Liaw Hwee Choo, Shirinzadeh Bijan, Smith Julian
Robotics and Mechatronics Research Laboratory, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria 3800, Australia.
IEEE Trans Neural Netw. 2009 Feb;20(2):356-67. doi: 10.1109/TNN.2008.2004406. Epub 2009 Jan 13.
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.
本文提出了一种用于微纳操作中压电驱动系统的鲁棒神经网络运动跟踪控制方法。该控制方法旨在解决在控制系统存在未知系统参数、包括滞后效应在内的非线性以及外部干扰的情况下，跟踪期望运动轨迹的问题。本文研究了相关控制问题，并建立了一种包括神经网络和滑模控制方案的控制方法。具体而言，选择径向基函数（RBF）神经网络进行函数逼近。在上述条件下，该控制方法确保了闭环系统的稳定性以及位置和速度跟踪误差收敛到零。还提出了一种离线学习过程以提高运动跟踪性能。实验研究展示了所提出的控制方法对期望运动轨迹的精确跟踪结果。凭借这种运动跟踪能力，所提出的控制方法有望实现高性能的压电驱动微纳操作系统。
