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Record W2734418031 · doi:10.23919/acc.2017.7963238

A terminal cost for economic model predictive control with local optimality

2017· article· en· W2734418031 on OpenAlex
Su Liu, Jinfeng Liu

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsControl theory (sociology)Lagrange multiplierTerminal (telecommunication)Optimal controlController (irrigation)Model predictive controlDuality (order theory)Mathematical optimizationBellman equationMathematicsComputer scienceControl (management)

Abstract

fetched live from OpenAlex

In this work, we design a terminal cost for economic model predictive control (EMPC) which preserves local optimality. We first show, based on the strong duality and second order sufficient condition (SOSC) of the steady-state optimization problem, that the optimal operation of the system is locally equivalent to an infinite-horizon LQR controller. The proposed terminal cost is constructed as the value function of the LQR controller plus a linear term characterized by the Lagrange multiplier associated with the steady state constraint. EMPC with the proposed terminal cost is stabilizing with an appropriately chosen control horizon, and preserves the local optimality of the LQR controller. Simulation results of an isothermal CSTR verify our analysis.

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: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.452

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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
Published2017
Admission routes1
Has abstractyes

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