ADAPTIVE CONTROL AND IDENTIFICATION OF DRILLING SYSTEM WITH CONSIDERING THE TYPE OF DRILLED ORE MATERIAL
Journal Title: Computer Science Information Technology Automation - Year 2017, Vol 1, Issue 7
Abstract
The object is to investigate methods for forming a model for the system of adaptive control over drilling with the control object identifier. The following methods have been used in the course of the research: analysis of domestic and foreign experience, systematization of available approaches and methods, methods of numerical simulation for synthesis and analysis of mathematical model, methods of mathematical statistics and probability theory for processing the results of experiments, methods of analytical design and computer simulation in the synthesis and analysis of control system, methods of system analysis in the development of control algorithms. The scientific novelty consists in determining optimal parameters – number of membership functions, membership function type, inputs number - for neuro-fuzzy subsystem of drilling control system with considering the type of drilled ore material. The practical significance of the studies is a structure of adaptive control and identification of drilling system with considering the type of drilled ore material including model of geological structure. The results of the research investigate a strategy of the two-level adaptive control of borehole drilling under rapidly changing conditions which implies simultaneous drilling investigation and control. The subsystem of prediction is implemented on the basis of an adaptive neuro-fuzzy system. The applied neuro-fuzzy system realizes the Sugeno fuzzy inference in the form of a five-layer neural network of signal feedforward, the first layer of which contains the terms of input variables (the current signal value and its delayed values). It should be noted that the membership function type did not influence much on the prediction result. While processing and analyzing the current information about the latest characteristics of drilling and while forming the adaptive control it is reasonable to apply neuro-fuzzy structures with Gaussian membership functions.
Authors and Affiliations
V. S. Morkun, V. V. Tron, D. I. Paranyuk
Keywords
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