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Observer-Based Optimal Backstepping Security Control for Nonlinear Systems Using Reinforcement Learning Strategy
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DOI：10.1109/TCYB.2024.3443522.png)
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摘要
En 中文
This article considers an observer-based optimal backstepping security control for nonlinear systems using reinforcement learning (RL) strategy. The main challenge faced is the design of optimal contoller under the deception attacks. Therefore, this article introduces an improved security RL algorithm based on neural network technology under the design framework of critic-actor to resist attacks and optimize the entire system. Second, compared with some existing results, how to relax the general assumption about deception attack is also a difficult research topic. In this article, an unusual observer that uses the attacked system output is designed to estimate the real unavailable states caused by deception attacks, so that the impact of deception attacks is eliminated and the output feedback control is also achieved. By selecting the virtual controllers and the real controller as corresponding optimized controllers within the framework of the RL algorithm, the control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded. Finally, two simulation experiments will be run to demonstrate the effectiveness of the strategy.
Keyword:
Nonlinear systems
Security
Backstepping
Optimal control
Control systems
Vectors
Observers
Deception attacks
improved state observer
nonlinear systems
optimized backstepping (OB)
reinforcement learning (RL)
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
A survey on security control and attack detection for industrial cyber-physical systems工业网络物理系统安全控制与攻击检测综述.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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