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Warianty tytułu
Języki publikacji
Abstrakty
In this work the optimization process of the tracking and reactive controllers for a mobile robot are presented. The Chemical Reaction Algorithm (CRA) is used to find the optimal parameter values of the membership functions and rules for the reactive and tracking controllers. In this case, we are using five membership functions in each variable of the fuzzy controllers. The main goal of the reactive controller is aimed at providing the robot with the ability to avoid obstacles in its environment. The tests are performed on a benchmark maze problem, in which the goal is not necessarily to leave the maze, but rather that the robot avoids obstacles, in this case the walls, and penalizing for unwanted trajectories, such as cycles. The tracking controller’s goal is for the robot to keep into to a certain path, this in order that the robot can learn to react to unknown environments. The optimization algorithm that was used is based on an abstraction of chemical reactions. To perform the simulation we use the “SimRobot” toolbox, the results of the tests are presented in a detailed fashion, and at the end we are presenting a comparison of results among the CRA, PSO and GA methods.
Słowa kluczowe
Rocznik
Tom
Strony
10--19
Opis fizyczny
Bibliogr. 46 poz., rys.
Twórcy
autor
- Tijuana Institute of Technology, 22379, Tijuana, Mexico
autor
- Tijuana Institute of Technology, 22379, Tijuana, Mexico
autor
- Tijuana Institute of Technology, 22379, Tijuana, Mexico
Bibliografia
- 1. Amador-Angulo, L., Castillo, O., Comparison of the optimal design of fuzzy controllers for the water tank using ant colony optimization. Series: Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Springer International Publishing, 2014, 255–273. DOI: 10.1007/978-3-319-05170-3_18.
- 2. Amador-Angulo, L., Castillo, O., “A Fuzzy Bee Colony Optimization Algorithm Using an Interval Type-2 Fuzzy Logic System for Trajectory Control of a Mobile Robot”. In: Mexican International Conference on Artificial Intelligence, Springer International Publishing, October 2015. DOI: 10.1007/978-3-319-27060-9_38.
- 3. Amin S., Adriansyah A., “Particle Swarm Fuzzy Controller for Behavior-based Mobile Robot”. In: ICARCV’06. 9th International Conference on Control, Automation, Robotics and Vision, Dec. 2006, pp. 1, 6, 5–8. DOI: 10.1109/ICARCV.2006.345293.
- 4. Astudillo L., Castillo O., Aguilar L., R. Martínez, “Hybrid Control for an Autonomous Wheeled Mobile Robot Under Perturbed Torques”. In: IFSA (1) 2007, chapter 59, 594–603. DOI: 10.1007/978-3-540-72950-1_59.
- 5. Astudillo L., Castillo O., Aguilar L., “Intelligent Control of an Autonomous Mobile Robot using Type-2 Fuzzy Logic”. In: IC-AI 2006, 565–570.
- 6. Astudillo, L., Melin, P., Castillo, O., “A new optimization method based on a paradigm inspired by nature”. In: Soft Computing for Recognition Based on Biometrics, Springer Berlin-Heidelberg, 2010, 277–283.
- 7. Astudillo, L., Melin, P., Castillo, O., “Nature inspired chemical optimization to design a type-2 fuzzy controller for a mobile robot”. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013, 1423–1428. DOI: 10.1109/IFSA-NAFIPS.2013.6608610.
- 8. Caraveo, C., Valdez, F., Castillo, O., “Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation”, Applied Soft Computing, vol. 43, 2016, 131–142.DOI: 10.1016/j.asoc.2016.02.033.
- 9. Cardenas S., Garibaldi J., Aguilar L., Castillo O, “Intelligent Planning and Control of Robots Using Genetic Algorithms and Fuzzy Logic”. IC-AI, 2005, 412–418.
- 10. Castillo O., Martinez R., Melin P., Valdez F., Soria J., “Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot”, Information Sciences, vol. 192, 2012, 19–38. DOI: 10.1016/j.ins.2010.02.022.
- 11. Cervantes L., Castillo O., “Design of a Fuzzy System for the Longitudinal Control of an F-14 Airplane”. In: Soft Computing for Intelligent Control and Mobile Robotics, 2011, 213–224. DOI: 10.1007/978-3-642-15534-5_13.
- 12. de la O D., Castillo O., Soria J., “Optimization of Reactive Control for Mobile Robots Based on the CRA Using Type-2 Fuzzy Logic”. In: de la O., Nature-Inspired Design of Hybrid Intelligent Systems, chapter 33, 2017, 505–518. DOI: 10.1007/978-3-319-47054-2_33.
- 13. de la O D., Castillo O., Melendez A., Astudillo L., “Optimization of a reactive controller for mobile robots based on CRA”. In: Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015 Annual Conference of the North American IEEE, 1–6.
- 14. de la O D., Castillo O., Astudillo L., Soria J., “Fuzzy chemical reaction algorithm with dynamic adaptation of parameters”. In: Fuzzy Logic in Intelligent ystem Design, Patricia Melin, Oscar Castill o,
- Janusz Kacprzyk, Marek Reformat, William Melek (eds.), Springer International Publishing, 2018, 122–130. DOI: 10.1007/978-2-319-67137-6_13.
- 15. De Santis E., Rizzi A., Sadeghiany A, Mascioli F.M.F., “Genetic optimization of a fuzzy control system for energy flow management in micro--grids”. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), June 2013 pp. 418, 423, 24–28 June 2013. DOI: 10.1109/IFSA-NAFIPS.2013.6608437
- 16. Dongyun Wang, Guan Wang, Rong Hu, “Parameters optimization of fuzzy controller based on PSO”. In: ISKE 2008. 3rd International Conference on Intelligent System and Knowledge Engineering, 17th–19th Nov. 2008 vol. 1, 599, 603.
- 17. Esmin A. A A, Aoki A. R., Lambert-Torres G., “Particle warm optimization for fuzzy membership functions optimization”. In: 2002 IEEE International Conference on Systems, Man and Cybernetics, 6th–9th Oct. 2002, vol. 3, p. 6. DOI: 10.1109/ICSMC.2002.1176020.
- 18. Fierro R., Castillo O., “Design of Fuzzy Control Systems with Different PSO Variants”. In: Recent Advances on Hybrid Intelligent Systems, chapter 6, 2013, 81–88. DOI: 10.1007/978-3-642-33021-6_6.
- 19. Fierro R., Castillo O., Valdez F., “Optimization of fuzzy control systems with different variants of particle swarm optimization”. In: 2013 IEEE Workshop on Hybrid Intelligent Models and Applications (HIMA), 51–56. DOI: 10.1109/HIMA.2013.6615022.
- 20. Fierro R., Castillo O., Valdez F., Cervantes L., “Design of optimal membership functions for fuzzy controllers of the water tank and inverted pendulum with PSO variants”. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint (pp. 1068–1073). IEEE.
- 21. Gu Fang, Ngai Ming Kwok, Quang Ha, “Automatic fuzzy membership function tuning using the particle swarm optimization”. In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008, vol. 2, 324–328, 2008. DOI: 10.1109/PACIIA.2008.105.
- 22. H. X. Li, H. B. Gatland, “A New Methodology for Designing a Fuzzy Logic Controller”, IEEE Trans. On Sys., Man, and Cybernetics, vol.25, no. 3, March 1995, 505–512.
- 23. Kamejima T., Phimmasone V., Kondo Y., Miyatake M., “The optimization of control parameters of PSO based MPPT for photovoltaics”. In: 2011 IEEE 9th International Conference on Power Electronics and Drive Systems (PEDS), 5th–8th Dec. 2011, 881–883. DOI: 10.1109/PEDS.2011.6147358.
- 24. Lizarraga E., Castillo O., Soria J., Valdez F., “A Fuzzy Control Design for an Autonomous Mobile Robot Using Ant Colony Optimization”. In: Recent Advances on Hybrid Approaches for Designing Intelligent Systems, chapter 20, 2014, 289–304. DOI: 10.1007/978-3-319-05170-3_20 .
- 25. Martínez R., Castillo O., Soria J., “Particle Swarm Optimization Applied to the Design of Type-1 and Type-2 Fuzzy Controllers for an Autonomous Mobile Robot”. In: Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition, 2009, 247–262.
- 26. Martínez R., Castillo O., Aguilar L., “Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms”, Information Sciences, 2009, vol. 179, vol. 13, 2158–2174. DOI: 10.1016/j.ins.2008.12.028.
- 27. Martinez-Soto R., Castillo O., Aguilar L., Baruch I., “Bio-inspired optimization of fuzzy logic controllers for autonomous mobile robots. In: 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), 2012, 1–6. DOI: 10.1109/NAFIPS.2012.6291053.
- 28. Martínez-Soto R., Castillo O., Aguilar L., Melin P., “Fuzzy Logic Controllers Optimization Using Genetic Algorithms and Particle Swarm Optimization”. In: Advances in Soft Computing, chapter 41, 2010, 475–486. DOI: 10.1007/978-3-642-16773-7_41.
- 29. Martinez-Marroquin R., Castillo O., Soria J., “Parameter tuning of membership functions of a fuzzy logic controller for an autonomous wheeled mobile robot using ant colony optimization”. In: FUZZ-IEEE 2009. IEEE International Conference on Fuzzy Systems, 2007–2012.
- 30. Measurement and Instrumentation, Faculty of Electrical Engineering and Computer Science, Brno University of Technology, Czech Republic Department of Control. Autonomous Mobile Robotics Toolboxfor Matlab 5. Online. http://www. uamt.feec.vutbr.cz/robotics/simulations/amrt/simrobot en.html, 2001.
- 31. Melendez A., Castillo O., “Optimization of type-2 fuzzy reactive control-lers for an autonomous mobile robot”. In: 2012 Fourth World Congress on Nature and Biologically Inspired Computing (Na-BIC), 2012, 207–211.
- 32. Melendez A., Castillo O., “Evolutionary optimization of the fuzzy integrator in a navigation system for a mobile robot”. In: Castillo O., Melin P., Janusz Kacprzyk (eds.), Recent Advances on Hybrid Intelligent Systems, 2013, vol. 451 of Studies in Computational Intelligence, 21–31.DOI: 10.007/978-3-319-47054-2-43 .
- 33. Melendez A., Castillo O., Soria J., “Reactive control of a mobile robot in a distributed environment using fuzzy logic”. In: Fuzzy Information Processing Society, 2008. NAFIPS 2008. Annual Meeting of the North American, 2008 1–5. DOI: 10.1109/NAFIPS.2008.4531341.
- 34. Melendez A., Castillo O., Garza A., Soria J., “Reactive and tracking control of a mobile robot in a distributed environment using fuzzy logic”. In: IEEE International Conference on Fuzzy Systems, 2010, 1–5. DOI: 10.1109/FUZZY.2010.5583955.
- 35. Melendez A., Castillo O. “Hierarchical genetic optimization of the fuzzy integrator for navigation of a mobile robot”. In: Soft Computing Applications in Optimization, Control, and Recognition, chapter 4, 2013, 77–96.DOI: 10.1007/978-3-642-35323-9_4.
- 36. Porta García M. A., Montiel O., Castillo O., Sepúlveda R., Optimal Path Planning for Autonomous Mobile Robot Navigation Using Ant Colony Optimization and a Fuzzy Cost Function Evaluation. Analysis and Design of Intelligent Systems using Soft Computing Techniques, 2007, 790–798.
- 37. Milla F., Sáez D., Cortés C.E., Cipriano A., “Bus--Stop Control Strategies Based on Fuzzy Rules for the Operation of a Public Transport System”, IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 3, pp. 1394–1403. DOI: 10.1109/TITS.2012.2188394.
- 38. Montiel O., Camacho J., Sepúlveda R., Castillo O., “Fuzzy System to Control the Movement of a Wheeled Mobile Robot”. In: Soft Computing for Intelligent Control and Mobile Robotics, 2011, 445–463.
- 39. Ochoa P., Castillo O., Soria J., “Differential evolution with dynamic adaptation of parameters for the optimization of fuzzy controllers”. In: Recent Advances on Hybrid Approaches for designing intelligent systems, chapter 19, 2014, 275–288.DOI: 10.1007/978-3-319-05170-3_19.
- 40. Porta M., Montiel O., Castillo O., R. Sepúlveda, Melin P., “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation”, Appl. Soft Comput., 2009, vol. 9, no. 3, 1102–1110.
- 41. Vaneshani S., Jazayeri-Rad H., “Optimized fuzzy control by particle swarm optimization technique for control of SCTR”, International Journal of Electrical and Computer Engineering, 2011, vol. 11, no. 5, 464–470.DOI: scholar.waset.org/1307-6892/391.
- 42. Aguas-Marmolejo S.J., Castillo O., “Optimization of Membership Functions for Type-1 and Type 2 Fuzzy Controllers of an Autonomous Mobile Robot Using PSO”, Recent Advances on Hybrid Intelligent Systems, vol. 451, 2013, 97–104. DOI: 10.1007/978-3-642-33021-6_8.
- 43. Wong S., Hamouda A., “Optimization of fuzzy rules design using genetic algorithm”, Advances in Engineering Software, vol. 31, issue 4, April 2000, 251–262, ISSN 0965-9978.DOI: 10.1016/S0965-9978(99)00054-X.
- 44. Yen J., Langari R., Fuzzy Logic: Intelligence, Control, and Information, Prentice Hall, 1999.
- 45. Ying Bai, Hanqi Zhuang, Zvi. S. Roth, “Fuzzy Logic Control to Suppress Noises and Coupling Effects in a Laser Tracking System”, IEEE Trans on Control Systems Technology, vol.13, no.1, January 2005, 113–121.
- 46. Zafer B., Oğuzhan K., “A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control”, Expert Systems with Applications, vol. 38, issue 1, January 2011, 1017–1031, ISSN 0957-4174.DOI: 10.1016/j.eswa.2010.07.131
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c368ff46-1248-4781-88bd-8b08f88429e5