Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-14T07:01:39.439Z Has data issue: false hasContentIssue false
  • English
  • Français

Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

Published online by Cambridge University Press:  18 November 2019

Rupam Gupta Roy Open the ORCID record for Rupam Gupta Roy [Opens in a new window]  and
Dibyendu Ghoshal
Rupam Gupta Roy*
Affiliation:
Department of Electronics and Instrumentation Engineering, National Institute of Technology, Agartala, Jirania, Tripura, India
Dibyendu Ghoshal
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology, Agartala, Jirania, Tripura, India, E-mail: tukumw@gmail.com
*
*Corresponding author. E-mail: rupamguptaroy05@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

The flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). It portrays the valuable comparative analysis by measuring the accomplished joint angles, error, and response of the controller. Thus the proposed method discovers the supervisory controller for the inchworm robot that is immensely better than conventional controllers mentioned earlier.

Keywords

Inchworm robotControllerSMCSoSMCGrey Wolf Optimization
Type
Articles
Information
Robotica , Volume 38 , Issue 9 , September 2020 , pp. 1539 - 1557
Copyright
© Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Rahmani, M., Ghanbari, A. and Ettefagh, M. M., “Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator,” Mech. Syst. Signal Process. 80, 117–136 (2016).10.1016/j.ymssp.2016.04.004CrossRefGoogle Scholar
Yang, Y. and Yan, Y., “Neural network approximation-based nonsingular terminal sliding mode control for trajectory tracking of robotic airships,” Aerosp. Sci. Technol. 54 192–197 (2016).CrossRefGoogle Scholar
Xu, G., Liu, F., Xiu, C., Sun, L. and Liu, C., “Optimization of hysteretic chaotic neural network based on fuzzy sliding mode control,” Neurocomputing 189, 72–79, (2016).CrossRefGoogle Scholar
Ghanbari, A., Rostami, A., Noorani, S. M. R. S. and Fakhrabadi, M. M. S., “Modeling and Simulation of Inchworm Mode Locomotion,” International Conference on Intelligent Robotics and Applications, Berlin, Heidelberg (2008) pp. 617–624.10.1007/978-3-540-88513-9_66CrossRefGoogle Scholar
Fang, Y., Fei, J. and Ma, K., “Model reference adaptive sliding mode control using RBF neural network for active power filter,” Int. J. Elec. Pow. Energy Syst. 73, 249–258, (2015).CrossRefGoogle Scholar
Xiao, T. and Li, H.–X., “Sliding mode control design for a rapid thermal processing system,” Chem. Eng. Sci. 143, 76–85 (2016).CrossRefGoogle Scholar
Zhang, J., Shi, P. and Lin, W., “Extended sliding mode observer based control for Markovian jump linear systems with disturbances,” Automatica 70, 140–147 (2016).CrossRefGoogle Scholar
Levant, A. and Livne, M., “Weighted homogeneity and robustness of sliding mode control,” Automatica 72, 186–193 (2016).CrossRefGoogle Scholar
Wang, L., Chai, T. and Zhai, L., “Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics,” IEEE Trans. Ind. Electron. 56(9), 3296–3304 (2009).CrossRefGoogle Scholar
Lee, H., Nam, D. and Park, C. H., “A Sliding Mode Controller Using Neural Networks for Robot Manipulator,” European Symposium on Artificial Neural Networks, Bruges (2004) pp. 193–198.Google Scholar
Shtessel, Y., Edwards, C., Fridman, L. and Levant, A., “Introduction: Intuitive Theory of Sliding Mode Control,” In: Sliding Mode Control and Observation (Levine, W. S., ed.) (Birkhäuser, New York, 2014) pp. 1–42.CrossRefGoogle Scholar
d’Armi, P., “A quick introduction to sliding mode control and its applications,” Universita’ Degli Studi Di Cagliari, pp. 1–22.Google Scholar
Ma, Z. and Sun, G., “Adaptive sliding mode control of tethered satellite deployment with input limitation,” Acta Astronautica 127, 67–75 (2016).CrossRefGoogle Scholar
Song, Y.-D., Lu, Y. and Gan, Z.-X., “Descriptor sliding mode approach for fault/noise reconstruction and fault-tolerant control of nonlinear uncertain systems,” Inform. Sci. 367–368, 194–208 (2016).CrossRefGoogle Scholar
Qiu, Z.-C. and Zhang, S. M., “Fuzzy fast terminal sliding mode vibration control of a two-connected flexible plate using laser sensors,” J. Sound Vib. 380, 51–77 (2016).10.1016/j.jsv.2016.06.002CrossRefGoogle Scholar
Cui, R., Zhang, X. and Cui, D., “Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities,” Ocean Eng. 123, 45–54 (2016).CrossRefGoogle Scholar
Fang, Y., Chow, T. W. S. and Li, X. D., “Use of a recurrent neural network in discrete sliding-mode control,” IEEE Proc. Control Theory Appl. 146(1), 84–90 (2002).CrossRefGoogle Scholar
Wu, L., Gao, H., Wang, C., “Quasi sliding mode control of differential linear repetitive processes with unknown input disturbance,” IEEE Trans. Ind. Electron. 58(7), 3059–3068 (2011).CrossRefGoogle Scholar
Bandyopadhyay, B., Gandhi, P. S. and Kurode, S., “Sliding mode observer based sliding mode controller for slosh-free motion through PID scheme,” IEEE Trans. Ind. Electron. 56(9), 3432–3442 (2009).CrossRefGoogle Scholar
Chen, X. and Hisayama, T., “Adaptive sliding-mode position control for piezo-actuated stage,” IEEE Trans. Ind. Electron. 55(11), 3927–3934 (2009).CrossRefGoogle Scholar
Mohammadzadeh, A. and Ghaemi, S., “A modified sliding mode approach for synchronization of fractional-order chaotic/hyperchaotic systems by using new self-structuring hierarchical type-2 fuzzy neural network,” Neurocomputing 191, 200–213 (2016).CrossRefGoogle Scholar
Fnaiech, M. A., Betin, F., Capolino, G.-A. and Fnaiech, F., “Fuzzy logic and sliding-mode controls applied to six-phase induction machine with open phases,” IEEE Trans. Ind. Electron. 57(1), 354–364 (2009).CrossRefGoogle Scholar
Pukdeboon, C., Zinober, A. S. I. and Thein, M.-W. L., “Quasi-continuous higher order sliding-mode controllers for spacecraft-attitude-tracking maneuvers,” IEEE Trans. Ind. Electron. 57(4), 1436–1444, (2010).CrossRefGoogle Scholar
Song, S., Zhang, X. and Tan, Z., “RBF neural network based sliding mode control of a lower limb Exoskeleton suit,” J. Mech. Eng. 60(6), 437–446 (2014).CrossRefGoogle Scholar
Hussain, M. A. and Ho, P. Y., “Adaptive sliding mode control with neural network based hybrid models,” J. Process Cont. 14(2), 157–176 (2004).CrossRefGoogle Scholar
Narendra, K. S. and Parthasarathy, K., “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Net. 1(1), 4–27 (2002).CrossRefGoogle Scholar
Hussain, M. A., “Review of the applications of neural networks in chemical process control-simulation and online implementation,” Artif. Intel. Eng. 13(1), 55–68 (1998).CrossRefGoogle Scholar
Hussain, M. A. and Kershenbaum, L. S., “Implementation of an inverse-model-based control strategy using neural networks on a partially simulated exothermic reactor,” Chem. Eng. Res. Design 78(2), 299–311 (2000).CrossRefGoogle Scholar
Narendra, K. S. and Lewis, F. L., “Introduction to the special issue on neural network feedback control,” Automatica 37(8), 1147–1148 (2001).CrossRefGoogle Scholar
Lennox, B., Montague, G. A., Frith, A. M., Gent, C. and Bevan, V., “Industrial application of neural networks – an investigation,” J. Process Cont. 11(5), 497–507 (2001).CrossRefGoogle Scholar
David, J. B., “Experiments on neural net recognition of spoken and written text,” IEEE Trans. Acoust. Speech Signal Process. 36(7), 1162–1168 (2002).Google Scholar
Gorman, R. P. and Sejnowski, T. J., “Learned classification of sonar targets using a massively parallel network,” IEEE Trans. Acoust. Speech Signal Process. 36(7), 1135–1140 (2002).CrossRefGoogle Scholar
Widrow, B., Winter, R. G. and Baxter, R. A., “Layered neural nets for pattern recognition,” IEEE Trans. Acoust. Speech Signal Process. 36(7), 1109–1118 (2002).CrossRefGoogle Scholar
Hopfield, J. J., “Neural networks and physical systems with emergent collective computational abilities,” Proc. Nat. Acad. Sci. USA 79(8), 2554–2558 (1982).CrossRefGoogle ScholarPubMed
Tank, D. and Hopfield, J., “Simple ‘neural’ optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit,” IEEE Trans. Circuits Syst. 33(5), 533–541, (2003).CrossRefGoogle Scholar
Rauch, H. E. and Winarske, T., “Neural networks for routing communication traffic,” IEEE Control Systems Magazine 8(2), 26–31, (2002).10.1109/37.1870CrossRefGoogle Scholar
Zhao, P., Shi, Y. and Huang, J., “Proportional-integral based fuzzy sliding mode control of the milling head,” Control Eng. Prac. 53, 1–13, (2016).CrossRefGoogle Scholar
Nayak, J., Naik, B. and Behera, H. S., “A novel nature inspired firefly algorithm with higher order neural network – performance analysis,” Eng. Sci. Technol. Int. J. 19(1), 197–211 (2016).Google Scholar
Balachennaiah, P., Suryakalavathi, M. and Nagendra, P., “Optimizing real power loss and voltage stability limit of a large transmission network using firefly algorithm,” Eng. Sci. Technol. Int. J. 19(2), 800–810 (2016).Google Scholar
Mirjalili, S., Mirjalili, S. M. and Lewis, A., “Grey wolf optimizer,” Adv. Eng. Softw. 69, 46–61 (2014).CrossRefGoogle Scholar
Ye, Y., “Attitude regulation for unmanned quadrotors using adaptive fuzzy gain-scheduling sliding mode control,” Aerosp. Sci. Technol. 54, 208–217 (2016).Google Scholar
Ye, Y., “Backstepping sliding mode control for uncertain strictfeedback nonlinear systems using neural-network-based adaptive gain scheduling,” J. Syst. Eng. Electron. 29(3), 580–586 (2018).Google Scholar
Yang, Y., “A time-specified nonsingular terminal sliding mode control approach for trajectory tracking of robotic airships,” Nonlinear Dynam. 92(3), 1359–1367 (2018).CrossRefGoogle Scholar
Mefoued, S., “A second order sliding mode control and a neural network to drive a knee joint actuated orthosis,” Neurocomputing 155, 71–79 (2015).CrossRefGoogle Scholar
Ponticorvo, M., Rega, A., Di Ferdinando, A., Marocco, D. and Miglino, O., “Approaches to Embed Bio-inspired Computational Algorithms in Educational and Serious Games,” Proceedings of the 1st International Workshop on Cognition and Artificial Intelligence for Human-Centred Design, 2099, 21–26 (2017).Google Scholar
Singh, G., Jain, V. K. and Singh, A., “Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system,” J. Energy Environ. 29(7), 1–25 (2018).Google Scholar
Phong, N. T., Phuc, V. N. and Quyen, T. T. H. L. N., “Application of fuzzy analytic network process and topsis method for material supplier selection,” Key Eng. Mat. 728, 411–415 (2017).CrossRefGoogle Scholar
Morerira, M. G., Ferraz, G. A. S., Barbosa, B. D. S., Iwasaki, E. M., Ferraz, P. F. P., Damasceno, F. A. and Rossi, G., “Design and construction of a low-cost remotely piloted aircraft for precision agriculture applications,” Agron. Res. 17, 1–8 (2019).Google Scholar
Nguyen, P. T., Vo, K. D., Phan, P. T., Nguyen, T. A., Cao, T. M., Huynh, V. D. B., Nguyen, Q. L. H. T. T. and Le, L. P., “A hybrid multi criteria decision analysis for engineering project manager evaluation,” Int. J. Ad. Appl. Sci. 4(4), 49–52 (2017).Google Scholar
Marotkar, D. S., Zade, P. and Kapur, V., “Design of microstrip patch antenna with asymmetric sai shape DGS for bandwidth enhancement,” Appl. Electrom. Conf. IEEE. 1–2 (2015).CrossRefGoogle Scholar
Sherifi, I. and Senja, E., “Internet usage on mobile devices and their impact on evolution of informative websites in Albania,” Eur. J. Business Econ. Account. 3(6), 37–43 (2018).Google Scholar