.png){kind=icon}
返回
Finite-level uniformly quantized learning control with random data dropouts
.png){kind=icon}
PRE
AI
DOI：10.1002/rnc.6376.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 study, a quantized iterative learning control method with an encoding-decoding mechanism is investigated for networked control systems with constrained transmission bandwidths and random data dropouts at both the measurement and actuator sides. The intermittent update principle is used to address the problem of data asynchronism caused by two-sided data dropouts. Then, a new process concept is introduced for the convergence analysis. The input sequence is guaranteed to achieve asymptotic zero-error convergence to the unknown desired input for the given reference through an appropriate selection of scaling sequences. Furthermore, the quantization errors are shown bounded, where the upper bounds of the quantization level are precisely determined for selecting a finite-level uniform quantizer. To verify the proposed scheme, an example of a permanent magnet linear motor is simulated.
Keyword:
encoding and decoding mechanism
iterative learning control
random data dropouts
uniform quantizer
期刊
IF:
3.2
论文数:
7.0K
被引数:
1.4W
机构
引用论文
Stability analysis of quantized iterative learning control systems using lifting representation
PREAI
分享收藏A Probabilistically Quantized Learning Control Framework for Networked Linear Systems网络化线性系统的概率量化学习控制框架
PREAI
分享收藏Improving Boundary Level Calculation in Quantized Iterative Learning Control With Encoding and Decoding Mechanism.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容