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Robust Hybrid Fractional Order Proportional Derivative Sliding Mode Controller for Robot Manipulator Based on Extended Grey Wolf Optimizer

Published online by Cambridge University Press:  13 June 2019

Hossein Komijani*
Affiliation:
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Mojtaba Masoumnezhad
Affiliation:
Department of Mechanical Engineering, Guilan Branch, Technical and Vocational University, Tehran, Iran. E-mail: mmasomnezhad@tvu.ac.ir
Morteza Mohammadi Zanjireh
Affiliation:
Computer Engineering Department, Imam Khomeini International University, Qazvin, Iran. E-mail: mmzanjireh@gmail.com
Mahdi Mir
Affiliation:
Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. E-mail: mahdimir.ir@gmail.com
*
*Corresponding author. E-mail: h.komijani@gmail.com

Summary

This paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HFOPDSMC is proposed. The bounded model uncertainties are considered in the dynamics of the robot, and then the robustness of the controller is verified. The Lyapunov theory is utilized in order to show the stability of the proposed controller. In this paper, the EGWO is developed by adding the emphasis coefficients to the typical grey wolf optimizer (GWO). The GWO and EGWO, then, are applied to optimize the proposed control parameters which result in the optimized GWO-HFOPDSMC and EGWO-HFOPDSMC, respectively. The effectivenesses of the optimized controllers (GWO-HFOPDSMC and EGWO-HFOPDSMC) are completely verified by comparing the simulation results of the optimized controllers with the typical FOSMC and HFOPDSMC.

Type
Articles
Copyright
© Cambridge University Press 2019 

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References

Capisani, L., Ferrara, A. and Pisano, A., “January. Second-order sliding mode control with adaptive control authority for the tracking control of robotic manipulators,” IFAC Proceedings 44(1), 1031910324 (2011).10.3182/20110828-6-IT-1002.01202CrossRefGoogle Scholar
Amer, A. F., Sallam, E. A. and Elawady, W. M., “Adaptive fuzzy sliding mode control using supervisory fuzzy control for 3 DOF planar robot manipulators,” Appl. Soft Comput. 11(8), 49434953 (2011).CrossRefGoogle Scholar
Ouyang, P. R., Acob, J. and Pano, V., “PD with sliding mode control for trajectory tracking of robotic system,” Rob. Comput. Integr. Manuf. 30(2), 189200 (2014).CrossRefGoogle Scholar
Hosseyni, A., Trabelsi, R., Iqbal, A., Padmanaban, S. and F., M. Mimouni, “An improved sensorless sliding mode control/adaptive observer of a five-phase permanent magnet synchronous motor drive,” Int. J. Adv. Manuf. Technol. 93(1–4), 10291039 (2017).CrossRefGoogle Scholar
Fei, J., Fan, X., Dai, W., Shen, J. and Hua, M., “Robust tracking control of triaxial angular velocity sensors using adaptive sliding mode approach,” Int. J. Adv. Manuf. Technol. 52(5–8), 627636 (2011).CrossRefGoogle Scholar
Zhang, H., Dong, G., Zhou, M., Song, C., Huang, Y. and Du, K., “A new variable structure sliding mode control strategy for FTS in diamond-cutting microstructured surfaces,” Int. J. Adv. Manuf. Technol. 65(5–8), 11771184 (2013).CrossRefGoogle Scholar
Jin, M., Lee, J., Chang, P. H. and Choi, C., “Practical nonsingular terminal sliding-mode control of robot manipulators for high-accuracy tracking control,” IEEE Trans. Ind. Electron. 56(9), 35933601 (2009).Google Scholar
Zhao, Z., Gu, H., Zhang, J. and Ding, G., “Terminal sliding mode control based on super-twisting algorithm,” J. Syst. Eng. Electron. 28(1), 145150 (2017).CrossRefGoogle Scholar
Asl, R. M., Hagh, Y. S. and Palm, R., “Robust control by adaptive non-singular terminal sliding mode,” Eng. Appl. of Artif. Intell. 59, 205217 (2017).Google Scholar
Zhang, C., Lin, Z., Yang, S. X. and He, J., “Total-amount synchronous control based on terminal sliding-mode control,” IEEE Access 5, 54365444 (2017).CrossRefGoogle Scholar
Van, M., “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. 23(3), 13621371 (2018).CrossRefGoogle Scholar
Chen, G., Song, Y. and Guan, Y., “Terminal sliding mode-based consensus tracking control for networked uncertain mechanical systems on digraphs,” IEEE Trans. Neural Networks Learn. Syst. 29(3), 749756 (2018).CrossRefGoogle ScholarPubMed
Yang, Y., Chen, H. and Zhang, L., “Nonsingular Terminal Sliding-Mode Control for Nonlinear Robot Manipulators with Uncertain Parameters,” 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, China (2015) pp. 12271232.CrossRefGoogle Scholar
Ma, Z. and Sun, G., “Dual terminal sliding mode control design for rigid robotic manipulator,” J. Franklin Inst. 355(18), 91279149 (2018).CrossRefGoogle Scholar
Chen, Y., Petras, I. and Xue, D., “Fractional Order Control – A Tutorial,” American Control Conference, 2009. ACC’09, St. Louis, MO (2009) pp. 13971411.CrossRefGoogle Scholar
Efe, M. Ö., “Fractional fuzzy adaptive sliding-mode control of a 2-DOF direct-drive robot arm,” IEEE Trans. Syst. Man Cybern. Part B (Cybern). 38(6), 15611570 (2008).CrossRefGoogle ScholarPubMed
Rahmani, M., Ghanbari, A. and M., M. Ettefagh, “Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator,” Mech. Syst. Sig. Process. 80, 117136 (2016).CrossRefGoogle Scholar
Sun, G., Ma, Z. and Yu, J., “Discrete-time fractional order terminal sliding mode tracking control for linear motor,” IEEE Trans. Ind. Electron. 65(4), 33863394 (2018).CrossRefGoogle Scholar
Sun, G., Wu, L., Kuang, Z., Ma, Z. and Liu, J., “Practical tracking control of linear motor via fractional-order sliding mode,” Automatica 94, 221235 (2018).CrossRefGoogle Scholar
Rekanos, I. T. and Yioultsis, T. V., “Approximation of Grünwald–Letnikov fractional derivative for FDTD modeling of Cole–Cole media,” IEEE Trans. Magn. 50(2), 181184 (2014).CrossRefGoogle Scholar
Mirjalili, S., Mirjalili, S. M. and Lewis, A., “Grey wolf optimizer,” Adv. Eng. Softw. 69, 4661 (2014).CrossRefGoogle Scholar
Razmjooy, N., Ramezani, M. and Namadchian, A., “A new lqr optimal control for a single-link flexible joint robot manipulator based on grey wolf optimizer,” Majlesi J. Electr. Eng. 10(3), 53 (2016).Google Scholar
Sanjay, R., Jayabarathi, T., Raghunathan, T., Ramesh, V. and Mithulananthan, N., “Optimal allocation of distributed generation using hybrid grey wolf optimizer,” IEEE Access 5, 1480714818 (2017).CrossRefGoogle Scholar
Li, L., Sun, L., Kang, W., Guo, J., Han, C. and Li, S., “Fuzzy multilevel image thresholding based on modified discrete grey wolf optimizer and local information aggregation,” IEEE Access 4(4), 64386450 (2016).CrossRefGoogle Scholar
Zhao, L. and Wang, X., “A deep feature optimization fusion method for extracting bearing degradation features,” IEEE Access 6, 1964019653 (2018).CrossRefGoogle Scholar