Fuzzy synergetic control for dynamic car-like mobile robot

• Autorzy: Bouhamatou Zoulikha , Abedssemed Foudil

Identyfikatory

DOI

10.2478/ama-2022-0007

• Języki publikacji: EN

Abstrakty

EN

This paper aims to present the dynamic control of a Car-like Mobile Robot (CLMR) using Synergetic Control (SC). The SC control is used to make the linear velocity and steering velocity converge to references. Lyapunov synthesis is adopted to assure controlled system stability. To find the optimised parameters of the SC, the grey wolf optimiser (GWO) algorithm is used. These parameters depend on the best-selected fitness function. Four fitness functions are selected for this purpose, which is based on the integral of the error square (ISE), the integral of the square of the time-weighted error (ITSE), the integral of the error absolute (IAE) and the integral of the absolute of the time-weighted error (TIAE) criterion. To go further in the investigation, fuzzy logic type 2 is used to get at each iteration the appropri-ate controller parameters that give the best performances and robustness. Simulations results are conducted to show the feasibility and efficiency of the proposed control methods.

Słowa kluczowe

EN

CLMR; synergetic control; lyapunov stability; GWO; fuzzy logic type 2

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2022
• Tom: Vol. 16, no. 1
• Strony: 48--57
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Bouhamatou Zoulikha

• Department of Electronics, Faculty of Technology, University Mostefa Ben Boulaïd, Batna2, Algeria

• https://orcid.org/0000-0002-3985-0147

autor

Abedssemed Foudil

• Department of Electronics, Faculty of Technology, University Mostefa Ben Boulaïd, Batna2, Algeria

• https://orcid.org/0000-0003-0935-3147

Bibliografia

• 1. Ahifar A, Ranjbar AN, Rahmani Z. Finite Time Terminal Synergetic Controller for Nonlinear Helicopter Model. 2019; 32(2):236–241.
• 2. Benaziza W, Slimane N, Mallem A. Disturbances elimination with fuzzy sliding mode control for mobile robot trajectory tracking. Ad-vances in Electrical and Electronic Engineering. 2018; 16(3):297–310. https://doi.org/10.15598/aeee.v16i3.2767
• 3. Bhattacharyya S, Shimoda S, Hayashibe M. A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control. IEEE Transactions on Systems Man and Cybernetics: Systems 2016; 46(7):957–968. https://doi.org/10.1109/TSMC.2016.2560532
• 4. Dung NM, Duy VH, Phuong NT, Kim SB, Oh MS. Two-wheeled welding mobile robot for tracking a smooth curved welding path using adaptive sliding-mode control technique. International Journal of Control Automation and Systems. 2007; 5(3):283–294.
• 5. Elhariri E, El-Bendary N, Hassanien AE, Abraham A. Grey wolf optimization for one-against-one multi-class support vector ma-chines. 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR). 2015; 7–12. https://doi.org/10.1109/SOCPAR.2015.7492781
• 6. Gupta S, Deep K. Cauchy Grey Wolf Optimiser for continuous opti-misation problems. Journal of Experimental & Theoretical Artificial In-telligence. 2018; 30(6):1051–1075 https://doi.org/10.1080/0952813X.2018.1513080.
• 7. Humaidi AJ, Ibraheem IK, Azar AT, Sadiq ME. A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles. Entropy. 2020; 22(7). https://doi.org/10.3390/e22070723
• 8. Ibrahim AEB. Wheeled Mobile Robot Trajectory Tracking using Sliding Mode Control. 2016. https://doi.org/10.3844/jcssp.2016.48.55
• 9. Kamalova A, Navruzov S, Qian D, Lee SG. Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer. Applied Sciences. 2019; 9(14). https://doi.org/10.3390/app9142931
• 10. Kolesnikov A, Veselov G, Kolesnikov A. Modern applied control theory: synergetic approach in control theory. TRTU Moscow Tagan-rog. 2000; 4477–4479.
• 11. Liu CH, Hsiao MY. A finite time synergetic control scheme for robot manipulators. Computers and Mathematics with Applications. 2012; 64(5):1163–1169. https://doi.org/10.1016/j.camwa.2012.03.058
• 12. Mallem A, Slimane N, Benaziza W. Dynamic Control of Mobile Robot Using RBF Global Fast Sliding mode. IAES International Journal of Robotics and Automation (IJRA). 2018; 7(3):159. https://doi.org/10.11591/ijra.v7i3.pp159-168
• 13. Mesquita EDEM, Sampaio RC, Vicente H, Ayala H, Llanos CH. Recent Meta-Heuristics Improved by Self-Adaptation Applied to Non-linear Model-Based Predictive Control. 2020; 118841–118852.
• 14. Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Advances in Engineering Software. 2014; 69:46–61.https://doi.org/https://doi.org/10.1016/j.advengsoft.2013.12.007
• 15. Mittal N, Singh U, Sohi BS. Modified Grey Wolf Optimizer for Global Engineering Optimization. Applied Computational Intelligence and Soft Computing. 2016; 1–16. https://doi.org/10.1155/2016/7950348
• 16. Peng S, Shi W. Adaptive fuzzy integral terminal sliding mode control of a nonholonomic wheeled mobile robot. Mathematical Problems in Engineering. 2017. https://doi.org/10.1155/2017/3671846
• 17. Podvalny SL, Vasiljev EM. Synergetic control of UAV on the basis of multi-alternative principles. International Russian Automation Confer-ence RusAutoCon. 2018; 1–6. https://doi.org/10.1109/RUSAUTOCON.2018.8501727
• 18. Sklyarov AA, Veselov GE, Sklyarov SA, Pohilina TE. Synthesis of the synergetic control law of the transport robotic platform. Proceedings of 2017 IEEE 2nd International Conference on Control in Technical Systems CTS. 2017; 285–288. https://doi.org/10.1109/CTSYS.2017.8109547
• 19. Veselov G, Sklyrov A, Mushenko A, Sklyrov S. Synergetic Control of a Mobile Robot Group. Proceedings - 2nd International Conference on Artificial Intelligence Modelling and Simulation AIMS. 2014; 155–160. https://doi.org/10.1109/AIMS.2014.22
• 20. Yeh YC, Li THS., Chen CY. Adaptive fuzzy sliding-mode control of dynamic model based car-like mobile robot. International Journal of Fuzzy Systems. 2009; 11(4):272–286. https://doi.org/10.30000/IJFS.200912.0006.