Intelligent tracking control for a class of uncertain high-order nonlinear systems
Date
2016
Authors
Zhao, X.
Shi, P.
Zheng, X.
Zhang, J.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2016; 27(9):1976-1982
Statement of Responsibility
Xudong Zhao, Peng Shi, Xiaolong Zheng, and Jianhua Zhang
Conference Name
Abstract
This brief is concerned with the problem of intelligent tracking control for a class of high-order nonlinear systems with completely unknown nonlinearities. An intelligent adaptive control algorithm is presented by combining the adaptive backstepping technique with the neural networks' approximation ability. It is shown that the practical output tracking performance of the system is achieved using the proposed state-feedback controller under two mild assumptions. In particular, by introducing a parameter in the derivations, the tracking error between the time-varying target signal and the output can be reduced via tuning the controller design parameters. Moreover, in order to solve the problem of overparameterization, which is a common issue in adaptive control design, a controller with one adaptive law is also designed. Finally, simulation results are given to show the effectiveness of the theoretical approaches and the potential of the proposed new design techniques.
School/Discipline
Dissertation Note
Provenance
Description
Date of publication August 11, 2015; date of current version August 15, 2016.
Access Status
Rights
© 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.