In this article,we study the secure control of the Markovian jumping neural networks(MJNNs)subject to deception attacks.Considering the limitation of the network bandwidth and the impact of the deception attacks,we pr...In this article,we study the secure control of the Markovian jumping neural networks(MJNNs)subject to deception attacks.Considering the limitation of the network bandwidth and the impact of the deception attacks,we propose two memory-based adaptive event-trigger mechanisms(AETMs).Different from the available event-trigger mechanisms,these two memory-based AETMs contain the historical triggered data not only in the triggering conditions,but also in the adaptive law.They can adjust the data transmission rate adaptively so as to alleviate the impact of deception attacks on the controlled system and to suppress the peak of the system response.In view of the proposed memory-based AETMs,a time-dependent Lyapunov functional is constructed to analyze the stability of the error system.Some sufficient conditions to ensure the asymptotical synchronization of master-slave MJNNs are obtained,and two easy-to-implement co-design algorithms for the feedback gain matrix and the trigger matrix are given.Finally,a numerical example is given to verify the feasibility and superiority of the two memory-based AETMs.展开更多
The problem of flapping motion control of Micro Air Vehicles (MAVs) with flapping wings was studied in this paper.Based upon the knowledge of skeletal and muscular components of hummingbird, a dynamic model for flappi...The problem of flapping motion control of Micro Air Vehicles (MAVs) with flapping wings was studied in this paper.Based upon the knowledge of skeletal and muscular components of hummingbird, a dynamic model for flapping wing wasdeveloped.A control scheme inspired by human memory and learning concept was constructed for wing motion control ofMAVs.The salient feature of the proposed control lies in its capabilities to improve the control performance by learning fromexperience and observation on its current and past behaviors, without the need for system dynamic information.Furthermore,the overall control scheme has a fairly simple structure and demands little online computations, making it attractive for real-timeimplementation on MAVs.Both theoretical analysis and computer simulation confirms its effectiveness.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.61973199,62003794,and 62173214)the Shandong Provincial Natural Science Foundation (Grant Nos.ZR2020QF050 and ZR2021MF003)the Taishan Scholar Project of Shandong Province of China。
文摘In this article,we study the secure control of the Markovian jumping neural networks(MJNNs)subject to deception attacks.Considering the limitation of the network bandwidth and the impact of the deception attacks,we propose two memory-based adaptive event-trigger mechanisms(AETMs).Different from the available event-trigger mechanisms,these two memory-based AETMs contain the historical triggered data not only in the triggering conditions,but also in the adaptive law.They can adjust the data transmission rate adaptively so as to alleviate the impact of deception attacks on the controlled system and to suppress the peak of the system response.In view of the proposed memory-based AETMs,a time-dependent Lyapunov functional is constructed to analyze the stability of the error system.Some sufficient conditions to ensure the asymptotical synchronization of master-slave MJNNs are obtained,and two easy-to-implement co-design algorithms for the feedback gain matrix and the trigger matrix are given.Finally,a numerical example is given to verify the feasibility and superiority of the two memory-based AETMs.
文摘The problem of flapping motion control of Micro Air Vehicles (MAVs) with flapping wings was studied in this paper.Based upon the knowledge of skeletal and muscular components of hummingbird, a dynamic model for flapping wing wasdeveloped.A control scheme inspired by human memory and learning concept was constructed for wing motion control ofMAVs.The salient feature of the proposed control lies in its capabilities to improve the control performance by learning fromexperience and observation on its current and past behaviors, without the need for system dynamic information.Furthermore,the overall control scheme has a fairly simple structure and demands little online computations, making it attractive for real-timeimplementation on MAVs.Both theoretical analysis and computer simulation confirms its effectiveness.
