Paper Citation Record · LEDGER

Neural network based control of unknown nonlinear systems via contraction analysis

As of 18 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2505.16511.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.16511 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:05:36.617703Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy38
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a0d800c9-7be4-49d2-8e3c-657e1a0d5dcc · outbound

This paper cites Thi s property makes the single-hidden layer NN ( k = 1) commonly used for system representation in control theory [11], [22] , [29].

Neural network based control of unknown nonlinear systems via contraction analysis Thi s property makes the single-hidden layer NN ( k = 1) commonly used for system representation in control theory [11], [22] , [29]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:44.230166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e328dda-90de-49a9-b475-1142de5d0db0 · outbound

This paper cites Nonlinearity Ca n- cellation.

Neural network based control of unknown nonlinear systems via contraction analysis Nonlinearity Ca n- cellation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:43.952702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 83df9caf-18ab-4786-b26f-8e49b19c4c40 · outbound

This paper cites an unresolved cited work.

Neural network based control of unknown nonlinear systems via contraction analysis Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:05:43.700027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ae468470-25f1-4268-a86d-908b1e7ab5c9 · outbound

This paper cites an unresolved cited work.

Neural network based control of unknown nonlinear systems via contraction analysis Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:05:43.463886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12d26bdf-e286-44fe-ab0a-92c23c449309 · outbound

This paper cites The states x1, x2 are the angular position and velocity, respectively, u(x) is the applied torque.

Neural network based control of unknown nonlinear systems via contraction analysis The states x1, x2 are the angular position and velocity, respectively, u(x) is the applied torque

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:43.194225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d527766d-802b-4960-bc24-c33991a6a5f9 · outbound

This paper cites 2] Consider a wheeled vehicle path following system { ˙de = ν sin(θe) ˙θe = ω − νκ(s) cos(θe) 1−κ(s)de.

Neural network based control of unknown nonlinear systems via contraction analysis 2] Consider a wheeled vehicle path following system { ˙de = ν sin(θe) ˙θe = ω − νκ(s) cos(θe) 1−κ(s)de

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:43.026347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 361b7a1a-cea0-4eff-96b3-95cb53e51532 · outbound

This paper cites an unresolved cited work.

Neural network based control of unknown nonlinear systems via contraction analysis Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:05:42.755768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 625400fe-de1b-4e92-8067-a3b8485af0a3 · outbound

This paper cites Contraction methods for nonlinea r systems: A brief introduction and some open problems[C]//53rd IEEE Co nference on Decision and Control.

Neural network based control of unknown nonlinear systems via contraction analysis Contraction methods for nonlinea r systems: A brief introduction and some open problems[C]//53rd IEEE Co nference on Decision and Control

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:42.482025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d564daf5-b271-4c46-b023-3807fa6fb7fd · outbound

This paper cites A Lyapunov approach to incremental stability p roperties[J].

Neural network based control of unknown nonlinear systems via contraction analysis A Lyapunov approach to incremental stability p roperties[J]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:42.284667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bbcbf894-4466-4289-984f-13f1e01382d5 · outbound

This paper cites On contraction of time-v arying port- Hamiltonian systems[J].

Neural network based control of unknown nonlinear systems via contraction analysis On contraction of time-v arying port- Hamiltonian systems[J]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:42.096478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9844abd0-47fd-4086-be5d-c93682a4fdf8 · outbound

This paper cites Convex optimization[M].

Neural network based control of unknown nonlinear systems via contraction analysis Convex optimization[M]

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:41.889422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a0eea1f4-938e-4443-91d1-9acb70773d88 · outbound

This paper cites Modeling and contractivi ty of neural- synaptic networks with Hebbian learning[J].

Neural network based control of unknown nonlinear systems via contraction analysis Modeling and contractivi ty of neural- synaptic networks with Hebbian learning[J]

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:41.678673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a4978927-f691-4a3f-b75d-0dcce6bbc64e · outbound

This paper cites Euclidean cont ractivity of neural networks with symmetric weights[J].

Neural network based control of unknown nonlinear systems via contraction analysis Euclidean cont ractivity of neural networks with symmetric weights[J]

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:41.526220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5d7d06b1-6f89-40fd-9e5b-b2f5f0683201 · outbound

This paper cites Adaptive optimal control of unknown nonlinear systems via homotopy-based policy iteration[J].

Neural network based control of unknown nonlinear systems via contraction analysis Adaptive optimal control of unknown nonlinear systems via homotopy-based policy iteration[J]

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:41.304799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a48979b3-bb38-4d0c-9a0f-cadaba84d484 · outbound

This paper cites Neural ordina ry differential equations[J].

Neural network based control of unknown nonlinear systems via contraction analysis Neural ordina ry differential equations[J]

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:41.086965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f8569d2e-3a85-4b0e-a6d9-2c65be46a745 · outbound

This paper cites Non-linear system identi ﬁcation using neural networks[J].

Neural network based control of unknown nonlinear systems via contraction analysis Non-linear system identi ﬁcation using neural networks[J]

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:40.888023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4ed447a4-55ab-45d3-9885-ee60b2966d6e · outbound

This paper cites An incremental input-to -state stability condition for a class of recurrent neural network s[J].

Neural network based control of unknown nonlinear systems via contraction analysis An incremental input-to -state stability condition for a class of recurrent neural network s[J]

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:40.687407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5cce9ed3-567b-469d-b0e5-30006d6ac516 · outbound

This paper cites Non-Euclidean con traction analysis of continuous-time neural networks[J].

Neural network based control of unknown nonlinear systems via contraction analysis Non-Euclidean con traction analysis of continuous-time neural networks[J]

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:40.465908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 74fe1ae8-5ae9-456b-823d-2bdfe8ceaddc · outbound

This paper cites Learning controllers from data via ap- proximate nonlinearity cancellation[J].

Neural network based control of unknown nonlinear systems via contraction analysis Learning controllers from data via ap- proximate nonlinearity cancellation[J]

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:40.272663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1c9bb60f-c973-45e8-80b5-2f7089f23eda · outbound

This paper cites Cautious optimization via data informativity.

Neural network based control of unknown nonlinear systems via contraction analysis Cautious optimization via data informativity

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:05:36.768421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aedfec1d-93ef-4190-a5de-194d0486f366 · outbound

This paper cites Safety veriﬁcation and r obustness analysis of neural networks via quadratic constraints and s emideﬁnite programming[J].

Neural network based control of unknown nonlinear systems via contraction analysis Safety veriﬁcation and r obustness analysis of neural networks via quadratic constraints and s emideﬁnite programming[J]

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:40.106304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 36a94f9c-aaed-48cc-bdfc-b4e6c0ebb305 · outbound

This paper cites A differential Lyapunov framewor k for contraction analysis[J].

Neural network based control of unknown nonlinear systems via contraction analysis A differential Lyapunov framewor k for contraction analysis[J]

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:39.891291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 546c2e15-3da3-45cb-800b-88c14f9505e4 · outbound

This paper cites Approximate opti mal trajectory tracking with sparse bellman error extrapolation[J].

Neural network based control of unknown nonlinear systems via contraction analysis Approximate opti mal trajectory tracking with sparse bellman error extrapolation[J]

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:39.695436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f2a626b4-77bb-4f4b-9e41-52342a46fc11 · outbound

This paper cites Deep neural network -based approximate optimal tracking for unknown nonlinear system s[J].

Neural network based control of unknown nonlinear systems via contraction analysis Deep neural network -based approximate optimal tracking for unknown nonlinear system s[J]

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:39.519564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 95587e02-0022-4f99-ba31-9971736aaf1c · outbound

This paper cites Multilayer feedforwa rd networks are universal approximators[J].

Neural network based control of unknown nonlinear systems via contraction analysis Multilayer feedforwa rd networks are universal approximators[J]

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:39.329475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6d9279a8-2902-476e-906a-48ac7d471e7d · outbound

This paper cites Enforcing contraction via data.

Neural network based control of unknown nonlinear systems via contraction analysis Enforcing contraction via data

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:35.543955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87262d04-37df-4df9-840a-e7834fcd36fb · outbound

This paper cites A tutorial on incremental stabil ity analysis using contraction theory[J].

Neural network based control of unknown nonlinear systems via contraction analysis A tutorial on incremental stabil ity analysis using contraction theory[J]

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:39.169326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 61b75e34-3285-4f9b-bf66-caf4c95d8a58 · outbound

This paper cites Model-base d reinforce- ment learning for inﬁnite-horizon approximate optimal tra cking[J].

Neural network based control of unknown nonlinear systems via contraction analysis Model-base d reinforce- ment learning for inﬁnite-horizon approximate optimal tra cking[J]

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:39.048333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6020d2ae-482e-4fd2-ad0e-7abee6fc6722 · outbound

This paper cites Stable neural ode with lyapu nov-stable equilibrium points for defending against adversarial atta cks[J].

Neural network based control of unknown nonlinear systems via contraction analysis Stable neural ode with lyapu nov-stable equilibrium points for defending against adversarial atta cks[J]

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:38.977657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 29116473-e3db-4e60-b0ea-649e7b8f30c3 · outbound

This paper cites Nonlinear systems[M].

Neural network based control of unknown nonlinear systems via contraction analysis Nonlinear systems[M]

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:38.848014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43600c5c-8a51-4d13-95f2-caee0496de53 · outbound

This paper cites On contraction analysis for n on-linear systems[J].

Neural network based control of unknown nonlinear systems via contraction analysis On contraction analysis for n on-linear systems[J]

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:38.693723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f1fd8ec5-0c78-48ab-92aa-63681d0b9cf7 · outbound

This paper cites Learning nonlinear operators v ia DeepONet based on the universal approximation theorem of operators[ J].

Neural network based control of unknown nonlinear systems via contraction analysis Learning nonlinear operators v ia DeepONet based on the universal approximation theorem of operators[ J]

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:38.588991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b58ef9c0-c696-4939-9d4e-7649b14a28e6 · outbound

This paper cites Unco nstrained parametrization of dissipative and contracting neural ord inary differential equations[C]//2023 62nd IEEE Conference on Decision and Co ntrol (CDC).

Neural network based control of unknown nonlinear systems via contraction analysis Unco nstrained parametrization of dissipative and contracting neural ord inary differential equations[C]//2023 62nd IEEE Conference on Decision and Co ntrol (CDC)

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:38.487662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6e239bf3-1dbf-4535-8152-ab8e0c7f5180 · outbound

This paper cites Control barrier function -based quadratic programs introduce undesirable asymptotically stable equilib- ria[J].

Neural network based control of unknown nonlinear systems via contraction analysis Control barrier function -based quadratic programs introduce undesirable asymptotically stable equilib- ria[J]

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:38.336087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f1ce8014-f6cf-4876-8b4f-fde64c37d620 · outbound

This paper cites Stability analysis and con troller synthe- sis using single-hidden-layer relu neural networks[J].

Neural network based control of unknown nonlinear systems via contraction analysis Stability analysis and con troller synthe- sis using single-hidden-layer relu neural networks[J]

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:38.188386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 958f162e-6837-4acc-b722-8c321b948871 · outbound

This paper cites Koopman-ba sed feedback design with stability guarantees[J].

Neural network based control of unknown nonlinear systems via contraction analysis Koopman-ba sed feedback design with stability guarantees[J]

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:38.056879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 328a042d-3a60-4ada-a39c-0735088beed0 · outbound

This paper cites Learning certiﬁed control using cont raction metric[C]//conference on Robot Learning.

Neural network based control of unknown nonlinear systems via contraction analysis Learning certiﬁed control using cont raction metric[C]//conference on Robot Learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:37.936034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34285bd6-3b7d-4a7d-8835-05951d49e16f · outbound

This paper cites Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview[J].

Neural network based control of unknown nonlinear systems via contraction analysis Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview[J]

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:37.786779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b4fb53e1-fcc6-482b-9430-bc3e33c670b0 · outbound

This paper cites Reprojection met hods for Koopman-based modelling and prediction[C]//2023 62nd IEE E Confer- ence on Decision and Control (CDC).

Neural network based control of unknown nonlinear systems via contraction analysis Reprojection met hods for Koopman-based modelling and prediction[C]//2023 62nd IEE E Confer- ence on Decision and Control (CDC)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:37.670079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63927077-2e3a-4ff5-ad96-cec62e1247e0 · outbound

This paper cites Model-free veriﬁcation fo r neural network controlled systems[J].

Neural network based control of unknown nonlinear systems via contraction analysis Model-free veriﬁcation fo r neural network controlled systems[J]

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:37.526087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a6e6f6f5-5fc9-4d01-90d2-ed17e6110dc9 · outbound

This paper cites A new concept using LSTM Neural Networks for dyna mic system identiﬁcation[C]//2017 American control conferen ce (ACC).

Neural network based control of unknown nonlinear systems via contraction analysis A new concept using LSTM Neural Networks for dyna mic system identiﬁcation[C]//2017 American control conferen ce (ACC)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:37.376757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 18a6787c-73b0-4f2a-b568-bdae9d85bb84 · outbound

This paper cites Stability analysis using quadr atic constraints for systems with neural network controllers[J].

Neural network based control of unknown nonlinear systems via contraction analysis Stability analysis using quadr atic constraints for systems with neural network controllers[J]

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:37.149245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e74c3221-eabf-45b9-a3a2-6d8978e2b6ff · outbound

This paper cites Neural Lyapunov cont rol of unknown nonlinear systems with stability guarantees[J].

Neural network based control of unknown nonlinear systems via contraction analysis Neural Lyapunov cont rol of unknown nonlinear systems with stability guarantees[J]

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:05:36.919367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.