期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 20, 期 2, 页码 356-367出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2008.2004406
关键词
Function approximations; micro/nanomanipulation; neural network control; piezoelectric actuator; robust control
类别
资金
- Australian Research Council (ARC) Linkage Infrastructure, Equipment and Facilities (LIEF) [LE0775692, DP0666366]
This paper presents a robust neural network motion tracking control methodology for piezoelectric actuation systems employed in micro/nanomanipulation. This control methodology is proposed for tracking of desired motion trajectories in the presence of unknown system parameters, nonlinearities including the hysteresis effect and external disturbances in the control systems. In this paper, the related control issues are investigated, and a control methodology is established including the neural networks and a sliding control scheme. In particular, the radial basis function (RBF) neural networks are chosen for function approximations. The stability of the closed-loop system, as well as the convergence of the position and velocity tracking errors to zero, is assured by the control methodology in the presence of the aforementioned conditions. An offline learning procedure is also proposed for the improvement of the motion tracking performance. Precise tracking results of the proposed control methodology for a desired motion trajectory are demonstrated in the experimental study. With such a motion tracking capability, the proposed control methodology promises the realization of high-performance piezoelectric actuated micro/nanomanipulation systems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据