Abstract
Robotic manipulators are complex and dynamic nonlinear mechanical systems subject to numerous uncertainties, such as payload variations, frictions, and unmodeled dynamics. To mitigate the uncertainty caused by these disturbances and minimize the tracking errors of the controllers, this study proposed a finite time tracking-based controller (FTC) that embeds a (NDO) and a second-order sliding-mode modifier (SOSM). The NDO was incorporated to compensate for the system’s global bounded uncertainty and the SOSM used a robust nonsingular terminal sliding-mode modifier to stabilize the controller. The theoretical analysis showed that the tracking error could quickly converge in finite time. Simulation on a typical robotics manipulator demonstrated the practical appeal of the proposed scheme.
References
1.
Tran
, M.-D.
, and Kang
, H.-J.
, 2016
, “A Novel Adaptive Finite Time Tracking Control for Robotic Manipulators Using Nonsingular Terminal Sliding-Mode and RBF Neural Networks
,” Int. J. Precis. Eng. Manuf.
, 17
(7
), pp. 863
–870
. 2.
Amin
, A. T. M.
, Rahim
, A. H. A.
, and Low
, C. Y.
, 2014
, “Adaptive Controller Algorithm for 2-DOF Humanoid Robot Arm
,” Procedia Technol.
, 15
, pp. 765
–774
. 3.
Ren
, B.
, Wang
, Y.
, Luo
, X.
, and Lozano
, R.
, 2019
, “A Hybrid Adaptive Control Strategy for Industrial Robotic Joints
,” IEEE Access
, 7
, pp. 47034
–47042
. 4.
Feng
, Y.
, Yu
, X.
, and Man
, Z.
, 2001
, “Adaptive Fast Terminal Sliding-Mode Tracking Control
of
Robotic Manipulator
,” Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No. 01CH37228)
, Orlando, FL
, Dec. 4-7
, pp. Vol. 4
, pp. 4021
–4026
. 5.
Maolin
, J.
, Jinoh
, L.
, Pyung Hun
, C.
, and Chintae
, C.
, 2009
, “Practical Nonsingular Terminal Sliding-Mode Control of Robot Manipulators for High-Accuracy Tracking Control
,” IEEE Trans. Ind. Electron.
, 56
(9
), pp. 3593
–3601
. 6.
Tran
, M.-D.
, and Kang
, H.-J.
, 2017
, “Adaptive Terminal Sliding-Mode Control of Uncertain Robotic Manipulators Based on Local Approximation of a Dynamic System
,” Neurocomputing
, 228
, pp. 231
–240
. 7.
Pan
, Y.
, Li
, X.
, Wang
, H.
, and Yu
, H.
, 2018
, “Continuous Sliding-Mode Control of Compliant Robot Arms: A Singularly Perturbed Approach
,” Mechatronics
, 52
, pp. 127
–134
. 8.
Ge
, S. S.
, Huang
, C. C.
, and Woon
, L. C.
, 1997
, “Adaptive Neural Network Control of Robot Manipulators in Task Space
,” IEEE Trans. Ind. Electron.
, 44
(6
), pp. 746
–752
. 9.
Liu
, H.
, and Zhang
, T.
, 2013
, “Adaptive Neural Network Finite Time Control for Uncertain Robotic Manipulators
,” J Intell. Rob. Syst.
, 75
(3–4
), pp. 363
–377
.10.
He
, W.
, David
, A. O.
, Yin
, Z.
, and Sun
, C.
, 2016
, “Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint
,” IEEE Trans. Syst. Man Cybernet.: Syst.
, 46
(6
), pp. 759
–770
. 11.
Khelfi
, M. F.
, and Abdessameud
, A.
, 2007
, “Robust H-Infinity Trajectory Tracking Controller for a 6 D.O.F PUMA 560 Robot Manipulator
,” The 2007 IEEE International Electric Machines and Drives Conference (IEMDC)
, Antalya, Turkey
, May 3–5
, pp. 88
–94
. 12.
Liu
, H.
, Tian
, X.
, Wang
, G.
, and Zhang
, T.
, 2016
, “Finite Time H Control for High-Precision Tracking in Robotic Manipulators Using Backstepping Control
,” IEEE Trans. Ind. Electron.
, 63
(9
), pp. 5501
–5513
. 13.
He
, W.
, Dong
, Y.
, and Sun
, C.
, 2016
, “Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation
,” IEEE Trans. Syst. Man Cybernet.: Syst.
, 46
(3
), pp. 334
–344
. 14.
Wang
, L.
, Chai
, T.
, and Zhai
, L.
, 2009
, “Neural-Network-Based Terminal Sliding-Mode Control of Robotic Manipulators Including Actuator Dynamics
,” IEEE Trans. Ind. Electron.
, 56
(9
), pp. 3296
–3304
. 15.
Guan
, J.
, Lin
, C.-M.
, Ji
, G.-L.
, Qian
, L.-W.
, and Zheng
, Y.-M.
, 2018
, “Robust Adaptive Tracking Control for Manipulators Based on a TSK Fuzzy Cerebellar Model Articulation Controller
,” IEEE Access
, 6
, pp. 1670
–1679
. 16.
Wai
, R.-J.
, 2003
, “Tracking Control Based on Neural Network Strategy for Robot Manipulator
,” Neurocomputing
, 51
, pp. 425
–445
. 17.
Sun
, T.
, Pei
, H.
, Pan
, Y.
, Zhou
, H.
, and Zhang
, C.
, 2011
, “Neural Network-Based Sliding-Mode Adaptive Control for Robot Manipulators
,” Neurocomputing
, 74
(14–15
), pp. 2377
–2384
. 18.
Islam
, S.
, and Liu
, X. P.
, 2011
, “Robust Sliding-Mode Control for Robot Manipulators
,” IEEE Trans. Ind. Electron.
, 58
(6
), pp. 2444
–2453
. 19.
Van Cuong
, P.
, and Nan
, W. Y.
, 2015
, “Adaptive Trajectory Tracking Neural Network Control With Robust Compensator for Robot Manipulators
,” Neural Comput. Appl.
, 27
(2
), pp. 525
–536
. 20.
Lian
, K.-Y.
, and Lin
, C.-R.
, 1998
, “Sliding-Mode Motion/Force Control of Constrained Robots
,” IEEE Trans. Autom. Control
, 43
(8
), pp. 1101
–1103
. 21.
Feng
, Y.
, Yu
, X.
, and Man
, Z.
, 2002
, “Non-singular Terminal Sliding-Mode Control of Rigid Manipulators
,” Automatica
, 38
(12), pp. 2159
–2167
. 22.
Mien
, V.
, Shuzhi Sam
, G.
, and Hongliang
, R.
, 2017
, “Finite Time Fault Tolerant Control for Robot Manipulators Using Time Delay Estimation and Continuous Nonsingular Fast Terminal Sliding-Mode Control
,” IEEE Trans. Cybern.
, 47
(7
), pp. 1681
–1693
.23.
Yang
, L.
, and Yang
, J.
, 2011
, “Nonsingular Fast Terminal Sliding-Mode Control for Nonlinear Dynamical Systems
,” Int. J. Robust Nonlinear Control
, 21
(16
), pp. 1865
–1879
. 24.
Mohammadi
, A.
, Tavakoli
, M.
, Marquez
, H. J.
, and Hashemzadeh
, F.
, 2013
, “Nonlinear Disturbance Observer Design for Robotic Manipulators
,” Control Eng. Pract.
, 21
(3
), pp. 253
–267
. 25.
Van
, M.
, Kang
, H.-J.
, and Suh
, Y.-S.
, 2013
, “A Novel Neural Second-Order Sliding-Mode Observer for Robust Fault Diagnosis in Robot Manipulators
,” Int. J. Precis. Eng. Manuf.
, 14
(3
), pp. 397
–406
. 26.
Yang
, P.
, Ma
, X.
, Wang
, J.
, Zhang
, G.
, Zhang
, Y.
, and Chen
, L.
, 2019
, “Disturbance Observer-Based Terminal Sliding-Mode Control of a 5-DOF Upper-Limb Exoskeleton Robot
,” IEEE Access
, 7
, pp. 62833
–62839
. 27.
Kim
, M. J.
, and Chung
, W. K.
, 2015
, “Disturbance-Observer-Based PD Control of Flexible Joint Robots for Asymptotic Convergence
,” IEEE Trans. Rob.
, 31
(6
), pp. 1508
–1516
. 28.
Chen
, W.-H.
, Ballance
, D. J.
, Gawthrop
, P. J.
, and O’Reilly
, J.
, 2000
, “A Nonlinear Disturbance Observer for Robotic Manipulators
,” IEEE Trans. Ind. Electron.
, 47
(4
), pp. 932
–938
. 29.
Li
, Z.
, Su
, C.-Y.
, Wang
, L.
, Chen
, Z.
, and Chai
, T.
, 2015
, “Nonlinear Disturbance Observer-Based Control Design for a Robotic Exoskeleton Incorporating Fuzzy Approximation
,” IEEE Trans. Ind. Electron.
, 62
(9
), pp. 5763
–5775
. 30.
Pan
, Y.
, Liu
, Y.
, Xu
, B.
, and Yu
, H.
, 2016
, “Hybrid Feedback Feedforward An Efficient Design of Adaptive Neural Network Control
,” Neural Netw.
, 76
, pp. 122
–134
. 31.
Cheng
, C.-C.
, and Lee
, J.-J.
, 2019
, “Design of Adaptive Terminal Super-Twisting Controllers for Nonlinear Systems With Mismatched Perturbations
,” Int. J. Control
, pp. 1
–11
. 32.
Jouini
, M.
, Dhahri
, S.
, and Sellami
, A.
, 2017
, “Design of Robust Supertwisting Algorithm Based Second-Order Sliding-Mode Controller for Nonlinear Systems With Both Matched and Unmatched Uncertainty
,” Complexity
, 2017
, pp. 1
–8
. 33.
Nagesh
, I.
, and Edwards
, C.
, 2014
, “A Multivariable Super-Twisting Sliding-Mode Approach
,” Automatica
, 50
(3
), pp. 984
–988
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