Research Papers

Adaptive Nonlinear Sliding Mode Control of Mechanical Servo System With LuGre Friction Compensation

[+] Author and Article Information
Qiang Chen

College of Information Engineering,
Zhejiang University of Technology,
Hangzhou 310023, China
e-mail: sdnjchq@zjut.edu.cn

Liang Tao

College of Information Engineering,
Zhejiang University of Technology,
Hangzhou 310023, China
e-mail: tao_liang@yeah.net

Yurong Nan

College of Information Engineering,
Zhejiang University of Technology,
Hangzhou 310023, China
e-mail: nyr@zjut.edu.cn

Xuemei Ren

School of Automation,
Beijing Institute of Technology,
Beijing 100081, China
e-mail: xmren@bit.edu.cn

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 16, 2015; final manuscript received November 21, 2015; published online December 15, 2015. Assoc. Editor: Hashem Ashrafiuon.

J. Dyn. Sys., Meas., Control 138(2), 021003 (Dec 15, 2015) (9 pages) Paper No: DS-15-1113; doi: 10.1115/1.4032068 History: Received March 16, 2015; Revised November 21, 2015

In this paper, the parameter identification and control problem are investigated for a mechanical servo system with LuGre friction. First of all, an intelligent glowworm swarm optimization (GSO) algorithm is developed to identify the friction parameters. Then, by using a finite-time parameter estimate law and nonlinear sliding mode technique, an adaptive nonlinear sliding mode control (NSMC) based on GSO is designed to speed up the parameter convergence and to decrease the overshoot and steady-state time in control process. Finally, comparative simulations are given to show that the proposed parameters identification technique and adaptive NSMC law are both effective with respect to fast convergence speed and high tracking accuracy.

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Grahic Jump Location
Fig. 1

Identified Stribeck curve for static parameters

Grahic Jump Location
Fig. 2

Tracking trajectories of θref1

Grahic Jump Location
Fig. 3

Tracking errors comparison of θref1

Grahic Jump Location
Fig. 4

Identification of σ0  = 12 for θref1

Grahic Jump Location
Fig. 5

Identification of σ1  = 2.5 for θref1

Grahic Jump Location
Fig. 6

Identification of ζ  = 2.7 for θref1

Grahic Jump Location
Fig. 7

Identification of J  = 0.9 for θref1

Grahic Jump Location
Fig. 8

Tracking trajectories of θref2

Grahic Jump Location
Fig. 9

Tracking errors comparison of θref2

Grahic Jump Location
Fig. 10

Identification of σ0  = 12 for θref2

Grahic Jump Location
Fig. 11

Identification of σ1  = 2.5 for θref2

Grahic Jump Location
Fig. 12

Identification of ζ  = 2.7 for θref2

Grahic Jump Location
Fig. 13

Identification of J  = 0.9 for θref2



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