• M. V. Bobyr South-West State University, Kursk
  • N. A. Milostnaya South-West State University, Kursk




Fuzzy inference system, fuzzy set theory, RMSE, soft computing, mobile robot.


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.

Author Biographies

M. V. Bobyr, South-West State University, Kursk

Dr.Sc., Associate Professor, Professor of department of Computer Science

N. A. Milostnaya, South-West State University, Kursk

PhD., Lecturer of department of Computer Science


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


How to Cite

Bobyr, M. V., & Milostnaya, N. A. (2018). A SOFT FUZZY ALGORITHM OF THE MOBILE ROBOT CONTROL. Radio Electronics, Computer Science, Control, (4), 168–178. https://doi.org/10.15588/1607-3274-2017-4-19



Control in technical systems