QRnet: optimal regulator design with LQR-augmented neural networks
This addresses control problems in high-dimensional nonlinear systems, but it is incremental as it builds on existing neural network methods.
The paper tackles designing optimal regulators for high-dimensional nonlinear systems by augmenting linear quadratic regulators with neural networks, demonstrating improved robustness and accuracy for an unstable Burgers' equation.
In this paper we propose a new computational method for designing optimal regulators for high-dimensional nonlinear systems. The proposed approach leverages physics-informed machine learning to solve high-dimensional Hamilton-Jacobi-Bellman equations arising in optimal feedback control. Concretely, we augment linear quadratic regulators with neural networks to handle nonlinearities. We train the augmented models on data generated without discretizing the state space, enabling application to high-dimensional problems. We use the proposed method to design a candidate optimal regulator for an unstable Burgers' equation, and through this example, demonstrate improved robustness and accuracy compared to existing neural network formulations.