Control of dynamical systems with neural networks

• URL: http://arxiv.org/abs/2510.12810v1
• Date: Mon, 06 Oct 2025 19:33:00 GMT
• Title: Control of dynamical systems with neural networks
• Authors: Lucas Böttcher,
• Abstract summary: Recent advances in deep learning and automatic differentiation have made applying these methods to control problems increasingly practical.<n>We show the use of neural networks and modern machine-learning libraries to parameterize control inputs across discrete-time and continuous-time systems.<n>We highlight applications in multiple domains, including biology, engineering, physics, and medicine.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Control problems frequently arise in scientific and industrial applications, where the objective is to steer a dynamical system from an initial state to a desired target state. Recent advances in deep learning and automatic differentiation have made applying these methods to control problems increasingly practical. In this paper, we examine the use of neural networks and modern machine-learning libraries to parameterize control inputs across discrete-time and continuous-time systems, as well as deterministic and stochastic dynamics. We highlight applications in multiple domains, including biology, engineering, physics, and medicine. For continuous-time dynamical systems, neural ordinary differential equations (neural ODEs) offer a useful approach to parameterizing control inputs. For discrete-time systems, we show how custom control-input parameterizations can be implemented and optimized using automatic-differentiation methods. Overall, the methods presented provide practical solutions for control tasks that are computationally demanding or analytically intractable, making them valuable for complex real-world applications.

Related papers

• Controlling dynamical systems into unseen target states using machine learning [45.84205238554709]
  We present a model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states.<n>Our approach accurately predicts system behavior in unobserved parameter regimes, enabling control over transitions to arbitrary target states.
  arXiv  Detail & Related papers  (2024-12-13T16:21:56Z)
• Learning System Dynamics without Forgetting [60.08612207170659]
  We investigate the problem of Continual Dynamics Learning (CDL), examining task configurations and evaluating the applicability of existing techniques.<n>We propose the Mode-switching Graph ODE (MS-GODE) model, which integrates the strengths LG-ODE and sub-network learning with a mode-switching module.<n>We construct a novel benchmark of biological dynamic systems for CDL, Bio-CDL, featuring diverse systems with disparate dynamics.
  arXiv  Detail & Related papers  (2024-06-30T14:55:18Z)
• Neural Control: Concurrent System Identification and Control Learning with Neural ODE [13.727727205587804]
  We propose a neural ODE based method for controlling unknown dynamical systems, denoted as Neural Control (NC) Our model concurrently learns system dynamics as well as optimal controls that guides towards target states. Our experiments demonstrate the effectiveness of our model for learning optimal control of unknown dynamical systems.
  arXiv  Detail & Related papers  (2024-01-03T17:05:17Z)
• Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computing [68.8204255655161]
  Controlling nonlinear dynamical systems using machine learning allows to drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. We show first that classical reservoir computing excels at this task. In a next step, we compare those results based on different amounts of training data to an alternative setup, where next-generation reservoir computing is used instead. It turns out that while delivering comparable performance for usual amounts of training data, next-generation RC significantly outperforms in situations where only very limited data is available.
  arXiv  Detail & Related papers  (2023-07-14T07:05:17Z)
• AI Enhanced Control Engineering Methods [66.08455276899578]
  We explore how AI tools can be useful in control applications. Two immediate applications are linearization of system dynamics for local stability analysis or for state estimation using Kalman filters. In addition, we explore the use of machine learning models for global parameterizations of state vectors and control inputs in model predictive control applications.
  arXiv  Detail & Related papers  (2023-06-08T20:31:14Z)
• Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control [22.549914935697366]
  We present a method to incorporate priori knowledge into data-driven control algorithms using kernel embeddings. Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem. We demonstrate the improved sample efficiency and out-of-sample generalization of our approach over a purely data-driven baseline.
  arXiv  Detail & Related papers  (2023-01-09T18:35:32Z)
• Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations [0.0]
  Real-world robotics systems often present challenges that limit the applicability of model-based Reinforcement Learning and Control. We propose a general framework that overcomes those difficulties by meta-learning adaptive dynamics models for continuous-time prediction and control. We present evaluations in two different robot simulations and on a real industrial robot.
  arXiv  Detail & Related papers  (2022-07-25T11:07:29Z)
• Near-optimal control of dynamical systems with neural ordinary differential equations [0.0]
  Recent advances in deep learning and neural network-based optimization have contributed to the development of methods that can help solve control problems involving high-dimensional dynamical systems. We first analyze how truncated and non-truncated backpropagation through time affect runtime performance and the ability of neural networks to learn optimal control functions.
  arXiv  Detail & Related papers  (2022-06-22T14:11:11Z)
• Controlling nonlinear dynamical systems into arbitrary states using machine learning [77.34726150561087]
  We propose a novel and fully data driven control scheme which relies on machine learning (ML) Exploiting recently developed ML-based prediction capabilities of complex systems, we demonstrate that nonlinear systems can be forced to stay in arbitrary dynamical target states coming from any initial state. Having this highly flexible control scheme with little demands on the amount of required data on hand, we briefly discuss possible applications that range from engineering to medicine.
  arXiv  Detail & Related papers  (2021-02-23T16:58:26Z)
• Physical deep learning based on optimal control of dynamical systems [0.0]
  In this study, we perform pattern recognition based on the optimal control of continuous-time dynamical systems. As a key example, we apply the dynamics-based recognition approach to an optoelectronic delay system. This is in contrast to conventional multilayer neural networks, which require a large number of weight parameters to be trained.
  arXiv  Detail & Related papers  (2020-12-16T06:38:01Z)
• Learning to Control PDEs with Differentiable Physics [102.36050646250871]
  We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames. We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs.
  arXiv  Detail & Related papers  (2020-01-21T11:58:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.