Robustness improvement of optimal control in terms of RBFNN with empirical model reduction and transfer learning-Reference-Cited by-同舟云学术

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Robustness improvement of optimal control in terms of RBFNN with empirical model reduction and transfer learning

Published:2024-03-21 Issue: Volume: Page:1-15
ISSN:0020-7179
Container-title:International Journal of Control
language:en
Short-container-title:International Journal of Control

Author:

Zhao Anni¹,Toudeshki Arash¹,Ehsani Reza¹,Viers Joshua H.²,Sun Jian-Qiao¹

Affiliation:

1. Department of Mechanical Engineering, School of Engineering, University of California, Merced, CA, USA

2. Department of Civil & Environment Engineering, School of Engineering, University of California, Merced, CA, USA

Funder

NSF and USDA-NIFA

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207179.2024.2328687

Reference66 articles.

1. Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach

2. An adaptive domain decomposition method for the Hamilton–Jacobi–Bellman equation

3. An adaptive least-squares collocation radial basis function method for the HJB equation

4. Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs

5. Meshfree Methods: Progress Made after 20 Years

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