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Record W2964359134 · doi:10.11159/eee19.109

Fuzzy Sliding Mode Control For Inverted Pendulum With Random Disturbance

2019· article· en· W2964359134 on OpenAlexvenueno aff
Jieke Lin, Guanglin Shi, Shu Tang

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2019
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsnot available
Fundersnot available
KeywordsInverted pendulumDisturbance (geology)Control theory (sociology)Sliding mode controlMode (computer interface)Double inverted pendulumComputer scienceFuzzy control systemControl (management)Fuzzy logicPhysicsArtificial intelligenceGeologyNonlinear system

Abstract

fetched live from OpenAlex

In this paper, a novel inverted pendulum with a motor-driven random disturbance rod at the top is presented, which is different from the normal inverted pendulum. The simplified mathematic model of the control system is established on the basis of dynamic equations. The traditional PID control method is adopted and proved invalid for this system. Then Sliding Mode Control is used for the system due to its strong anti-interference ability. To weakening the chattering which is common in Sliding Mode Control, Fuzzy Sliding Mode Control is developed using fuzzy inference to adjust the thickness of the boundary layer in real time. A simulation of the control system is produced by combing the software MATLAB/Simulink and Adams, which can real-time display the movement of the system during simulation. The theoretical analysis and simulation results verify the rationality of the proposed control method for the inverted pendulum with random disturbance.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.536

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)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.185
Teacher spread0.181 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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