dedicated controller that outperforms conventional microcontroller and microprocessor based designs. The FPGA based controller takes advantage of both hardware features and virtual processor technology. This study details the development of a cascaded type PD controller for an inverted pendulum system implemented on a single FPGA device. The controller includes a hardware based implementation of the IO modules including quadrature decoders/counters and a Pulse Width Modulation (PWM) controller for the motor driver. The NIOS II processor was synthesized to implement the cascaded PID controller algorithm. This study also proposes a novel method for obtaining the optimal controller gains for the system. It uses the Central Composite Design (CCD) in Response Surface Methodology (RSM) for obtaining these gains. A classic inverted pendulum system was selected to demonstrate the applicability of the proposed approach. The gains provided by the RSM were verified experimentally to validate the proposed controller tuning method.
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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 | 0.001 |
| 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) | 0.002 | 0.000 |
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.
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".