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DESIGN OF INTEGRAL SLIDING MODE OBSERVERS FOR STATE, FAULT AND UNKNOWN INPUT RECONSTRUCTION

2012· article· en· W2034969673 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueControl and Intelligent Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)NoveltyNonlinear systemObserver (physics)Fault (geology)State (computer science)Mode (computer interface)Computer scienceState observerIntegral sliding modeSliding mode controlClass (philosophy)Control (management)MathematicsControl engineeringArtificial intelligenceEngineeringAlgorithm

Abstract

fetched live from OpenAlex

This paper proposes a new technique for fault diagnosis and estimation of states and unknown inputs in a class of nonlinear systems. The novelty of this approach lies in the design of two nonlinear observers which incorporate a combination of sliding and integral control actions. The observers are networked together for online information interchange. The first observer is used for fault diagnosis, and the second is used for the unknown inputs. It is shown that under certain conditions, the proposed observer is able to reduce chattering without compromising on estimation accuracy. Another significant advantage of the proposed approach is that the network of two interconnected integral sliding mode observers permits the relaxation of the fault isolability from the unknown inputs (in an appropriate sense), which has been a major problem previously.

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 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: none
Teacher disagreement score0.779
Threshold uncertainty score0.636

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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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