Adaptive Neural Control for Uncertain Nonlinear Systems in Pure-feedback Form with Hysteresis Input

被引：9
|
作者：
Ren, Beibei [1 ,2 ]
Ge, Shuzhi Sam [1 ,2 ]
Lee, Tong Heng [1 ,2 ]
Su, Chun-Yi [3 ]
机构：
[1] Natl Univ Singapore, Social Robot Lab, Interact Digital Media Inst, Singapore 117576, Singapore
[2] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
[3] Concordia Univ, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada
来源：
47TH IEEE CONFERENCE ON DECISION AND CONTROL, 2008 (CDC 2008) | 2008年
关键词：
D O I：
10.1109/CDC.2008.4739240
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. The non-affine problem both in the pure-feedback form and in the generalized Prandtl-Ishlinskii hysteresis input function is solved by adopting the Mean Value Theorem. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.
引用
收藏
页码：86 / 91
页数：6
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