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Inverse optimal control for averaged cost per stage linear quadratic regulators

Overview of attention for article published in Systems & Control Letters, January 2024
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About this Attention Score

  • Among the highest-scoring outputs from this source (#12 of 179)
  • Good Attention Score compared to outputs of the same age (65th percentile)

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Title
Inverse optimal control for averaged cost per stage linear quadratic regulators
Published in
Systems & Control Letters, January 2024
DOI 10.1016/j.sysconle.2023.105658
Authors

Han Zhang, Axel Ringh

Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 November 2023.
All research outputs
#8,531,374
of 25,363,685 outputs
Outputs from Systems & Control Letters
#12
of 179 outputs
Outputs of similar age
#109,824
of 333,512 outputs
Outputs of similar age from Systems & Control Letters
#1
of 1 outputs
Altmetric has tracked 25,363,685 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 179 research outputs from this source. They receive a mean Attention Score of 2.1. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,512 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them