mlsanityData to 14 Oct 2026

Identification For Control Based on Neural Networks: Approximately Linearizable Models

arXiv 2409.15858 ↗First posted 24 Sep 2024eess.SY cs.AI cs.SYcited by 1

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Abstract (arXiv)

This work presents a control-oriented identification scheme for efficient control design and stability analysis of nonlinear systems. Neural networks are used to identify a discrete-time nonlinear state-space model to approximate time-domain input-output behavior of a nonlinear system. The network is constructed such that the identified model is approximately linearizable by feedback, ensuring that the control law trivially follows from the learning stage. After the identification and quasi-linearization procedures, linear control theory comes at hand to design robust controllers and study stability of the closed-loop system. The effectiveness and interest of the methodology are illustrated throughout the paper on popular benchmarks for system identification.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 24 Sep 2024.

Builds on

12 of 30 references.

References · 12

  1. 2017Deep learning for universal linear embeddings of nonlinear dynamicscited by 494
  2. 2019Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programmingcited by 101
  3. 2019Nonlinear System Identification: A User-Oriented Roadmapcited by 57
  4. 2020Stability Analysis using Quadratic Constraints for Systems with Neural Network Controllerscited by 44
  5. 2016Linear identification of nonlinear systems: A lifting technique based on the Koopman operatorcited by 19
  6. 2020Certifying Incremental Quadratic Constraints for Neural Networks via Convex Optimizationcited by 18
  7. 2019Model structures and fitting criteria for system identification with neural networkscited by 16
  8. 2022Neural network training under semidefinite constraintscited by 11
  9. 2021Improved Initialization of State-Space Artificial Neural Networkscited by 8
  10. 2022Deep-Learning-Based Identification of LPV Models for Nonlinear Systemscited by 5
  11. 2022Data-Driven Feedback Linearization using the Koopman Generatorcited by 4
  12. 2018Improved Initialization for Nonlinear State-Space Modelingcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangiancited by 0

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  1. 2025Stable-by-Design Neural Network-Based LPV State-Space Models for System Identification
  2. 2022Neural Ordinary Differential Equations for Nonlinear System Identification
  3. 2022Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach
  4. 2026Learning Control-Affine Reduced-Order Models via Autoencoders
  5. 2026Model-Free Neural Filtering: A Comparison with Classical Filters in Nonlinear Systems
  6. 2024An L-BFGS-B approach for linear and nonlinear system identification under ℓ1 and group-Lasso regularization
  7. 2026Stabilization Learning: A Paradigm Transition Bridging Control Theory and Machine Learning ↗
  8. 2023Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks