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Type: Journal article
Title: Adaptive neural dynamic surface control for nonstrict-feedback systems with output dead-zone
Author: Shi, X.
Lim, C.
Shi, P.
Xu, S.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(11):5200-5213
Publisher: IEEE Computational Intelligence Society
Issue Date: 2018
ISSN: 2162-237X
Statement of
Xiaocheng Shi , Cheng-Chew Lim, Peng Shi and Shengyuan Xu
Abstract: This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead-zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young’s inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an n-order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semi-global uniformly ultimately bounded and the tracking error never violates the output constraint.
Keywords: Adaptive systems; nonlinear systems; control systems; artificial neural networks; backstepping; adaptation models
Rights: © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030081890
DOI: 10.1109/TNNLS.2018.2793968
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Appears in Collections:Electrical and Electronic Engineering publications

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