Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/81937
Title: Adaptive neural network control of pure-feedback nonlinear discrete-time systems
Authors: Zhai, L.-F.
Chai, T.-Y.
Ge, S.-Z. 
Keywords: Adaptive control
Backstepping
Discrete systems
Neural networks
Nonlinear systems
Issue Date: Apr-2009
Source: Zhai, L.-F.,Chai, T.-Y.,Ge, S.-Z. (2009-04). Adaptive neural network control of pure-feedback nonlinear discrete-time systems. Kongzhi yu Juece/Control and Decision 24 (4) : 488-493+498. ScholarBank@NUS Repository.
Abstract: For a class of pure-feedback discrete-time nonlinear systems, adaptive neural network control based on backstepping design is proposed. To avoid the causality contradiction problem in backstepping design, the system is firstly transformed through a coordinate transformation. Then implicit function theorem is exploited to assert the existence of the desired virtual controls and practical control. By using high-order neural networks to approximate the desired controls, an effective adaptive neural network control system is developed by backstepping design. The closed-loop system is proved to be semi-globally uniformly ultimately bounded. Simulation result illustrates the effectiveness of the proposed control.
Source Title: Kongzhi yu Juece/Control and Decision
URI: http://scholarbank.nus.edu.sg/handle/10635/81937
ISSN: 10010920
Appears in Collections:Staff Publications

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