International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 11, No. 11, 8 Nov. 2019
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Intelligent Control for a Swarm of Two Wheel Mobile Robot with Presence of External Disturbance
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Author(s)
Index Terms
PSO, particle swarm optimization, GA, genetic algorithms, IAE, integral absolute error
Abstract
This paper proposed an optimization algorithm in order to improve path maintaining of swarm of two wheel mobile robots with presence of external disturbance. The three robots forms use the leader-follower strategy, the best path for leader is determined using A* algorithm ,the other two robots follow the leader path. Two PID controller are used in each robot to control the angular and velocity torque of wheel. Each PID controller is tuned using intelligent optimization control method which are Particle swarm optimization ,random occurring distributed time delay particle swarm optimization and hybrid particle swarm optimization and genetic after that the proposed algorithm is used for tuning. The new algorithm is the contribution of this article. It is built by combine the random occurring distributed time delayed and genetic algorithm .The combination of these two algorithms takes the advantage of them by using the historical best global position of particles in random occurring distributed time delayed particle swarm optimization algorithm to update velocity of new population generated by genetic algorithm. The integral absolute error (IAE) is computed for system in each algorithm for comparison between them. The performance of intelligent control systems for controlling the three robots path is tested with presence of external disturbance in environment .Two type of external disturbance is tested, these are constant external disturbance and dynamic external disturbance. The performance of the same optimization algorithm is tested in pure environments. From the obtained result ,the new combination method is the best in both disturbance environments (constant or dynamic) and pure.
Cite This Paper
Mehdi J. Marie, Safaa S.Mahdi, Esraa Y. Tarkan, " Intelligent Control for a Swarm of Two Wheel Mobile Robot with Presence of External Disturbance", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.11, pp. 7-12, 2019. DOI:10.5815/ijmecs.2019.11.02
Reference
[1]Andres F. Rivera , “Formation Control And Robustness Analysis Of Time-Delayed Agents “, MSC thesis , California State University , Long Beach, Department of Mechanical and Aerospace Engineering ,2019
[2]Dr.Ahmed Al-Araji ,Khulood E.Dagher, “Design of a Nonlinear PID Neural Trajectory Tracking Controller for Mobile Robot based on Optimization Algorithm”,Eng&Tech. Journal ,Vol.32,part(A),No.4.2014.
[3]Zhang, Y., Wang, S., & Ji, G. (2015). A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications. Mathematical Problems inEngineering,2015.
[4]https://www.researchgate.net/publication/266614526_Hybrid_Cooperative_Energy_Detection_Techniques_in_Cognitive_Radio_Networks
[5]J. C. Bansal,P.K.Singh 3Mukesh Saraswat, Abhishek Verma, Shimpi Singh Jadon,Ajith Abraham,”Inertia Weight Strategies in Particle Swarm Optimization”, Third World Congress on Nature and Biologically Inspired Computing,2011.
[6]A.Nikabadi,M.Ebadzadeh,“Particle swarm optimization algorithms with adaptive Inertia Weight : A survey of the state of the art and a Novel method”, IEEE journal of evolutionary computation , 2008
[7]Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation, 8(3), 240–255. https://doi.org/10.1109/TEVC.2004.826071
[8]Liu W., Wang Z., Liu X., Zeng N., Bell D, “A Novel Particle Swarm Optimization Approach for Patient Clustering from Emergency Departments”. IEEE Transactions on Evolutionary Computation,vol.23,2018.
[9]Ameer L. Saleh , Maab A. Hussain , Sahar M.Klim , “Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller”, Journal of University of Babylon, Engineering Sciences, Vol.(26), No.(4): 2018
[10]Zhang, J., Zhu, X., Wang, Y., & Zhou, M. (2019). Dual-Environmental Particle Swarm Optimizer in Noisy and Noise-Free Environments. IEEE Transactions on Cybernetics, 49(6), 2011–2021. https
[11]Ali, A. F., Tawhid, M. A. (2017). A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems. Ain Shams Engineering Journal, 8(2), 191–206. https://doi.org/10.1016/j.asej.2016.07.008
[12]Chen, Y. Q., & Wang, Z. (2005). Formation control: A review and a new consideration. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, (435), 3664–3669. https://doi.org/10.1109/IROS.2005.1545539
[13]McCall J.,”Genetic algorithms for modelling and optimization”, Journal of Computational and Applied Mathematics, vol.184(1), 2004.
[14]Chang Boon Low,”A flexible virtual structure formation keeping control design for nonholonomic mobile robots with low-level control systems”, IEEE Multi-conference on Systems and Control , Antibes, France ,October 8-10, 2014.
[15]Zhang J., Zhu X., Wang Y., Zhou M. ,” Dual-Environmental Particle Swarm Optimizer in Noisy and Noise-Free Environments”, IEEE Transactions on Cybernetics, 49(6), 2019.