Data transmission is a common characteristic of network control systems (NCSs) and multiagent systems (MASs), and it is also a critical issue due to the limited transmission bandwidth of networks. Aiming for saving the limited resource, it is desirable to quantize signals before data transmissions. As an earlier work, a logarithmic quantizer (Bu et al. 2015, 2017) was used to quantize the output measurements when designing the ILC law. Xu et al. (2017) have extended the results of Bu et al. (2015) by using quantized error information. In Yin et al. (2018); Jin (2018), the input signals are quantized before they are fed to the controlled plant. An ILC method is proposed in Shen and Xu (2017) employing a probabilistic quantizer. Both quantization and event-triggered transmission methods are considered in Xiong et al. (2016) for the learning consensus protocol of a discrete-time network. A quantized ILC method using finite-leveled sigma-delta quantization is proposed for MASs (Zhang and Li 2017). The authors in Gao et al. (2017) have discussed both the quantization and the multi-rate sampling effects on the iterative learning fault-tolerant control for networked processes. In Huo and Shen (2020), Huo and Shen (2019), the zero-convergence problem of ILC is addressed by incorporating a finite uniform quantizer with an encoding-decoding method. However, one can observe in these studies that they often adopt a P-type learning algorithm with fixed learning gain and the theoretical results are most limited to linear systems. From a practical viewpoint, the fixed learning gain may hinder wide applications of these ILC methods because it greatly reduces the adaptability in presence of uncertainties and exogenous disturbances.
Data-Driven ILC for Systems with Quantized Data
Intelligent Control & Learning Systems
Data-Driven Iterative Learning Control for Discrete-Time Systems ; Chapter : 7 ; 115-132
2022-11-16
18 pages
Article/Chapter (Book)
Electronic Resource
English
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