Abstract
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.
Issue Section:
Research Papers
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
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