A Novel Automated Framework for Networked Control of High-DOF Robot Manipulators: A Case Study of IRB140
Bibliographic record
Abstract
This paper proposes an efficient networked control methodology for high degree-offreedom (DOF) robot manipulators, offering detailed yet simplified procedures suitable for arm-like industrial robots.The methodology aims to precisely capture the behavior of contemporary industrial robotic manipulators across varied and challenging environments, despite their high DOF and complex characteristics.The automated framework, rooted in the Newton-Euler formulation, is assessed using the ABB IRB140 robot manipulator.Notably, the paper introduces a three phases-based novel approach to robot networked control.The integration of the network into the closed-loop control system of the manipulator is presented in three sequential stages, outlining the key factors in choosing the appropriate network protocol and reducing the negative impacts of the network on the feedback control system.The analysis identifies PROFINET as an effective network choice for networked control systems (NCS) applications, especially advantageous for highly dynamic manipulators with complex models.Furthermore, an adaptive robust proportional derivative control law incorporating gravity compensation is introduced, accompanied by a mathematical proof demonstrating the global asymptotic stability for position control.An extensive simulation process conducted using TrueTime toolbox integrated into Matlab validates the asymptotic stability, proving promising performances in high-DOF robot manipulators networked control.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".