.png){kind=icon}
返回
A Probabilistically Quantized Learning Control Framework for Networked Linear Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2021.3085559.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article, we consider quantized learning control for linear networked systems with additive channel noise. Our objective is to achieve high tracking performance while reducing the communication burden on the communication network. To address this problem, we propose an integrated framework consisting of two modules: a probabilistic quantizer and a learning scheme. The employed probabilistic quantizer is developed by employing a Bernoulli distribution driven by the quantization errors. Three learning control schemes are studied, namely, a constant gain, a decreasing gain sequence satisfying certain conditions, and an optimal gain sequence that is recursively generated based on a performance index. We show that the control with a constant gain can only ensure the input error sequence to converge to a bounded sphere in a mean-square sense, where the radius of this sphere is proportional to the constant gain. On the contrary, we show that the control that employs any of the two proposed gain sequences drives the input error to zero in the mean-square sense. In addition, we show that the convergence rate associated with the constant gain is exponential, whereas the rate associated with the proposed gain sequences is not faster than a specific exponential trend. Illustrative simulations are provided to demonstrate the convergence rate properties and steady-state tracking performance associated with each gain, and their robustness against modeling uncertainties.
Keyword:
Quantization (signal)
Probabilistic logic
Convergence
Learning systems
Uncertainty
Linear systems
Additives
Channel noise
learning gain design
performance analysis
probabilistic quantizer
quantized learning control (QLC)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
Stability analysis of quantized iterative learning control systems using lifting representation
PREAI
分享收藏Synergy routing and dynamic spectrum allocation in multi‐hop cognitive radio networks.png)
IET Networks
IET NetworksIF0
PREAI
分享收藏Analysis and synthesis of networked control systems: A survey of recent advances and challengesISA TRANSACTIONS
ISA TRANSACTIONSIF6.5
PREAI
分享收藏MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic QuantizerIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容