移动操纵器的神经网络控制。

Neural-network control of mobile manipulators.

作者信息

Lin S, Goldenberg A A

机构信息

Mechanical and Industrial Engineering Department, University of Toronto, Toronto, ON M5S 3G8, Canada.

出版信息

IEEE Trans Neural Netw. 2001;12(5):1121-33. doi: 10.1109/72.950141.

DOI:10.1109/72.950141

PMID:18249939

Abstract

In this paper, a neural network (NN)-based methodology is developed for the motion control of mobile manipulators subject to kinematic constraints. The dynamics of the mobile manipulator is assumed to be completely unknown, and is identified online by the NN estimators. No preliminary learning stage of NN weights is required. The controller is capable of disturbance-rejection in the presence of unmodeled bounded disturbances. The tracking stability of the closed-loop system, the convergence of the NN weight-updating process and boundedness of NN weight estimation errors are all guaranteed. Experimental tests on a 4-DOF manipulator arm illustrate that the proposed controller significantly improves the performance in comparison with conventional robust control.

摘要

本文提出了一种基于神经网络（NN）的方法，用于受运动学约束的移动机械手的运动控制。假设移动机械手的动力学完全未知，并由神经网络估计器在线识别。无需对神经网络权重进行初步学习阶段。该控制器能够在存在未建模的有界干扰的情况下进行干扰抑制。闭环系统的跟踪稳定性、神经网络权重更新过程的收敛性以及神经网络权重估计误差的有界性均得到保证。在一个四自由度机械臂上进行的实验测试表明，与传统的鲁棒控制相比，所提出的控制器显著提高了性能。