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Record W2969358681 · doi:10.3390/inventions4030049

A Tutorial on Robust Control, Adaptive Control and Robust Adaptive Control—Application to Robotic Manipulators

2019· article· en· W2969358681 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInventions · 2019
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsYork University
Fundersnot available
KeywordsAdaptive controlControl engineeringRobust controlRoboticsComputer scienceControl (management)Robot manipulatorControl theory (sociology)Artificial intelligenceControl systemRobotEngineering

Abstract

fetched live from OpenAlex

A tutorial on robust control, adaptive control, robust adaptive control and adaptive control of robotic manipulators is presented in a systematic manner. Some limitations of the above methods are also illustrated. The relationships between the robust control, adaptive control and robust adaptive control are demonstrated. Basic information on the joint space control, operational space control and force control is also given. This tutorial summarizes the most advanced control techniques currently in use in a very simple manner, and applies to robotic manipulators, which can provide an informative guideline for students who have little knowledge of controls or who want to understand the adaptive control of robotics in a systematic way.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.023
GPT teacher head0.219
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it