Learning Tracking Control Over Unknown Fading Channels Without System Information

A novel data-driven learning control scheme is proposed for unknown systems with unknown fading sensor channels. The fading randomness is modeled by multiplicative and additive random variables subject to certain unknown distributions. In this scheme, we propose an error transmission mode and an ite...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2021-06, Vol.32 (6), p.2721-2732
Main Authors: Shen, Dong, Yu, Xinghuo
Format: Article
Language:English
Subjects:
Channels
Control systems
Convergence
Covariance matrices
Estimation
Fading
Fading channels
Gradient estimation
Information systems
iterative learning control
Iterative methods
Kalman filters
Learning
Random variables
stochastic approximation
Tracking control
Tracking errors
unknown fading channel
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A novel data-driven learning control scheme is proposed for unknown systems with unknown fading sensor channels. The fading randomness is modeled by multiplicative and additive random variables subject to certain unknown distributions. In this scheme, we propose an error transmission mode and an iterative gradient estimation method. Unlike the conventional transmission mode where the output is directly transmitted back to the controller, in the error transmission mode, we send the desired reference to the plant such that tracking errors can be calculated locally and then transmitted back through the fading channel. Using the faded tracking error data only, the gradient for updating input is iteratively estimated by a random difference technique along the iteration axis. This gradient acts as the updating term of the control signal; therefore, information on the system and the fading channel is no longer required. The proposed scheme is proved effective in tracking the desired reference under random fading communication environments. Theoretical results are verified by simulations.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2020.3007765