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research-article

Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators

Authors:

Shibo CaiAuthors Info & Claims

Volume 72, Issue C

https://doi.org/10.1016/j.rcim.2019.101884

Published: 01 December 2021 Publication History

Highlights

•

A robust adaptive fuzzy sliding controller designed in the task space.

•

High-frequency uncertain term approximated by using a fuzzy logic system.

•

Experimental test results validations of the proposed controller.

Abstract

The ever increasingly stringent performance requirements of industrial robotic applications highlight significant importance of advanced robust control designs for serial robots that are generally subject to various uncertainties and external disturbances. Therefore, this paper proposes and investigates the design and implementation of a robust adaptive fuzzy sliding mode controller in the task space for uncertain serial robotic manipulators. The sliding mode control is well known for its robustness to system parameter variations and external disturbances, and is thus a highly desirable and cost-effective approach to achieve high precision control task for serial robots. The proposed controller is designed based on a fuzzy logic approximation to accomplish trajectory tracking with high accuracy and simultaneously attenuate effects from uncertainties. In the controller, the high-frequency uncertain term is approximated by using a fuzzy logic system while the low-frequency term is adaptively updated in real time based on a parametric adaption law. The control efficacy and effectiveness of the proposed control algorithm are comparatively verified against a recently proposed conventional controller. The test results demonstrate that the proposed controller has better trajectory tracking performances and is more robust against large disturbances than the conventional controller under the same operating conditions.

References

[1]

P. Corke, Robotics, Vision and Control: Fundamental Algorithms In MATLAB®., 118, Springer, 2017.

[2]

Z. Pandilov, V. Dukovski, Comparison of the characteristics between serial and parallel robots, Acta Techn. Corviniensis-Bull. Eng. 7 (1) (2014) 143.

[3]

B. Xiao, S. Yin, O. Kaynak, Tracking control of robotic manipulators with uncertain kinematics and dynamics, IEEE Trans. Ind. Electron. 63 (10) (2016) 6439–6449.

[4]

B. Xiao, S. Yin, O. Kaynak, et al., Observer-based control for robotic manipulations with uncertain kinematics and dynamics. Advanced Motion Control (AMC), in: 2016 IEEE 14th International Workshop on, IEEE, 2016, pp. 282–288.

[5]

Q. Zhou, H. Li, P. Shi, Decentralized adaptive fuzzy tracking control for robot finger dynamics, IEEE Trans. Fuzzy Syst. 23 (3) (2015) 501–510.

[6]

S.A. Ajwad, J. Iqbal, A.A. Khan, et al., Disturbance-observer-based robust control of a serial-link robotic manipulator using SMC and PBC techniques, Stud. Inform. Control 24 (4) (2015) 401–408.

[7]

D. Zhao, S. Li, Q. Zhu, Adaptive synchronised tracking control for multiple robotic manipulators with uncertain kinematics and dynamics, Int. J. Syst. Sci. 47 (4) (2016) 791–804.

Digital Library

[8]

L. Pan, G. Bao, F. Xu, et al., Adaptive robust sliding mode trajectory tracking control for 6 degree-of-freedom industrial assembly robot with disturbances, Assem. Autom. (2018).

[9]

M.I. Ullah, S.A. Ajwad, M. Irfan, et al., Non-linear control law for articulated serial manipulators: simulation augmented with hardware implementation, Elektronika ir Elektrotechnika 22 (1) (2016) 3–7.

[10]

S. Mishra, et al., Task space robust motion control of a mobile manipulator using a nonlinear control with an uncertainty estimator, Comput. Electr. Eng. (2017),.

Digital Library

[11]

L. Yu, S. Fei, L. Sun, et al., Design of robust adaptive neural switching controller for robotic manipulators with uncertainty and disturbances, J. Intell. Robot. Syst. 77 (3–4) (2015) 571–581.

[12]

S. Gorji, M.J. Yazdanpanah, A novel robust adaptive trajectory tracking in robot manipulators, J. Comput. Robot. 10 (2) (2017) 1–11.

[13]

F. Piltan, A. Hosainpour, S. Emamzadeh, et al., Design sliding mode controller of with parallel fuzzy inference system compensator to control of robot manipulator, IAES Int. J. Robot. Autom. 2 (4) (2013) 149.

[14]

P.R. Ouyang, V. Pano, Position domain synchronization control of multi-degrees of freedom robotic manipulator, J. Dyn. Syst. Meas. Control 136 (2) (2014).

[15]

M.I. Ullah, S.A. Ajwad, R.U. Islam, et al., Modeling and computed torque control of a 6 degree of freedom robotic arm. Robotics and Emerging Allied Technologies in Engineering (iCREATE, in: 2014 International Conference on, IEEE, 2014, pp. 133–138.

[16]

H. Liu, T. Zhang, Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics, Robot. Comput. Integr. Manuf. 29 (2) (2013) 301–308.

[17]

H. Liu, W. Zhou, X. Lai, et al., An efficient inverse kinematic algorithm for a PUMA560-structured robot manipulator, Int. J. Adv. Robot. Syst. 10 (5) (2013) 236.

[18]

L. Yu, S. Fei, J. Huang, et al., Trajectory switching control of robotic manipulators based on RBF neural networks, Circuit. Syst. Signal Process. 33 (4) (2014) 1119–1133.

[19]

N. Al-Shuka H F, B. Corves, H Zhu W, Function approximation technique-based adaptive virtual decomposition control for a serial-chain manipulator, Robotica 32 (3) (2014) 375–399.

[20]

L. Tang, Y.J. Liu, S. Tong, Adaptive neural control using reinforcement learning for a class of robot manipulator, Neural Comput. Appl. 25 (1) (2014) 135–141.

[21]

A. Kumar, P.M. Pathak, N. Sukavanam, Trajectory control of a two DOF rigid–flexible space robot by a virtual space vehicle, Rob. Auton. Syst. 61 (5) (2013) 473–482.

[22]

D. Markus E, T. Agee J, A Jimoh A, Flat control of industrial robotic manipulators, Rob. Auton. Syst. 87 (2017) 226–236.

[23]

D.J. López-Araujo, A. Zavala-Río, V. Santibáñez, et al., A generalized global adaptive tracking control scheme for robot manipulators with bounded inputs, Int. J. Adapt. Control Signal Process. 29 (2) (2015) 180–200.

[24]

X. Yin, L. Pan, Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy, ISA Trans. 72 (2018) 178–184.

[25]

X. Yin, L. Pan, Enhancing trajectory tracking accuracy for industrial robot with robust adaptive control, Robot Comput. Integr. Manuf. 51 (2018) 97–102.

[26]

K. Kokkalis, G. Michalos, P. Aivaliotis, et al., An approach for implementing power and force limiting in sensorless industrial robots, Procedia CIRP 76 (2018) 138–143.

[27]

P. Aivaliotis, G. Michalos, S. Makris, Cooperating robots for fixtureless assembly: modelling and simulation of tool exchange process, Int. J. Comput. Integr. Manuf. 31 (12) (2018) 1235–1246.

[28]

J Angeles, Rational Kinematic, Springer Science & Business Media, 2013.

[29]

J. Angeles, A. Rojas, Manipulator inverse kinematics via condition-number minimization and continuation, Int. J. Robot. Autom. 2 (2) (1987) 61–69.

[30]

L Chiu S, Kinematic characterization of manipulators: an approach to defining optimality, in: Robotics and Automation, 1988. Proceedings., 1988 IEEE International Conference on, IEEE, 1988, pp. 828–833.

[31]

S. Tong, Y. Li, Y. Li, et al., Observer-based adaptive fuzzy backstepping control for a class of stochastic nonlinear strict-feedback systems, IEEE Trans. Syst. Man Cybern. Part B 41 (6) (2011) 1693–1704.

[32]

L. X.Wang, Adaptive Fuzzy Systems and Control, Prentice-Hall, Englewood Cliffs, NJ, 1994.

[33]

Y. Li, S. Tong, T. Li, Observer-based adaptive fuzzy tracking control of MIMO stochastic nonlinear systems with unknown control directions and unknown dead zones, IEEE Trans. Fuzzy Syst. 23 (4) (2015) 1228–1241.

Digital Library

[34]

J.J.E. Slotine, W. Li, Applied Nonlinear Control, Prentice-Hall, Englewood Cliffs, NJ, 1991.

[35]

J.J. Slotine, S.S. Sastry, Tracking control of non-linear systems using sliding surfaces, with application to robot manipulators, Int. J. Control 38 (2) (1983) 465–492.

[36]

J.J.E. Slotine, Sliding controller design for non-linear systems, Int J Control 40 (2) (1984) 421–434.

[37]

https://www.lcautomation.com/wb_documents/mitsubishi/mitsubishi%20rv-f%20series%20robot%20brochure.pdf.

[38]

http://petercorke.com/wordpress/toolboxes/robotics-toolbox.

[39]

M. Van, An enhanced robust fault tolerant control based on an adaptive fuzzy pid-nonsingular fast terminal sliding mode control for uncertain nonlinear systems, IEEE/ASME Trans. Mechatron. (2018).

[40]

A. Levant, Robust exact differentiation via sliding mode technique, Automatica 34 (3) (1998) 379–384.

Cited By

Yuan XLi MLi Q(2023)Manipulator Control Based on Adaptive RBF Network ApproximationInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.32675116:3(1-16)Online publication date: 17-Aug-2023
https://dl.acm.org/doi/10.4018/IJITSA.326751
Feng HJiang JChang XYin CCao DYu HLi CXie J(2023)Adaptive sliding mode controller based on fuzzy rules for a typical excavator electro-hydraulic position control systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107008126:PCOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.107008
Zhang H(2022)Adaptive Fuzzy Controller Design for Uncertain Robotic Manipulators Subject to Nonlinear Dead Zone InputsComputational Intelligence and Neuroscience10.1155/2022/91732492022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9173249
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Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators

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Information & Contributors

Information

Published In

cover image Robotics and Computer-Integrated Manufacturing

Robotics and Computer-Integrated Manufacturing Volume 72, Issue C

Dec 2021

355 pages

ISSN:0736-5845

Issue’s Table of Contents

Copyright © 2021.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 December 2021

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Citations

Cited By

Yuan XLi MLi Q(2023)Manipulator Control Based on Adaptive RBF Network ApproximationInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.32675116:3(1-16)Online publication date: 17-Aug-2023
https://dl.acm.org/doi/10.4018/IJITSA.326751
Feng HJiang JChang XYin CCao DYu HLi CXie J(2023)Adaptive sliding mode controller based on fuzzy rules for a typical excavator electro-hydraulic position control systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107008126:PCOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.107008
Zhang H(2022)Adaptive Fuzzy Controller Design for Uncertain Robotic Manipulators Subject to Nonlinear Dead Zone InputsComputational Intelligence and Neuroscience10.1155/2022/91732492022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9173249
Aghaseyedabdollah MAbedi MPourgholi M(2022)Supervisory adaptive fuzzy sliding mode control with optimal Jaya based fuzzy PID sliding surface for a planer cable robotSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07237-y26:17(8441-8458)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s00500-022-07237-y

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