Pith Number

pith:GZEZPV4H

pith:2026:GZEZPV4H6BPUPFRNTVPN7HRSI3

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Data-Driven Linear Quadratic Control Using Output-Feedback via Non-Minimal Realization

Bowen Yi, Hai Lin, Panos J. Antsaklis, Weijian Li

An augmented system from Kreisselmeier's adaptive filter recovers the optimal state-feedback gain for the original plant in data-driven LQ control.

arxiv:2605.16752 v1 · 2026-05-16 · math.OC

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with the canonical non-minimal realization, and hence achieves the optimal state-feedback solution of the original plant.

C2weakest assumption

The Kreisselmeier's adaptive filter admits an observer interpretation that leads to an augmented system preserving the input-output response of the realization and providing accessible state trajectories (abstract, paragraph describing the filter and augmented system).

C3one line summary

Presents a data-driven value iteration algorithm for output-feedback LQR that recovers the optimal state-feedback gain via a non-minimal realization constructed from Kreisselmeier's adaptive filter.

References

32 extracted · 32 resolved · 0 Pith anchors

[1] R. S. Sutton and A. G. Barto,Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA: MIT Press, 2018 2018

[2] Beyond regression: New tools for prediction and analysis in the behavioral sciences, 1974

[3] Deep reinforcement learning for autonomous driving: A survey, 2021

[4] Reinforcement learning in robotics: A survey, 2013

[5] Data-driven control based on the behavioral approach from theory to applications in power systems, 2023

Receipt and verification

First computed	2026-05-20T00:03:19.802768Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

364997d787f05f47962d9d5edf9e3246fb37793d5f344a231310fff12b2cd046

Aliases

arxiv: 2605.16752 · arxiv_version: 2605.16752v1 · doi: 10.48550/arxiv.2605.16752 · pith_short_12: GZEZPV4H6BPU · pith_short_16: GZEZPV4H6BPUPFRN · pith_short_8: GZEZPV4H

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GZEZPV4H6BPUPFRNTVPN7HRSI3 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 364997d787f05f47962d9d5edf9e3246fb37793d5f344a231310fff12b2cd046

Canonical record JSON

{
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    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "math.OC",
    "submitted_at": "2026-05-16T02:09:02Z",
    "title_canon_sha256": "a288d22f04da6f6be8558d1e04735cf954c5e8c949d84210c21eced33a1863b1"
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  "source": {
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    "kind": "arxiv",
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}