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Research Papers

High Performance Adaptive Control of Mechanical Servo System With LuGre Friction Model: Identification and Compensation

[+] Author and Article Information
Xingjian Wang

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; Science and Technology on Aircraft Control Laboratory,  Beihang University, Beijing 100191, Chinawangxj@live.com

Shaoping Wang

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; Science and Technology on Aircraft Control Laboratory,  Beihang University, Beijing 100191, Chinashaopingwang@vip.sina.com

J. Dyn. Sys., Meas., Control 134(1), 011021 (Dec 06, 2011) (8 pages) doi:10.1115/1.4004785 History: Received October 03, 2010; Revised June 15, 2011; Published December 06, 2011; Online December 06, 2011

LuGre dynamic friction model has been widely used in servo system for friction compensation, but it increases the difficulty of controller design because its parameters are difficult to be identified and its internal state is immeasurable. This paper presents a parameter identification technique based on novel evolutionary algorithm (NEA) for LuGre friction model. In order to settle the practical digital implementation problem of LuGre model, this paper also proposes a modified dual-observer with discontinuous mapping and smooth transfer function. On the basis of the parameter identification results and the modified dual-observer, this paper designs an adaptive control algorithm with dynamic friction compensation for hydraulic servo system. The comparative experiments indicate that the proposed parameter identification technique and the adaptive control algorithm with modified dual-observer are effective with high tracking performance.

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Figures

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Figure 1

Experiment setup for friction identification

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Figure 2

Flow diagram of novel evolutionary algorithm

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Figure 3

Identified Stribeck curve for static parameter identification

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Figure 4

Identification experiment result of dynamic parameters

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Figure 5

Tracking errors in low frequency sinusoidal experiments

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Figure 6

Friction estimate in low frequency sinusoidal experiments

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Figure 7

Tracking errors in high frequency sinusoidal experiments

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Figure 8

The desired point-to-point trajectory

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Figure 9

Tracking errors in point-to-point experiments

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Figure 10

Friction estimate in the point-to-point experiments

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