A PSO-TUNED FUZZY LOGIC SYSTEM FOR POSITION TRACKING OF MOBILE ROBOT

Chaojiong Huang, Umar Farooq, Haiying Liu, Jason Gu, and Jun Luo

References

  1. [1] S.S. Ge, Autonomous mobile robots: Sensing, control, decisionmaking and applications, vol. 22 (Boca Raton, FL, USA: CRCPress, 2006).
  2. [2] M. Lepetiˇc, G. Klanˇcar, I. ˇSkrjanc, D. Matko, and B. Potoˇcnik,Path planning and path tracking for nonholonomic robots, inJohn X. Liu (ed.), Mobile robots: New research, (New York:Nova Science, 2005), 341–364.
  3. [3] S.T. Brassai, B. Iantovics, and C. En˘achescu, Artificial intelligence in the path planning optimization of mobile agentnavigation, Procedia Economics and Finance, 3, 2012, 243–250.
  4. [4] L. Ssebazza and Y.-J. Pan, DGPS-based localization andpath following approach for outdoor wheeled mobile robots,International Journal of Robotics and Automation, 30(1), 2015,13–25.
  5. [5] M. Amoozgar and Y. Zhang, Trajectory tracking of wheeledmobile robots: A kinematical approach, 2012 IEEE/ASME Int.Conf. Mechatronics and Embedded Systems and Applications(MESA), IEEE, Suzhou, China, 2012, 275–280.
  6. [6] G. Klanˇcar and I. ˇSkrjanc, Tracking-error model-based predictive control for mobile robots in real time, Robotics andautonomous systems, 55(6), 2007, 460–469.
  7. [7] Y. Tan and A. Chen, Fuzzy tracking control of wheeled mobilerobot, Manufacturing Automation, 31(2), 2009, 55–58.
  8. [8] H. Wu and X. Ren, An optimized fuzzy control approach to thetrajectory tracking of nonholonomic mobile robots, Journal ofSouth China University of Technology (Natural Science), 3,2004, 010.
  9. [9] L. Amador-Angulo, O. Castillo, and M. Pulido, Comparisonof fuzzy controllers for the water tank with type-1 and type-2fuzzy logic, 2013 Joint IFSA World Congress and NAFIPSAnnual Meeting (IFSA/NAFIPS), Edmonton, Canada, 2013,1062–1067.
  10. [10] L. Amador-Angulo and O. Castillo, Comparison of the optimal design of fuzzy controllers for the water tank using antcolony optimization, in O. Castillo, P. Melin, W. Pedrycz &J. Kacprzyk (eds.), Recent Advances on Hybrid Approachesfor Designing Intelligent Systems, 2014, 255–273.
  11. [11] L. Amador-Angulo and O. Castillo, A fuzzy bee colony optimization algorithm using an interval type-2 fuzzy logic systemfor trajectory control of a mobile robot, Mexican Int. Conf.on Artificial Intelligence, Cuernavaca, Mexico, 2015, 460–471.
  12. [12] L. Amador-Angulo and O. Castillo, Statistical analysis oftype-1 and interval type-2 fuzzy logic in dynamic parameteradaptation of the BCO, IFSA-EUSFLAT, Gij´on, Spain, 2015,776–783.
  13. [13] M.A. Sanchez, O. Castillo, and J.R. Castro, Generalized type-2fuzzy systems for controlling a mobile robot and a performancecomparison with interval type-2 and type-1 fuzzy systems,Expert Systems with Applications, 42(14), 2015, 5904–5914.
  14. [14] A. Celikyilmaz and I. B. Turksen, Enhanced fuzzy systemmodels with improved fuzzy clustering algorithm, IEEE Trans-actions on Fuzzy Systems, 16(3), 2008, 779–794.
  15. [15] W. Chen and M. Saif, A novel fuzzy system with dynamicrule base, IEEE Transactions on Fuzzy Systems, 13(5), 2005,569–582.
  16. [16] M.G. Hinchey, R. Sterritt, and C. Rouff, Swarms and swarmintelligence, Computer, 40(4), 2007, 111–113.
  17. [17] C.-H. Cheng and J.-X. Ye, Ga-based neural network for energyrecovery system of the electric motorcycle, Expert Systems withApplications, 38(4), 2011, 3034–3039.
  18. [18] Y.-J. Gong, M. Shen, J. Zhang, O. Kaynak, W.-N. Chen,and Z.-H. Zhan, Optimizing rfid network planning by using aparticle swarm optimization algorithm with redundant readerelimination, IEEE Transactions on Industrial Informatics,8(4), 2012, 900–912.
  19. [19] S.Yılmaz and E.U. K¨u¸c¨uksille, A new modification approachon bat algorithm for solving optimization problems, AppliedSoft Computing, 28, 2015, 259–275.
  20. [20] L. Wang, C. Luo, M. Li, and J. Cai, Trajectory planning ofan autonomous mobile robot by evolving ant colony system,International Journal of Robotics and Automation, 32(4), 2017,406–413.
  21. [21] H. Pham Huy Anh, Novel fuzzy narx imc control of miso dynamic system using particle swarm optimization, InternationalJournal of Robotics and Automation, 29(2), 2014, 133–143.
  22. [22] O. Castillo, H. Neyoy, J. Soria, P. Melin, and F. Valdez, Anew approach for dynamic fuzzy logic parameter tuning in antcolony optimization and its application in fuzzy control of amobile robot, Applied Soft Computing, 28, 2015, 150–159.
  23. [23] S. Mirjalili, S.M. Mirjalili, and A. Lewis, Grey wolf optimizer,Advances in Engineering Software, 69, 2014, 46–61.
  24. [24] E. Emary, H.M. Zawbaa, and A.E. Hassanien, Binary grey wolfoptimization approaches for feature selection, Neurocomputing,172, 2016, 371–381.
  25. [25] R. Malviya and D.K. Pratihar, Tuning of neural networks usingparticle swarm optimization to model MIG welding process,Swarm and Evolutionary Computation, 1(4), 2011, 223–235.
  26. [26] M. Maitra and A. Chatterjee, A hybrid cooperative–comprehensive learning based pso algorithm for image seg-mentation using multilevel thresholding, Expert Systems withApplications, 34(2), 2008, 1341–1350.
  27. [27] P. Acharjee and S. Goswami, Expert algorithm based onadaptive particle swarm optimization for power flow analysis,Expert Systems with Applications, 36(3), 2009, 5151–5156.
  28. [28] H.-Y. Chung, C.-C. Hou, and S.-C. Liu, Automatic navigationof a wheeled mobile robot using particle swarm optimizationand fuzzy control, 2013 IEEE International Symposium onIndustrial Electronics (ISIE), Taipei, Taiwan, 2013, 1–6.
  29. [29] F. Valdez, P. Melin, and O. Castillo, Fuzzy control of parameters to dynamically adapt the PSO and GA algorithms, 2010IEEE Int. Conf. Fuzzy Systems (FUZZ), Barcelona, Spain,2010, 1–8.
  30. [30] M. R. Bonyadi and Z. Michalewicz, Particle swarm optimizationfor single objective continuous space problems: A review,Evolutionary Computation, 25(1), 2017, 1–54.
  31. [31] C.W. Cleghorn and A.P. Engelbrecht, Particle swarm variants:Standardized convergence analysis, Swarm Intelligence, 9(2–3),2015, 177–203.
  32. [32] J.-R. Zhang, J. Zhang, T.-M. Lok, and M.R. Lyu, A hybridparticle swarm optimization–back-propagation algorithm forfeedforward neural network training, Applied Mathematics andComputation, 185(2), 2007, 1026–1037.
  33. [33] O. Castillo, R. Mart´ınez-Marroqu´ın, P. Melin, F. Valdez, andJ. Soria, Comparative study of bio-inspired algorithms appliedto the optimization of type-1 and type-2 fuzzy controllers foran autonomous mobile robot, Information Sciences, 192, 2012,19–38.
  34. [34] Z.T. Allawi and T.Y. Abdalla, A pso-optimized type-2 fuzzylogic controller for navigation of multiple mobile robots, 201419th Int. Conf. on Methods and Models in Automation andRobotics (MMAR), Miedzyzdroje, Poland, 2014, 33–39.
  35. [35] A. Chatterjee, R. Chatterjee, F. Matsuno, and T. Endo,Neuro-fuzzy state modeling of flexible robotic arm employingdynamically varying cognitive and social component basedPSO, Measurement, 40(6), 2007, 628–643.
  36. [36] A. Chatterjee and P. Siarry, A PSO-aided neuro-fuzzy classifier employing linguistic hedge concepts, Expert Systems withApplications, 33(4), 2007, 1097–1109.
  37. [37] R. Poli, Analysis of the publications on the applications ofparticle swarm optimisation, Journal of Artificial Evolutionand Applications, New York, NY, USA: Hindawi PublishingCorp, vol. 2008, no. 1(3), 2008.
  38. [38] L. Zhao, F. Qian, Y. Yang, Y. Zeng, and H. Su, Automaticallyextracting t–s fuzzy models using cooperative random learningparticle swarm optimization, Applied soft computing, 10(3),2010, 938–944.
  39. [39] A. Khosla, S. Kumar, and K. Aggarwal, A framework for identification of fuzzy models through particle swarm optimizationalgorithm, in 2005 Annual IEEE INDICON, Chennai, India,2005, 388–391.
  40. [40] K. Scheerlinck, H. Vernieuwe, and B. De Baets, Zadehs extension principle for continuous functions of non-interactive variables: A parallel optimization approach, IEEE Transactionson Fuzzy Systems, 20, (1), 2012, 96–108.
  41. [41] O. Castillo and P. Melin, Optimization of type-2 fuzzy systemsbased on bio-inspired methods: A concise review, InformationSciences, 205, 2012, 1–19.
  42. [42] C.-C. Chen, A PSO-based method for extracting fuzzy rulesdirectly from numerical data, Cybernetics and Systems: AnInternational Journal, 37(7), 2006, 707–723.
  43. [43] M.-S. Leu and M.-F. Yeh, Grey particle swarm optimization,Applied Soft Computing, 12(9), 2012, 2985–2996.
  44. [44] Z.-H. Zhan, J. Zhang, Y. Li, and H.S.-H. Chung, Adaptiveparticle swarm optimization, IEEE Transactions on Systems,Man, and Cybernetics, Part B (Cybernetics), 39(6), 2009,1362–1381.
  45. [45] D. Bratton and J. Kennedy, Defining a standard for particleswarm optimization, Swarm Intelligence Symposium, 2007(SIS 2007), IEEE, 2007, 120–127.
  46. [46] G.G. Yen and W.F. Leong, Dynamic multiple swarms in multi-objective particle swarm optimization, IEEE Transactions onSystems, Man, and Cybernetics-Part A: Systems and Humans,39(4), 2009, 890–911.
  47. [47] A.P. Engelbrecht, Scalability of a heterogeneous particle swarmoptimizer, 2011 IEEE Symp. on Swarm Intelligence (SIS),Paris, France, 2011, 1–8.
  48. [48] N.J. Cheung, X.-M. Ding, and H.-B. Shen, Optifel: A conver-gent heterogeneous particle swarm optimization algorithm forTakagi–Sugeno fuzzy modeling, IEEE Transactions on FuzzySystems, 22(4), 2014, 919–933.
  49. [49] D.N. Wilke, S. Kok, and A.A. Groenwold, Comparison oflinear and classical velocity update rules in particle swarm op-timization: Notes on scale and frame invariance, InternationalJournal for Numerical Methods in Engineering, 70(8), 2007,985–1008.
  50. [50] M. Bonyadi, X. Li, and Z. Michalewicz, A hybrid particle swarmwith velocity mutation for constraint optimization problems, inProc. 15th Ann. Conf. Genetic and Evolutionary Computation,Amsterdam, The Netherlands, 2013, 1–8.
  51. [51] M.R. Bonyadi and Z. Michalewicz, SPSO 2011: Analysis ofstability, local convergence, and rotation sensitivity, Proc.2014 Ann. Conf. on Genetic and Evolutionary Computation,Vancouver, BC, Canada, 2014, 9–16.

Important Links:

Go Back