FPGA based real-time adaptive fuzzy logic controller
Bibliographic record
Abstract
Fuzzy logic based control systems provide a simple and efficient method to control highly complex and imprecise systems. However, the lack of a simple hardware design that is capable of modifying the fuzzy controller's parameters to adapt for any changes in the operation environment, or behavior of the plant system limits the applicability of fuzzy based control systems in the automotive and industrial environments. The design and implementation of an FPGA based fuzzy logic controller, that allows real-time modification of its membership functions and rule base is introduced in this paper. The development of the controller's architecture is carried out on a National Instruments Intelligent DAQ board (PCI-7833R) with a reconfigurable Xilinx Virtex-II FPGA. The proposed design combines the performance advantages of existing static FPGA based fuzzy control architectures, with the flexibility and ease of implementation of conventional micro-controllers and general purpose processors. To test the efficiency of the controller and its ability to stabilize a highly dynamic system, a semi-active suspension system was developed. Simulation results for the proposed FPGA controller showed a 56% characteristic enhancement over the standard passive suspension system.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".