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research-article

Robust Iterative Learning Control for Vibration Suppression of Industrial Robot Manipulators

[+] Author and Article Information
Cong Wang

ECE and MIE Departments, New Jersey Institute of Technology, Newark, NJ 07102
wangcong@njit.edu

Minghui Zheng

ME Department, University of California, Berkeley, Berkeley, CA 94720
minghuizheng@berkeley.edu

Zining Wang

ME Department, University of California, Berkeley, Berkeley, CA 94720
wangzining@berkeley.edu

Cheng Peng

ME Department, University of California, Berkeley, Berkeley, CA 94720
chengpeng2014@berkeley.edu

Masayoshi Tomizuka

ME Department, University of California, Berkeley, Berkeley, CA 94720
tomizuka@berkeley.edu

1Corresponding author.

ASME doi:10.1115/1.4037265 History: Received October 02, 2016; Revised May 30, 2017

Abstract

Vibration suppression is of fundamental importance to the performance of industrial robot manipulators. Cost constraints, however, limit the design options of servo and sensing systems. The resulting low drive-train stiffness and lack of direct load side measurement make it difficult to reduce the vibration of the robot's end-effector and hinder the application of robot manipulators to many demanding industrial applications. This paper proposes a few ideas of iterative learning control (ILC) for vibration suppression of industrial robot manipulators. Compared to the state-of-the-art techniques such as the dual-stage ILC method and the two-part Gaussian Process Regression method, the proposed method adopts a two-degree-of-freedom structure and gives a very lean formulation as well as improved effects. Moreover, in regards to the system variations brought by the nonlinear dynamics of robot manipulators, two robust formulations are developed and analyzed. The proposed methods are explained using simulation studies and validated using an actual industrial robot manipulator.

Copyright (c) 2017 by ASME
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