Quantized Data Driven Model-Free Adaptive Predictive Control for a Class of Nonlinear Systems

被引：3
|
作者：
Liu, Genfeng [1 ]
Hou, Zhongsheng [2 ]
机构：
[1] Henan Univ Technol, Coll Elect Engn, Zhengzhou 450001, Peoples R China
[2] Qingdao Univ, Sch Automat, Qingdao 266071, Peoples R China
来源：
2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS | 2023年
关键词：
Quantizer; encoding-decoding; model-free adaptive predictive control; MFAC; data driven control; DESIGN;
D O I：
10.1109/DDCLS58216.2023.10165938
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a quantized data driven model-free adaptive predictive control algorithm is presented for a class of nonlinear systems to deal with the limited network transmission capacity. Firstly, a uniform quantizer is designed to handle the network bandwidth limitation and an encoding-decoding mechanism is introduced to reduce the effects of quantization errors. Secondly, based on compact form dynamic linearization method, by only utilizing the input and output data of the control system, a quantized data driven control scheme is designed. Finally, a simulation is given to show the effectiveness and robustness of the presented control algorithm.
引用
收藏
页码：1105 / 1110
页数：6
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