FPGA Implementation of Neural Network Based Adaptive Control of a Flexible Joint with Hard Nonlinearities
Bibliographic record
Abstract
An artificial neural network (ANN) based model reference adaptive controller has been developed for a positioning system with a flexible transmission element, taking into account hard nonlinearities in the motor and load models. Due to the presence of Coulomb friction and of the flexible coupling, the inverse model of the system is not realizable. The ability of ANNs to approximate nonlinear functions is exploited to obtain an approximate inverse model for the positioning system and a reference model is used to define the desired error dynamics. The controller uses desired load position and velocity trajectories with measurement of load position, load velocity and motor velocity. The paper describes a VLSI implementation of the controller on a Virtex2 Pro 2VP30 field programmable gate array (FPGA) from Xilinx. A pipelined adaptation of the on-line back-propagation algorithm is used. The hardware implementation is capable of a high degree of parallelism and pipelining of neural networks allows the controller to operate at even higher speed. The FPGA implementation on the other hand allows fast prototyping and rapid system deployment. The controller can be used to improve both static and dynamic performance of electromechanical systems
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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 | 0.000 |
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".