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.

References

References
1.
Qou
,
B.
, and
Cheng
,
S. K.
,
2008
,
AC Servo Motor and Control
,
Machinery Industry Press
,
Beijing
(in Chinese).
2.
Angue-Mintsa
,
H.
,
Venugopal
,
R.
,
Kenne
,
J. P.
, and
Belleau
,
C.
,
2011
, “
Adaptive Position Control of an Electrohydraulic Servo System With Load Disturbance Rejection and Friction Compensation
,”
ASME J. Dyn. Syst. Meas. Control
,
133
(
6
), p.
064506
.
3.
Chen
,
Q.
,
Yu
,
L.
, and
Nan
,
Y. R.
,
2013
, “
Finite-Time Tracking Control for Motor Servo Systems With Unknown Dead-Zones
,”
J. Syst. Sci. Complexity
,
26
(
6
), pp.
940
956
.
4.
Liu
,
Z.
,
Lai
,
G. Y.
,
Zhang
,
Y.
,
Chen
,
X.
, and
Chen
,
C. L. P.
,
2014
, “
Adaptive Neural Control for a Class of Nonlinear Time-Varying Delay Systems With Unknown Hysteresis
,”
IEEE Trans. Neural Networks Learn. Syst.
,
25
(
12
), pp.
2129
2140
.
5.
Armstrong-Helouvry
,
B.
,
Dupont
,
P.
, and
Dewit
,
C. C.
,
1994
, “
A Survey of Models, Analysis Tools and Compensation Methods for the Control of Machines With Friction
,”
Automatica
,
30
(
7
), pp.
1083
1138
.
6.
Armstrong
,
H. B.
,
1991
,
Control of Machines With Friction
,
Kluwer Academic Publishers
,
Boston
.
7.
Lampaert
,
V.
,
Swevers
,
J.
, and
AI-Bender
,
F.
,
2002
, “
Modification of the Leuven Integrated Friction Model Structure
,”
IEEE Trans. Autom. Control
,
47
(
4
), pp.
683
687
.
8.
Rizos
,
D. D.
, and
Fassois
,
S. D.
,
2009
, “
Friction Identification Based Upon the LuGre and Maxwell-Slip Models
,”
IEEE Trans. Control Syst. Technol.
,
17
(
1
), pp.
153
160
.
9.
Maeda
,
Y.
, and
Lwasaki
,
M.
,
2013
, “
Rolling Friction Model-Based Analyses and Compensation for Slow Settling Response in Precise Positioning
,”
IEEE Trans. Ind. Electron.
,
60
(
12
), pp.
5841
5853
.
10.
Canudas de Wit
,
C.
,
Olsson
,
H.
,
Astrom
,
K. J.
, and
Lischinsky
,
P.
,
1995
, “
A New Model for Control of Systems With Friction
,”
IEEE Trans. Autom. Control
,
40
(
3
), pp.
419
425
.
11.
Kurian
,
P. C.
,
2009
, “
Space-Borne Motor Friction Estimation Using Genetic Algorithm (GA)
,”
2009 International Conference on Control, Automation, Communication and Energy Conservation (INCACEC)
, Perundurai, Tamil Nadu, June 4–6, Vol. 1, pp.
475
478
.
12.
Zhang
,
W. J.
,
2007
, “
Parameter Identification of LuGre Friction Model for Servo System Based on Improved Particle Swarm Optimization Algorithm
,”
26th Chinese Control Conference
, Zhangjiajie, China, July 26–31, Vol.
3
, pp.
135
139
.
13.
Jayakumar
,
D. N.
, and
Venkatesh
,
P.
,
2014
, “
Glowworm Swarm Optimization Algorithm With Topsis for Solving Multiple Objective Environmental Economic Dispatch Problem
,”
Appl. Soft Comput.
,
23
(1), pp.
375
386
.
14.
Garcia-Segura
,
T.
,
Yepes
,
V.
,
Marti
,
J. V.
, and
Alcala
,
J.
,
2014
, “
Optimization of Concrete I-Beams Using a New Hybrid Glow-Worm Swarm Algorithm
,”
Latin Am. J. Solids Struct.
,
11
(
7
), pp.
1190
1205
.
15.
Lukasik
,
S.
, and
Kowalski
,
P. A.
,
2014
, “
Fully Informed Swarm Optimization Algorithms: Basic Concepts, Variants and Experimental Evaluation
,”
2014 Federated Conference on Computer Science and Information Systems
, Warsaw, Poland, Sept. 7–10, pp.
155
161
.
16.
Ouyang
,
Z.
, and
Zhou
,
Y.
,
2011
, “
Self-Adaptive Step Glowworm Swarm Optimization Algorithm
,”
J. Comput. Appl.
,
31
(
7
), pp.
1804
1807
.
17.
Krishnand
,
K. N.
, and
Ghose
,
D.
,
2009
, “
Glowworm Swarm Optimization for Simultaneous Capture of Multiple Local Optima of Multimodal Functions
,”
Swarm Intell.
,
3
(
2
), pp.
87
124
.
18.
Krishnand
,
K. N.
, and
Ghose
,
D.
,
2009
, “
Glowworm Swarm Optimization: A New Method for Optimizing Multi-Modal Functions
,”
Int. J. Comput. Intell. Stud.
,
1
(
1
), pp.
93
119
.
19.
Armstrong
,
B.
,
Neevel
,
D.
, and
Kusik
,
T.
,
1999
, “
New Results in NPID Control: Tracking, Integral Control, Friction Compensation and Experimental Results
,”
IEEE International Conference on Robotics and Automation
, Detroit, MI, Vol.
2
, pp.
837
842
.
20.
Na
,
J.
,
Chen
,
Q.
,
Ren
,
X. M.
, and
Guo
,
Y.
,
2014
, “
Adaptive Prescribed Performance Motion Control of Servo Mechanisms With Friction Compensation
,”
IEEE Trans. Ind. Electron.
,
61
(
1
), pp.
486
494
.
21.
Chaoui
,
H.
, and
Sicard
,
P.
,
2012
, “
Adaptive Fuzzy Logic Control of Permanent Magnet Synchronous Machines With Nonlinear Friction
,”
IEEE Trans. Ind. Electron.
,
59
(
2
), pp.
1123
1133
.
22.
Gilbart
,
J. W.
, and
Winston
,
G. C.
,
1974
, “
Adaptive Compensation for an Optical Tracking Telescope
,”
Automatica
,
10
(
2
), pp.
125
131
.
23.
Yoon
,
J. Y.
, and
Trumper
,
D. L.
,
2014
, “
Friction Modeling, Identification, and Compensation Based on Friction Hysteresis and Dahl Resonance
,”
Mechatronics
,
24
(
6
), pp.
734
741
.
24.
Meng
,
D. Y.
,
Tao
,
G. L.
,
Liu
,
H.
, and
Zhu
,
X. C.
,
2014
, “
Adaptive Robust Motion Trajectory Tracking Control of Pneumatic Cylinders With LuGre Model-Based Friction Compensation
,”
Chin. J. Mech. Eng.
,
27
(
4
), pp.
802
815
.
25.
Mondal
,
S.
, and
Mahanta
,
C.
,
2012
, “
A Fast Converging Robust Controller Using Adaptive Second Order Sliding Mode
,”
ISA Trans.
,
51
(
6
), pp.
713
721
.
26.
Fulwani
,
D.
,
Bandyopadhyay
,
B.
, and
Fridman
,
L.
,
2012
, “
Non-Linear Sliding Surface: Towards High Performance Robust Control
,”
IET Control Theory Appl.
,
6
(
2
), pp.
235
242
.
27.
Mohammad
,
A.
,
Uchiyama
,
N.
, and
Sano
,
S.
,
2014
, “
Reduction of Electrical Energy Consumed by Feed-Drive Systems Using Sliding-Mode Control With a Nonlinear Sliding Surface
,”
IEEE Trans. Ind. Electron.
,
61
(
6
), pp.
2875
2882
.
28.
Wang
,
X. J.
, and
Wang
,
S. P.
,
2012
, “
High Performance Adaptive Control of Mechanical Servo System With LuGre Friction Model: Identification and Compensation
,”
ASME J. Dyn. Syst. Meas. Control
,
134
(
1
), p.
011021
.
29.
Chen
,
C. Y.
, and
Cheng
,
M. Y.
,
2012
, “
Adaptive Disturbance Compensation and Load Torque Estimation for Speed Control of a Servomechanism
,”
Int. J. Mach. Tools Manuf.
,
59
(1), pp.
6
15
.
30.
Lu
,
Y. Z.
,
Yan
,
D. P.
, and
Levy
,
D.
,
2015
, “
Friction Coefficient Estimation in Servo Systems Using Neural Dynamic Programming Inspired Particle Swarm Search
,”
Appl. Intell.
,
43
(
1
), pp.
1
14
.
31.
Wang
,
Y. F.
,
Wang
,
D. H.
, and
Chai
,
T. Y.
,
2009
, “
Modeling and Control Compensation of Nonlinear Friction Using Adaptive Fuzzy Systems
,”
Mech. Syst. Signal Process.
,
23
(
8
), pp.
2445
2457
.
32.
Adetola
,
V.
, and
Guay
,
M.
,
2010
, “
Performance Improvement in Adaptive Control of Linearly Parameterized Nonlinear Systems
,”
IEEE Trans. Autom. Control
,
55
(
9
), pp.
2182
2186
.
33.
Adetola
,
V.
, and
Guay
,
M.
,
2008
, “
Finite-Time Parameter Estimation in Adaptive Control of Nonlinear Systems
,”
IEEE Trans. Autom. Control
,
53
(
3
), pp.
807
811
.
34.
Chen
,
Q.
,
Ren
,
X. M.
, and
Oliver
,
J. A.
,
2012
, “
Identifier-Based Adaptive Neural Dynamic Surface Control for Uncertain DC-DC Buck Converter System With Input Constraint
,”
Commun. Nonlinear Sci. Numer. Simul.
,
17
(
4
), pp.
1871
1883
.
This content is only available via PDF.
You do not currently have access to this content.