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DYNAMIC OUTPUT FEEDBACK SLIDING MODE CONTROL FOR UNCERTAIN SYSTEMS WITH STATE AND INPUT DELAYS

2008· article· en· W2076388052 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 · 2008
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)Observer (physics)State observerSliding mode controlLinear matrix inequalityLyapunov functionState (computer science)Computer scienceStability (learning theory)Transformation (genetics)Mode (computer interface)Control (management)MathematicsMathematical optimizationNonlinear systemAlgorithm

Abstract

fetched live from OpenAlex

This paper presents a methodological approach to design dynamic output feedback sliding mode control for a class of mismatched uncertain systems with state and input delays. A new switching surface is proposed and a state observer is employed to reconstruct the sliding mode control action. Based on the improved Lyapunov function and linear matrix inequality (LMI) technique, the stability of the overall closed-loop uncertain time-delay system under the proposed control scheme is guaranteed without the state transformation. Furthermore, the state observer and control law can be constructed from the positive-definite solutions of two LMIs, the design technique is simple and computationally efficient. The validity of the proposed control methodology is demonstrated by simulation results.

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)0.001
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 score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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