A Mine-clearing Plow Self-learning Fuzzy Neural Control System

In last few years, the combinatorial technology of fuzzy logic and neural network has been already established to solve some practical problems. Neural network is preferred to system identification and self-adaptive response to variable environment, while fuzzy inference systems are mainly applied to reasoning and decision-making with fuzzy rules derived from human experts.This paper proposes a method to combine neural network identification with fuzzy control to face some practical control problems of certain mine sweeping plough. In this paper, BP neural network is used to construct an intelligent model aimed at the servo system of the mine sweeping plough._Next the method incorporates fuzzy logic with neural network to obtain an intelligent controller of electro-hydraulic servo system to achieve its off-line optimization and on-line self-learning. Finally the performance of the intelligent controller is simulated and analyzed through electro-hydraulic servo system simulation software based on Matlab. The paper programs the intelligent control algorithm with Language C. A set of simulation and experimental results shows that the learning Neuro-Fuzzy intelligent controller possesses high performance statically and dynamically, and is able to produce accurate control results.