Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles
Author:
Affiliation:
1. University of California,Center for Control, Dynamical Systems, and Computation,Santa Barbara
2. Mitsubishi Electric Research Laboratories
Funder
Mitsubishi Electric Research Laboratories
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10155646/10155787/10155954.pdf?arnumber=10155954
Reference29 articles.
1. Neural Lander: Stable Drone Landing Control Using Learned Dynamics
2. Learning dynamics from partial observations with structured neural ODEs;buisson-fenet,2022
3. Learning discrepancy models from experimental data;kaheman,2019
4. Data-efficient control policy search using residual dynamics learning
5. Constrained Physical-Statistics Models for Dynamical System Identification and Prediction;dona;The Tenth International Conference on Learning Representations,2021
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units *;2024 IEEE Conference on Control Technology and Applications (CCTA);2024-08-21
2. Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States;IEEE Control Systems Letters;2023
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