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• W2885414030 abstract "In this chapter, we aim at improving the critic learning criterion to cope with the event-based nonlinear (H_{infty }) state feedback control design. First of all, the (H_{infty }) control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal inter-sample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method." @default.
• W2885414030 created "2018-08-22" @default.
• W2885414030 creator A5024549341 @default.
• W2885414030 creator A5057898837 @default.
• W2885414030 date "2018-08-11" @default.
• W2885414030 modified "2023-09-23" @default.
• W2885414030 title "Event-Triggered Nonlinear $$H_{infty }$$ Control Design via an Improved Critic Learning Strategy" @default.
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• W2885414030 doi "https://doi.org/10.1007/978-981-13-1253-3_8" @default.
• W2885414030 hasPublicationYear "2018" @default.
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