A SOFT FUZZY ALGORITHM OF THE MOBILE ROBOT CONTROL
DOI:
https://doi.org/10.15588/1607-3274-2017-4-19Keywords:
Fuzzy inference system, fuzzy set theory, RMSE, soft computing, mobile robot.Abstract
Context. The task of a mobile robot control on the base of soft algorithm fuzzy inference has been solved.
Objective is the creation of soft algorithm of the fuzzy inference which allows to provide additivity of fuzzy control system.
Method. A soft algorithm of fuzzy inference used to control the mobile robot is suggested. Given algorithm allows to compensate errors inherent to the traditional models of fuzzy inference. Errors include: the curse of dimensionality, the absence of additivity and the fuzzy partition. This soft algorithm of fuzzy inference at the expense of rational allocation of premises and conclusions in a matrix of fuzzy relations, reduces the number of operations of the fuzzy inference. Another distinctive feature of the proposed soft algorithm is that in fuzzy inference to find minima and maxima used soft arithmetic operators. The paper shows that during the work of hard formulas for the implementation of these formulas while controlling the mobile robot the situations will appear when the robot loses control. The article points out that the implementation of the possibility in soft algorithm of fuzzy inference option of changes in parameters of sigmoidal membership functions will minimize the error at fuzzy system output. Dynamics of changing RMSE ratio from the varying parameters of sigmoidal membership functions proves it. The additional simulations presented in the article shows that during varying the parameters of sigmoidal membership function, during in increasing of the parameter a, is being observed decrease in the value of RMSE. The effectiveness of the proposed soft algorithm is confirmed by numerical simulation and experiments in the researching of a mobile robot movement along a line.
Results. The specialized software for microcontroller Arduino Uno is developed and it realizes the proposed soft algorithm which allows to carry out an experimental study of its properties.
Conclusions. The software realizing proposed algorithm has been developed and used in computational experiments investigating the properties of the algorithm. The experiments confirmed the efficiency of the proposed algorithm and software.References
Shtovba S. D., Galushchak A. V. Fuzzy classifier learning based on distance between the main competitors, Radio Electronics, Computer Science, Control, 2016, No. 2, pp. 70–76. DOI: 10.15588/1607-3274-2016-2-9
Subbotin S. A. The neuro-fuzzy network synthesis with the ranking and specific encoding of features for the diagnosis and automatic classification on precedents, Radio Electronics, Computer Science, Control, 2016, No. 1, pp. 50–57. DOI: 10.15588/1607-3274-2016-1-6
Fateh M. M., Fateh S. A precise robust fuzzy control of robots using voltage control strategy, International Journal of Automation and Computing, 2013, No. 10, pp. 64–72. DOI:10.1007/s11633-013-0697-x
Brown M., Bossley K. M., Mills D. J., Harris C. J. High dimensional neurofuzzy systems: overcoming the curse of dimensionality, Proceedings IEEE International Conference, 1995, No. 4. pp. 2139–2146. DOI: 10.1109/fuzzy.1995.409976
Gactoa M. J., Galendeb M., Alcalбc R., Herrera F. METSK-HDe: A multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems, Information Sciences, 2014, Vol. 276, pp. 63–79. DOI: 10.1016/j.ins.2014.02.047
Vernieuwea H., De Baetsa B., Verhoest N.E.C. Comparison of clustering algorithms in the identification of Takagi-Sugeno models: A hydrological case study, Fuzzy Sets and Systems, 2006, Vol. 157, pp. 2876–2896. DOI:10.1016/j.fss.2006.04.007
Bodyanskiy Ye. V., Ryabova N. V., Zolotukhin O. V. Multilayer adaptive fuzzy probabilistic neural network in classification problems of text documents, Radio Electronics, Computer Science, Control, 2015, No. 1, pp. 39–45. DOI: 10.15588/1607-3274-2015-1-5
Piegat A. Fuzzy modelling and control. Physica-Verlag, Heidelberg, 2001, 728 p. DOI:10.1007/978-3-7908-1824-6
Shin M., Ryu K., Jung M. Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system, Expert Systems with Applications, 2012, Vol.39, pp. 8736–8743. DOI: 10.1016/j.eswa.2012.01.207
Zadeh L. A. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems, Soft Computing, 1998, No. 2, pp. 23–25. DOI:10.1007/s005000050030
Bobyr M. V., Milostnaya N. A. Analysis of the use of soft arithmetic operations in the structure of fuzzy logic inference, Vestnik komp’iuternykh i informatsionnykh tekhnologii, 2015, Vol. 133, pp. 7–15. DOI:10.14489/VKIT.2015.07.PP.007-015
Zadeh L. A. Fuzzy sets, Information and Control, 1965, No. 8, pp. 338–353. DOI:10.1016/S0019-9958(65)90241-X
Zadeh L. A. Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, 1999, Vol.100, pp. 9–34. DOI: 10.1016/S0165-0114(99)80004-9
Bobyr’ M. V., Titov V. S., Nasser A. A. Automation of the cutting-speed control process based on soft fuzzy logic computing, Journal of Machinery Manufacture and Reliability, 2015, Vol. 44, No.7, pp. 61–69. DOI: 10.3103/S1052618815070067.
Stepnicka M., De Baets B. Implication-based models of monotone fuzzy rule bases, Fuzzy Sets and Systems, 2013, Vol. 232, No.1, pp. 134–155. DOI: 10.1016/j.fss.2013.07.019
Kumanan S., Jesuthanam C. P., Ashok R. Kumar Application of multiple regression and adaptive neurofuzzy inference system for the prediction of surface roughness, The International Journal of Advanced Manufacturing Technology, 2008, Vol. 35, pp. 778–788. DOI: 10.1007/s00170-006-0755-4
Chernova I. V., Sumin S. A., Bobyr M. V., Seregin S. P. Forecasting and Diagnosing Cardiovascular Disease Based on Inverse Fuzzy Models, Biomedical Engineering, 2016, Vol. 49, No. 5, pp. 263–267. DOI 10.1007/s10527-016-9545-y.
Driankov D., Hellendoorn H., Reinfrank M. An introduction to fuzzy control. Springer, Berlin, 1996, 316 p. DOI:10.1007/978-3-662-03284-8
Neshat M., Adeli A., Sepidnam G., Sargolzaei M. Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems, The International Journal of Advanced Manufacturing Technology, 2012, Vol.63, pp. 373–390. DOI: 10.1007/s00170-012-3914-9
Greenfielda S., Chiclana F. Defuzzification of the discretised generalised type-2 fuzzy set: Experimental evaluation, Information Sciences, 2013, Vol. 244, pp. 1–25. DOI:10.1016/j.ins.2013.04.032
Bobyr M., Titov V. S., Belyaev A. Fuzzy System of Distribution of Braking Forces on the Engines of a Mobile Robot, MATEC Web of Conferences, 2016, Vol.79, pp. 01052 DOI: 10.1051/matecconf/20167901052
Palani S., Natarajan U., Chellamalai M. On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS), Machine Vision and Applications, 2013, Vol.24, pp. 19–32. DOI: 10.1007/s00138-011-0378-0
Deng X., Wang X. Incremental learning of dynamic fuzzy neural networks for accurate system modeling, Fuzzy Set and System, 2009, Vol. 60, pp. 972–987. DOI:10.1016/j.fss.2008.09.005
Banakara A., Azeem M. F. Parameter identification of TSK neuro-fuzzy models, Fuzzy Sets and Systems, 2011, Vol. 179, pp. 62–82. DOI: 10.1016/j.fss.2011.05.003
Bobyr M. V. Effect of conclusion rule on training of fuzzy-logic systems, Vestnik komp’iuternykh i informatsionnykh tekhnologii, 2014, Vol. 125, pp. 28–35. DOI: 10.14489/vkit.2014.11.pp.028-035
Azadeh A., Neshat N., Kazemi A., Saberi M. // Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach, The International Journal of Advanced Manufacturing Technology, 2012, Vol.58, pp. 585–596. DOI: 10.1007/s00170-011-3415-2
Selekwa M. F., Dunlap D. D., Shi D., Collins Jr. E. G. Robot navigation in very cluttered environments by preference-based fuzzy behaviors, Robotics and Autonomous Systems, 2008, Vol. 56, pp. 231–246. DOI:10.1016/j.robot.2007.07.006
Mo H., Tang Q., Meng L. Behavior-Based Fuzzy Control for Mobile Robot Navigation, Mathematical Problems in Engineering, 2013, 10 p. DOI: 10.1155/2013/561451
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2017 M. V. Bobyr, N. A. Milostnaya
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.