Learning-based Adaptive Control using Contraction Theory
Name
2103.02987v3.pdf
Description
Accepted version
Size
914.75 KB
Format
Adobe PDF
Checksum (MD5)
e119dc83e727e0669db101846c2c2c6c
Author(s)
Tsukamoto, Hiroyasu
Chung, Soon-Jo
Slotine, Jean-Jacques
Date Issued
December 14, 2021
Publisher
IEEE
Citation
H. Tsukamoto, S. -J. Chung and J. -J. Slotine, "Learning-based Adaptive Control using Contraction Theory," 2021 60th IEEE Conference on Decision and Control (CDC), Austin, TX, USA, 2021, pp. 2533-2538.
Version
Author's final manuscript
Abstract
Adaptive control is subject to stability and performance issues when a learned model is used to enhance its performance. This paper thus presents a deep learning-based adaptive control framework for nonlinear systems with multiplicatively-separable parametrization, called adaptive Neural Contraction Metric (aNCM). The aNCM approximates real-time optimization for computing a differential Lyapunov function and a corresponding stabilizing adaptive control law by using a Deep Neural Network (DNN). The use of DNNs permits real-time implementation of the control law and broad applicability to a variety of nonlinear systems with parametric and nonparametric uncertainties. We show using contraction theory that the aNCM ensures exponential boundedness of the distance between the target and controlled trajectories in the presence of parametric uncertainties of the model, learning errors caused by aNCM approximation, and external disturbances. Its superiority to the existing robust and adaptive control methods is demonstrated using a cart-pole balancing model.
Description
2021 60th IEEE Conference on Decision and Control (CDC), 14-17 December, Austin, TX, USA
MIT Department
Massachusetts Institute of Technology. Nonlinear Systems Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cdc45484.2021.9683435
