Development of fuzzy logic control system of the mobile robot

Authors

  • Виталий Иванович Булкин Makiivka economic-humanitarian institute Оstrovsky str., 16, Makiivka, Ukraine, 86157, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2015.44536

Keywords:

fuzzy algorithms, predicate algebra, algebra-predicate structures, associative-logical converters

Abstract

A mathematical model for the mobile robot control system based on fuzzy algorithms under rapidly changing dynamic environment was developed in the paper. The mathematical model is a system of algebra-predicate equations. Based on the equations obtained, AP-structures, which are associative-logical converters (ALC), were developed. Values of membership functions of input and output linguistic variables at intermediate points in the area of the carrier of linguistic values were found. Fuzzy subsets of input and output linguistic variables are presented as corresponding equations of the predicate algebra. Based on the algebra-predicate equations, the AP-structures of recognizers of fuzzy subsets of input and output linguistic variables were constructed. When developing the knowledge base of the fuzzy logic control system for the mobile robot, production rules were formalized using the mathematical apparatus of the predicate algebra. Based on the equations obtained,  the AP-structures, implementing these rules as ALC were constructed. As a result of the research, it was found that the AP-structures obtained can be reconfigured to recognize various domain objects. Therefore, such structures can be classified as flexible, reconfigurable structures of parallel data processing, operating in real time.  The results obtained are important from the theoretical and practical points of view since the functional-structural method allows to develop mathematical models for the human intelligence functions in the language of predicate algebra. Based on such models, creating the AP-structures that have functional-structural similarity with the human intelligence is possible.

Author Biography

Виталий Иванович Булкин, Makiivka economic-humanitarian institute Оstrovsky str., 16, Makiivka, Ukraine, 86157

Associate professor

Department of the applied mathematics and information technologies

References

  1. Ashby, W. R. (1956). An Introduction to Cybernetics. Chapman & Hall.
  2. Bondarenko, M. F. (2003). Fundamentals of theory of multivalued structures and coding in the systems of artificial intelligence. Kharkov, Factor-Druk, 336.
  3. Bulkin, V. I. (2012). Presentation of algebra predicate structures as associative-logical transformers. J Artificial intelligence, 3, 6–17.
  4. Darintsev, O. V. (2012). Various approaches of traffic control of mobile robots on the basis of soft calculations. J Artificial intelligence, 3, 339–347.
  5. Nishitani, I. (2015). Human-centered X–Y–T space path planning for mobile robot in dynamic environments. J Robotics and Autonomous Systems, 66, 18–26.
  6. Baca, J., Pagala, P., Rossi, C., Ferre, M. (2015). Modular robot systems towards the execution of cooperative tasks in large facilities. Robotics and Autonomous Systems, 66, 159–174. doi: 10.1016/j.robot.2014.10.008
  7. Agarwal, M., Kumar, N., Vig, L. (2014). Non-additive multi-objective robot coalition formation. Expert Systems with Applications, 41 (8), 3736–3747. doi: 10.1016/j.eswa.2013.11.044
  8. Silva, P., Santos, C. P., Matos, V., Costa, L. (2014). Automatic generation of biped locomotion controllers using genetic programming. Robotics and Autonomous Systems, 62 (10), 1531–1548. doi: 10.1016/j.robot.2014.05.008
  9. Glasius, R., Komoda, A., Gielen, S. C. A. M. (1995). Neural Network Dynamics for Path Planning and Obstacle Avoidance. Neural Networks, 8 (1), 125–133. doi: 10.1016/0893-6080(94)e0045-m
  10. Syed, U. A., Kunwar, F., Iqbal, M. (2014). Guided Autowave Pulse Coupled Neural Network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots. Robotics and Autonomous Systems, 62 (4), 474–486. doi: 10.1016/j.robot.2013.12.004
  11. Huang, D.-W., Gentili, R. J., Reggia, J. A. (2015). Self-organizing maps based on limit cycle attractors. Neural Networks, 63, 208–222. doi: 10.1016/j.neunet.2014.12.003
  12. Rommelfanger, H. (1994). Fuzzy Decision Support-Systeme. Springer-Lehrbuch. doi: 10.1007/978-3-642-57929-5
  13. Wang, X., Fu, M., Ma, H., Yang, Y. (2015). Lateral control of autonomous vehicles based on fuzzy logic. Control Engineering Practice, 34, 1–17. doi: 10.1016/j.conengprac.2014.09.015
  14. Naili, M., Boubetra, A., Tari, A., Bouguezza, Y., Achroufene, A. (2015). Brain-inspired method for solving fuzzy multi-criteria decision making problems (BIFMCDM). Expert Systems with Applications, 42 (4), 2173–2183. doi: 10.1016/j.eswa.2014.07.047
  15. Darintsev, O. V. (2011). Planning of a trajectory of the movement of the microrobot on the basis of indistinct rules. Artificial intelligence. Intellectual systems (AI-2011): materials of the International Scientific and technical conference. Donetsk: IPAI, 228–232.
  16. Shabanov-Kushnarenko, Y. P. (1984). Theory of intellect. Mathematical facilities. Kharkov, Vischa shk., 144.
  17. Shabanov-Kushnarenko, Y. P. (1984). Theory of intellect. Technical facilities. Kharkov, Vischa shk.,136.
  18. Bondarenko, M. F. (2011). Brainlike structures. Kiev: Naukova dumka, 460.

Published

2015-06-29

How to Cite

Булкин, В. И. (2015). Development of fuzzy logic control system of the mobile robot. Eastern-European Journal of Enterprise Technologies, 3(4(75), 45–53. https://doi.org/10.15587/1729-4061.2015.44536

Issue

Section

Mathematics and Cybernetics - applied aspects