mlsanityData to 7 Oct 2026

Deep Learning of Koopman Representation for Control

arXiv 2010.07546 ↗First posted 15 Oct 2020cs.LG cs.SY eess.SYcited by 25

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Abstract (arXiv)

We develop a data-driven, model-free approach for the optimal control of the dynamical system. The proposed approach relies on the Deep Neural Network (DNN) based learning of Koopman operator for the purpose of control. In particular, DNN is employed for the data-driven identification of basis function used in the linear lifting of nonlinear control system dynamics. The controller synthesis is purely data-driven and does not rely on a priori domain knowledge. The OpenAI Gym environment, employed for Reinforcement Learning-based control design, is used for data generation and learning of Koopman operator in control setting. The method is applied to two classic dynamical systems on OpenAI Gym environment to demonstrate the capability.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Oct 2020.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 4; 2024: 6; 2025: 6.

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  1. 2022Koopman Neural Forecaster for Time Series with Temporal Distribution Shiftscited by 35
  2. 2022Deep Koopman Operator with Control for Nonlinear Systemscited by 25
  3. 2022Unsupervised Learning of Equivariant Structure from Sequencescited by 15
  4. 2024VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecastingcited by 12
  5. 2023Efficient Dynamics Modeling in Interactive Environments with Koopman Theorycited by 10
  6. 2023Task-Oriented Koopman-Based Control with Contrastive Encodercited by 8
  7. 2024ActionDiffusion: An Action-aware Diffusion Model for Procedure Planning in Instructional Videoscited by 8
  8. 2024RoboKoop: Efficient Control Conditioned Representations from Visual Input in Robotics using Koopman Operatorcited by 4
  9. 2022Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Controlcited by 2
  10. 2022Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspacescited by 2
  11. 2022Physics-Informed Koopman Networkcited by 2
  12. 2021Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Predictioncited by 1
13 more
  1. 2023Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passingcited by 1
  2. 2023Optimal Control of Nonlinear Systems with Unknown Dynamicscited by 1
  3. 2024Automated Global Analysis of Experimental Dynamics through Low-Dimensional Linear Embeddingscited by 1
  4. 2025KIPPO: Koopman-Inspired Proximal Policy Optimizationcited by 1
  5. 2025MAKO: Meta-Adaptive Koopman Operators for Learning-based Model Predictive Control of Parametrically Uncertain Nonlinear Systemscited by 1
  6. 2025CHyLL: Learning Continuous Neural Representations of Hybrid Systemscited by 1
  7. 2021Supervised DKRC with Images for Offline System Identificationcited by 0
  8. 2022Learned Lifted Linearization Applied to Unstable Dynamic Systems Enabled by Koopman Direct Encodingcited by 0
  9. 2024Analysis of Truncated Singular Value Decomposition for Koopman Operator-Based Lane Change Modelcited by 0
  10. 2024Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observationscited by 0
  11. 2025Deep Neural Koopman Operator-based Economic Model Predictive Control of Shipboard Carbon Capture Systemcited by 0
  12. 2025KoopCast: Trajectory Forecasting via Koopman Operatorscited by 0
  13. 2025Deep Koopman Economic Model Predictive Control of a Pasteurisation Unitcited by 0