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In this article the task of determining the current position of pneumatic actuators is considered. The solution to the given task is achieved by using a technical vision system that allows to apply the fuzzy clustering method to determine in real time the center coordinates and the displacement position of a color label located on the mechatronic complex actuators. The objective of this work is to improve the accuracy of the moving actuator’s of mechatronic complex by improving the accuracy of the color label recognition. The intellectualization of process of the color shade recognition is based on fuzzy clustering. First, a fuzzy model is built, that allows depending on the input parameters of the color intensity for each of the RGB channels and the color tone component, to select a certain color in the image. After that, the color image is binarized and noise is suppressed. The authors used two defuzzification models during simulation a fuzzy system: one is based on the center of gravity method (CoG) and the other is based on the method of area ratio (MAR). The model is implemented based on the method of area ratio and allows to remove the dead zones that are present in the center of gravity model. The method of area ratio determines the location of the color label in the image frame. Subsequently, when the actuator is moved longitudinally, the vision system determines the location of the color label in the new frame. The color label position offset between the source and target images allows to determine the moved distance of the color label. In order to study  how noise affects recognition accuracy, the following digital filters were used: median, Gaussian, matrix and binomial. Analysis of the accuracy of these filters showed that the best result was obtained when using a Gaussian filter. The estimation was based on the signal-to-noise coefficient. The mathematical models of fuzzy clustering of color label recognition were simulated in the Matlab/Simulink environment. Experimental studies of technical vision system performance with the proposed fuzzy clustering model were carried out on a pneumatic mechatronic complex that performs processing, moving and storing of details. During the experiments, a color label was placed on the cylinder, after which the cylinder moved along the guides in the longitudinal direction. During the movement, video recording and image recognition were performed. To determine the accuracy of color label recognition, the PSNR and RMSE coefficients were calculated which were equal 38.21 and 3.14, respectively. The accuracy of determining the displacement based on the developed model for recognizing color labels was equal 99.7%. The defuzzifier speed has increased to 590 ns.

The task of reducing the computational complexity of contour detection in images is considered in the article. The solution to the task is achieved by modifying the Canny detector and reducing the number of passes through the original image. In the first case, two passes are excluded when determining the adjacency of the central pixel with eight adjacent ones in a frame of size 3х3. In the second case, three passes are excluded, two as in the first case and the third one necessary to determine the angle of gradient direction. This passage is provided by a combination of fuzzy rules. The goal of the work is to increase the performance of computational operations in the process of detecting the edges of objects by reducing the number of passes through the original image. The process of edge detection is carried out by some computational operations of the Canny detector with the replacement of the most complex procedures. In the proposed methods, fuzzification of eight input variables is carried out after determining the gradient and the angle of its direction. The input variables are the gradient difference between the central and adjacent cells in a frame of size 3х3. Then a base of fuzzy rules is built. In the first method, four fuzzy rules and one pass are excluded depending on the angle of gradient direction. In the second method, sixteen fuzzy rules themselves set the angle of the gradient direction, while eliminating two passes along the image. The gradient difference between the central cell and adjacent cells makes it possible to take into account the shape of the gradient distribution. Then, based on the center of gravity method, the resulting variable is defuzzified. Further use of fuzzy a-cut makes it possible to binarize the resulting image with the selection of object edges on it. The presented experimental results showed that the noise level depends on the value of the a-cut and the parameters of the labels of the trapezoidal membership functions. The software was developed to evaluate fuzzy edge detection methods. The limitation of the two methods is the use of piecewise-linear membership functions. Experimental studies of the performance of the proposed edge detection approaches have shown that the time of the first fuzzy method is 18% faster compared to the Canny detector and 2% faster than the second fuzzy method. However, during the visual assessment, it was found that the second fuzzy method better determines the edges of objects.

Linear, nonlinear, modified, high-speed defuzzifiers based on the area ratio method are presented in this paper. The proposed defuzzifiers are used in a fuzzy digital filter device and make it possible to ensure the additivity of the robotic manipulator control system, since traditional models do not have this property. The essence of this development is to find a crisp value of the output fuzzy variable, which in this case are the regulation coefficients of the fuzzy digital filter. Reducing the number of computational operations provides an increase in the performance of the defuzzifier. The reduction in number of computational operations is carried out by eliminating the output variable’s truncated term’s height calculation, thereby reducing the computation time. A simulation model which was implemented in the MatLab Simulink system, for a neuro-fuzzy device of the robotic arm using linear and non-linear defuzzifiers is presented. The dependence of time graphs on the angle of rotation of the joints of the robotic manipulator are compared, based on the traditional center of gravity method and the method shown in this paper. It was found that the traditional center of gravity method does not ensure the fulfillment of the specified rotation angles of the links of the robotic arm, while the proposed models of defuzzifiers have this property, which can be seen from the presented dependency graphs. The simulation model of the device was also designed as a parallel-conveyor device for implementation in the field-programmable gate array of the Xilinx Spartan 3Е family. The analysis showed that the calculation time for a crisp value with high-speed defuzzification is 130 ns, which is two orders of magnitude higher than existing models. The experiment was conducted at a frequency of 100 MHz.

Simulation of control parameters calculating process in the problem of cooling the parts surfaces machined on CNC equipment is considered in the article. A fuzzy model for determining the transmitted to the thermoelement current to control surfaces details cooling intensity proposed in the article. The fuzzy model consists of four steps. At the first step, the calculation of the degrees of the membership functions is produced. Input membership functions are having a triangular shape, and the output variable is represented by a singleton function. The second step, the degrees of the premises of the twenty-seven fuzzy rules is calculated. The third step, the eleven levels of conclusions of fuzzy rules is calculated. At the fourth step, defuzzification a crisp value from a simplified fuzzy inference is derived. After that, the scaling ratio and the output voltage level are determined. Also the condition of equality of the collector current for the calculated value is checked. If this condition is not implemented, the level of the output voltage is recalculated, until the set condition is fulfilled. The main problem in the system operation is the voltage calculation at the microcontroller output corresponding to the required current on the thermoelement with the maximum speed. To provide voltage calculating high speed in device was made on a field-programmable gate array (FPGA).Defuzzification in the fuzzy inference is based on a simplified fuzzy inference. The explaining the essence of the proposed technical solution numerical modeling is presented in the article. Timing diagrams of the cooling control device and voltage-current conversion devices in the cooling control system are presented in the article.