Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems
Not on the map. Why?
Abstract (arXiv)
This paper formalizes Hamiltonian-Informed Optimal Neural (Hion) controllers, a novel class of neural network-based controllers for dynamical systems and explicit non-linear model-predictive control. Hion controllers estimate future states and develop an optimal control strategy using Pontryagin's Maximum Principle. The proposed framework, along with our Taylored Multi-Faceted Approach for Neural ODE and Optimal Control (T-mano) architecture, allows for custom transient behavior, predictive control, and closed-loop feedback, addressing limitations of existing methods. Comparative analyses with established model-predictive controllers revealed Hion controllers' superior optimality and tracking capabilities. Optimal control strategies are also demonstrated for both linear and non-linear dynamical systems.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 2 Nov 2024.
Builds on
7 of 40 references.
References · 7
- 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 92
- 2018Learning an Approximate Model Predictive Controller with Guaranteescited by 57
- 2022Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platformscited by 41
- 2021Physics-Informed Neural Nets for Control of Dynamical Systemscited by 31
- 2021Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulatorscited by 21
- 2022Physics-informed neural networks for PDE-constrained optimization and controlcited by 19
- 2024Fast Physics-Informed Model Predictive Control Approximation for Lyapunov Stabilitycited by 1
Built on by
Not cited yet.