Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2002.804306
Title: Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems
Authors: Ge, S.S. 
Wang, J. 
Keywords: Backstepping
Neural networks
Robust adaptive control
Uncertain nonlinear systems
Issue Date: Nov-2002
Source: Ge, S.S.,Wang, J. (2002-11). Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems. IEEE Transactions on Neural Networks 13 (6) : 1409-1419. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2002.804306
Abstract: This paper presents a robust adaptive neural control design for a class of perturbed strict feedback nonlinear system with both completely unknown virtual control coefficients and unknown nonlinearities. The unknown nonlinearities comprise two types of nonlinear functions: one naturally satisfies the "triangularity condition" and can be approximated by linearly parameterized neural networks, while the other is assumed to be partially known and consists of parametric uncertainties and known "bounding functions." With the utilization of iterative Lyapunov design and neural networks, the proposed design procedure expands the class of nonlinear systems for which robust adaptive control approaches have been studied. The design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. Leakage terms are incorporated into the adaptive laws to prevent parameter drifts due to the inherent neural-network approximation errors. It is proved that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals. The control performance can be guaranteed by an appropriate choice of the design parameters. Simulation studies are included to illustrate the effectiveness of the proposed approach.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/57287
ISSN: 10459227
DOI: 10.1109/TNN.2002.804306
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