Strategic Level of Mobile Robot Control System Based on Fuzzy Logic
DOI:
https://doi.org/10.14419/ijet.v7i4.38.29192Keywords:
Tightly-Coupled Navigation System, MEMS Sensors, GNSS, Kalman Filter, Fuzzy LogicAbstract
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).
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Accepted 2019-05-09