This paper focuses on the design of event-triggered controllers for the synchronization of delayed Takagi-Sugeno(T-S)fuzzy neural networks(NNs)under deception attacks.The traditional event-triggered mechanism(ETM)dete...This paper focuses on the design of event-triggered controllers for the synchronization of delayed Takagi-Sugeno(T-S)fuzzy neural networks(NNs)under deception attacks.The traditional event-triggered mechanism(ETM)determines the next trigger based on the current sample,resulting in network congestion.Furthermore,such methods suffer from the issues of deception attacks and unmeasurable system states.To enhance the system stability,we adaptively detect the occurrence of events over a period of time.In addition,deception attacks are recharacterized to describe general scenarios.Specifically,the following enhancements are implemented:First,we use a Bernoulli process to model the occurrence of deception attacks,which can describe a variety of attack scenarios as a type of general Markov process.Second,we introduce a sum-based dynamic discrete event-triggered mechanism(SDDETM),which uses a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events.Finally,we incorporate a dynamic output feedback controller(DOFC)to ensure the system stability.The concurrent design of the DOFC and SDDETM parameters is achieved through the application of the cone complement linearization(CCL)algorithm.We further perform two simulation examples to validate the effectiveness of the algorithm.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.T2121002,62473321,62403014,and 62233001)。
文摘This paper focuses on the design of event-triggered controllers for the synchronization of delayed Takagi-Sugeno(T-S)fuzzy neural networks(NNs)under deception attacks.The traditional event-triggered mechanism(ETM)determines the next trigger based on the current sample,resulting in network congestion.Furthermore,such methods suffer from the issues of deception attacks and unmeasurable system states.To enhance the system stability,we adaptively detect the occurrence of events over a period of time.In addition,deception attacks are recharacterized to describe general scenarios.Specifically,the following enhancements are implemented:First,we use a Bernoulli process to model the occurrence of deception attacks,which can describe a variety of attack scenarios as a type of general Markov process.Second,we introduce a sum-based dynamic discrete event-triggered mechanism(SDDETM),which uses a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events.Finally,we incorporate a dynamic output feedback controller(DOFC)to ensure the system stability.The concurrent design of the DOFC and SDDETM parameters is achieved through the application of the cone complement linearization(CCL)algorithm.We further perform two simulation examples to validate the effectiveness of the algorithm.
