Model-Free Adaptive Predictive Quantitative Control for Nonlinear Systems Subject to False Data Injection Attacks

In this paper, a model-free adaptive predictive quantitative control algorithm is presented for unknown nonlinear systems to handle the limited network transmission capacity and false data injection attacks. Firstly, a uniform quantizer with encoding-decoding mechanism is designed to deal with the n...

Full description

Saved in:
Bibliographic Details
Published inAsian Control Conference (Online) pp. 503 - 508
Main Authors Liu, Genfeng, Wang, Yangyang, Hou, Zhongsheng
Format Conference Proceeding
LanguageEnglish
Published Asian Control Association 05.07.2024
Subjects
Adaptive systems
Bandwidth
Control systems
data-driven control
false data injection attacks
MFA C
model-free adaptive predictive control
Prediction algorithms
Predictive models
Quantization (signal)
Simulation
Online AccessGet full text
ISSN2770-8373

Cover

More Information
Summary:In this paper, a model-free adaptive predictive quantitative control algorithm is presented for unknown nonlinear systems to handle the limited network transmission capacity and false data injection attacks. Firstly, a uniform quantizer with encoding-decoding mechanism is designed to deal with the network bandwidth limitation and to reduce the effects of quantization errors. Secondly, an attack compensation mechanism is conducted to reduce the impact of the false data injection attacks. Finally, simulation results show the effectiveness and robustness of the proposed control algorithm.
ISSN:2770-8373