Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems
This addresses the need for improved control strategies in dynamical systems, offering a novel method that is not explicitly incremental but focuses on a specific domain.
The paper tackled the problem of designing neural network-based controllers for dynamical systems by introducing Hamiltonian-Informed Optimal Neural (Hion) controllers, which use Pontryagin's Maximum Principle for state estimation and optimal control, resulting in superior optimality and tracking capabilities compared to established model-predictive controllers.
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.