Event-Based Adaptive NN Tracking Control of Nonlinear Discrete-Time Systems

被引：58
作者：
Li, Yuan-Xin [1 ,2 ]
Yang, Guang-Hong [1 ,3 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
[2] Liaoning Univ Technol, Dept Math, Jinzhou 121001, Peoples R China
[3] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2018年 / 29卷 / 09期
关键词：
Adaptive tracking control; backstepping; event-triggered control; neural networks (NNs); nonlinear discrete-time systems (NDTSs); UNKNOWN CONTROL DIRECTIONS; NEURAL-NETWORK CONTROL; FEEDBACK SYSTEMS; UNIFIED APPROACH; SCHEME; INPUT;
D O I：
10.1109/TNNLS.2017.2765683
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is concerned with the simultaneous design of a neural network (NN)-based adaptive control law and an event-triggering condition for a class of strict feedback nonlinear discrete-time systems. The stability and tracking performance of the closed-loop network control system under the event-triggering strategy is formally proven based on the Lyapunov theory in a hybrid framework. The proposed Lyapunov formulation yields an event-triggered algorithm to update the control input and NN weights based on conditions involving the closed-loop state. Different from the existing traditional NN control schemes where the feedback signals are transmitted and executed periodically, the feedback signals are transmitted and executed only when the event-trigger error exceeds the specified threshold, which can largely reduce the communication load. The effectiveness of the approach is evaluated through a simulation example.
引用
收藏
页码：4359 / 4369
页数：11
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