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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".