MétaCan
Menu
Back to cohort
Record W3109987404 · doi:10.1080/00207179.2020.1853813

Observer-based backstepping control for nonlinear cyber-physical systems with incomplete measurements

2020· article· en· W3109987404 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Control · 2020
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaDepartment of Education of Liaoning Province
KeywordsBacksteppingControl theory (sociology)Nonlinear systemEstimatorObserver (physics)Bounded functionMathematicsLinear matrix inequalityCyber-physical systemNetwork packetState (computer science)Stability (learning theory)State observerMathematical optimizationComputer scienceControl (management)Adaptive controlAlgorithm

Abstract

fetched live from OpenAlex

This paper investigates the problem of observer-based control for cyber-physical systems (CPSs) with incomplete measurements. The CPSs are described as a class of nonlinear strict-feedback systems. When data transmission problems, such as information packet losing or transmission medium saturation, occur, the state variables become unavailable or distorted. To solve these problems, two-state estimators are constructed for different transmission cases, based on which two backstepping controllers are designed. The stability conditions of the state estimators and closed-loop system are derived by solving a linear matrix inequality (LMI). It is proved that the control methods can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded (UUB) in mean square. The effectiveness of the proposed methods is confirmed by a simulation example.

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 score0.716
Threshold uncertainty score0.441

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 head0.029
GPT teacher head0.244
Teacher spread0.215 · 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