Neural Network-Based Adaptive Dynamic Structure Control for a Class of Uncertain Nonlinear Systems in Strict-Feedback Form

In this paper, by incorporating this dynamic structure control technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty. Our development is able t...

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Bibliographic Details
Published in2015 8th International Symposium on Computational Intelligence and Design (ISCID) Vol. 1; pp. 521 - 524
Main Authors Lin Niu, Shengyun Zhou, Hongling Xie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Adaptation models
Adaptive control
Artificial neural networks
Control systems
neural network
nonlinear system
Nonlinear systems
predictive control
strict-feedback system
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Summary:In this paper, by incorporating this dynamic structure control technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of explosion of complexity inherent in the existing method. Taking the neural network as a model of the system, control signals are directly obtained by minimizing the cumulative differences between a setpoint and output of the model. The applicability in nonlinear system is demonstrated by simulation experiments.
ISBN:9781467395861
1467395862
DOI:10.1109/ISCID.2015.174