Adaptive Neural Event-Triggered Control for Discrete-Time Strict-Feedback Nonlinear Systems
作者： Min Wang , Zidong Wang , Yun Chen , Weiguo Sheng
DOI: 10.1109/TCYB.2019.2921733
关键词: Discrete time and continuous time 、 Adaptive system 、 Nonlinear system 、 Bounded function 、 Control theory 、 Computer science 、 Networked control system 、 Backstepping 、 Lyapunov stability 、 Artificial neural network
摘要: This paper proposes a novel event-triggered (ET) adaptive neural control scheme for class of discrete-time nonlinear systems in strict-feedback form. In the proposed scheme, ideal input is derived recursive design process, which relies on system states only and unrelated to virtual laws. this case, high-order networks (NNs) are used approximate (but not laws), then corresponding controller developed under ET mechanism. A modified NN weight updating law, nonperiodically tuned at triggering instants, designed guarantee uniformly ultimate boundedness (UUB) estimates all sampling times. virtue bounded dead-zone operator, condition together with an threshold coefficient constructed UUB closed-loop networked through Lyapunov stability theory, thereby largely easing network communication load. The easy implement because avoidance of: 1) use intermediate conditions backstepping procedure; 2) computation laws; 3) redundant events when converge desired region. validity presented demonstrated by simulation results.
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