Strategic Level of Mobile Robot Control System Based on Fuzzy Logic

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    As autonomous mobile objects become more complex and the range of tasks for which they are used widens, existing navigation systems are beginning to lag behind requirements for accuracy, weight and size, cost and other characteristics. The use of intelligent algorithms capable of reasonable data integration taking into account not only the design but also both the situational features of the sensors used and their noise characteristics, which determine the nature of the mobile object’s movement, can improve the navigation system’s accuracy. This article describes the operation of a tightly-coupled navigation system based on inertial low-cost MEMS sensors and GNSS navigation, as well as an adaptive Kalman filter based on an expert system with fuzzy logic technology. To implement the expert system’s knowledge base with the help of fuzzy logic, the Takagi-Sugeno model was chosen, as it is an effective tool for describing systems with a priori-known character of transformations between input and output signals. In the framework of the resulting algorithm, we propose refining the noise covariance matrix using fuzzy logic, based on analysis of the inertial sensors’ noise readings. In the paper the necessary calculations, a test simulation was carried out, which shows the results and operating time of the classic Kalman filter and the proposed algorithm on the microcontroller Cortex M4 by STMicroelectronics (Stm32f407).

     

     


  • Keywords


    Tightly-Coupled Navigation System, MEMS Sensors, GNSS, Kalman Filter, Fuzzy Logic

  • References


      [1] Branets V.N. Introduction to the theory of free-form inertial navigation systems / V.N. Branets, I.P. Shmyglevsky - “Science”, Chapters. ed. physical and mathematical literature, 1992.

      [2] Vavilov V.D. Optimization of parameters of a micromechanical accelerometer / V. D. Vavilov, V. L. Volkov, A. V. Ulyushkin // Proceedings of the Nizhny Novgorod State Technical University. R.E. Alekseeva - 2010. - V. 3 - No. 82 - 308-314c.

      [3] Raspopov V.Ya.Micromechanical devices / V.Ya. Raspopov - Tula: Tula State University, 2002.– 7–95c.

      [4] The open library GPSTk, http://www.gpstk.org/.

      [5] Kalman R.E. (1960). «A new approach to linear filtering and prediction problems». Journal of Basic Engineering 82 (1)-1960. pp. 35–45.

      [6] Takagi T.,Sugeno M. Fuzzy identification of systems and its application to modeling and control // IEEE Trans. Systems, Man and Cybernetics, 1985, №15(1), P. 116—132.

      [7] William Premerlani and Paul Bizard, Direction Cosine Matrix IMU: Theory.

      [8] I. M. Makarov, V. M. Lokhin, Intellectual Automatic Control Systems. Moscow: Fizmatlit, 2001.

      [9] I. M. Makarov, V. M. Lokhin, Artificial Intelligence and Complex Objects Control, New-York: Edwin Mellen Press, 2000.

      [10] I. M. Makarov, V. M. Lokhin, S. V. Manko, M. P. Romanov, Artificial Intelligence and Intelligent Control Systems, Branch Inform. Technology and Computer, RAS systems, Moscow: Nauka, 2006.

      [11] T. Takagi, M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 15, pp. 116-132, 1985.

      [12] N. A. Kazachek, “The use of intelligent algorithms based on fuzzy logic in management of industrial facilities”, Science Review, no. 20, pp. 165-170, 2015.

      [13] Ingvar Strid & Karl Walentin (2009), "Block Kalman Filtering for Large-Scale DSGE Models", Computational Economics (Springer). — Т. 33 (3): 277–304.

      [14] Branets V.N. Introduction to the theory of free-form inertial navigation systems / V.N. Branets, I.P. Shmyglevsky - “Science”, Chapters. ed. physical and mathematical literature, 1992.

      [15] Melikhov A.N., Bershtein L.S., Korovin S.Ya. Fuzzy logic situation counseling systems. - M .: Science, 1990.

      [16] A Piegat. Fuzzy modeling and control, Physica Verlag Heidelberg, 728 p. 2001.

      [17] V. Lokhin, M. P. Romanov. INTELLECTUAL CONTROL SYSTEMS –PRODUCTIVE PLATFORM FOR THE DEVELOPMENT OF A NEW GENERATION TECHNOLOGY // Russian Technology Journal. - 2014. - №. 1. - pp. 1-24.

      [18] Vodicheva LV. Increasing the reliability and accuracy of a strapdown inertial measuring unit with an excessive amount of measurements // Gyroscopy and navigation. 1997. № 1. - p. 55-67.

      [19] Steve Nasiri, David Sachs and Michael Maia. Selection and integration of MEMS-based motion processing devices//www.dspdesignline.com/howto/218401101#.

      [20] Abdel-Hamid, W. Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications. Ph.D. Thesis. University of Calgary, Calgary, AB, Canada, 2005.

      [21] Zaikin B. A. et al. Evaluation of coordinates of air target in a two-position range measurement radar //Russian technological journal. – 2016. – Т. 4. – №. 2. – С

      [22] . 65-72.


 

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Article ID: 29192
 
DOI: 10.14419/ijet.v7i4.38.29192




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