Radial Basis Function Neural Network-based Adaptive Control of Uncertain Nonlinear Systems

被引：0
|
作者：
Ait Abbas, Hamou [1 ]
Zegnini, Boubakeur [1 ]
Belkheiri, Mohammed [2 ]
Rabhi, Abdelhamid [3 ]
机构：
[1] Univ Amar Telidji Laghouat, Lab Etud & Dev Mat Semicond & Dielect, BP G37 Route Ghardaia, Laghouat 03000, Algeria
[2] Univ Amar Telidji Laghouat, Lab Telecommun Signaux & Syst, Laghouat 03000, Algeria
[3] Univ Picardie Jules Verne, Lab Modelisat Informat & Syst, F-80000 Amiens, France
来源：
3RD INTERNATIONAL CONFERENCE ON CONTROL, ENGINEERING & INFORMATION TECHNOLOGY (CEIT 2015) | 2015年
关键词：
OUTPUT-FEEDBACK CONTROL; SMALL-GAIN APPROACH;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We aim to design in the present paper an adaptive output feedback control scheme to address the tracking problem of an uncertain system having full relative degree in the presence of neglected dynamics and modelling errors. Then, the obtained controller is augmented by an online radial basis function neural network (RBF NN) that is used to adaptively compensate for the nonlinearity existing in the uncertain systems. A linear observer is introduced to generate an error signal for the adaptive laws. Ultimate boundedness is proven through Lyapunov's direct method. The forcefulness of the theoretical results is demonstrated through computer simulations of a nonlinear second-order system.
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页数：6
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