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Model Predictive Control of Asynchronously Switched Systems with Exogenous Disturbances

2023· article· en· W4380029556 on OpenAlex

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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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsControl theory (sociology)Model predictive controlAsynchronous communicationExponential stabilityComputer scienceDwell timeSet (abstract data type)Invariant (physics)Forcing (mathematics)Class (philosophy)Stability (learning theory)Terminal (telecommunication)State (computer science)Control (management)MathematicsAlgorithmArtificial intelligenceNonlinear system

Abstract

fetched live from OpenAlex

This paper investigates the model predictive control (MPC) problem for a class of asynchronously switched systems with external disturbances. To mitigate the negative impact of additive disturbances, we establish a disturbance mode-dependent dwell time (MDT) invariant (DMI) set which is competent to collect all possible disturbed behaviors in the presence of asynchronous switching. Based on the DMI set, we make use of the tube-based robust MPC (RMPC) methodology to tighten the original constraints. Then, by forcing the state trajectories into an objective zone, the recursive feasibility of the switched MPC design is ensured. Moreover, a terminal target set is designed such that closed-loop asymptotic stability is achieved by imposing this restriction on the reachable sets. A numerical example is provided to verify the theoretical findings.

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 score0.986
Threshold uncertainty score0.507

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 head0.007
GPT teacher head0.188
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

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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