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Research Papers

Robust Iterative Learning Control for Vibration Suppression of Industrial Robot Manipulators

[+] Author and Article Information
Cong Wang

Department of Electrical
and Computer Engineering;
Department of Mechanical
and Industrial Engineering,
New Jersey Institute of Technology,
Newark, NJ 07102
e-mail: wangcong@njit.edu

Minghui Zheng

Department of Mechanical Engineering,
University of California, Berkeley,
Berkeley, CA 94720
e-mail: minghuizheng@berkeley.edu

Zining Wang

Department of Mechanical Engineering,
University of California, Berkeley,
Berkeley, CA 94720
e-mail: wangzining@berkeley.edu

Cheng Peng

Department of Mechanical Engineering,
University of California, Berkeley,
Berkeley, CA 94720
e-mail: chengpeng2014@berkeley.edu

Masayoshi Tomizuka

Department of Mechanical Engineering,
University of California, Berkeley,
Berkeley, CA 94720
e-mail: tomizuka@berkeley.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 2, 2016; final manuscript received May 30, 2017; published online August 29, 2017. Assoc. Editor: Tesheng Hsiao.

J. Dyn. Sys., Meas., Control 140(1), 011003 (Aug 29, 2017) (9 pages) Paper No: DS-16-1479; doi: 10.1115/1.4037265 History: Received October 02, 2016; Revised May 30, 2017

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 (GPR) method, the proposed method adopts a two degrees-of-freedom (2DOF) 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.

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Figures

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Fig. 1

Misplacement of weld spots caused by drive-train flexibility

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Fig. 2

A lumped model of the geared joints of industrial robot manipulators

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Fig. 3

Torque learning and motor reference learning

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Fig. 5

Simulating a servo system with a two-mass model

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Fig. 6

A comparison of dual-stage ILC with 2DOF ILC

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Fig. 7

The near-unit response via a stable pseudo-inverse

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Fig. 8

A hypothetical feedback system with L as a fictitious real-time controller

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Fig. 9

The undesirable tradeoff comes with the H∞ penalty

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Fig. 10

The fictitious feedback system with uncertainty

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Fig. 11

Effect of the robust formulation (each line is a sampled system)

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Fig. 12

A FANUC M-16iB robot manipulator

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Fig. 13

Hardware deployment of the control system

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Fig. 14

Direct end-effector sensing

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Fig. 15

The frequency response of the first three joints (o: chirp sweep result and x: frequency-by-frequency result) [27]

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Fig. 16

Position and acceleration errors through iterations

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