Applied Neural Networks And Fuzzy Logic To Control The Speed To Reduce Vibration On The CB-250T


This paper introduces the control algorithm based on neural network and fuzzy logic to adjust the firing angle ? (thyristor controller) to control the rotation speed of the C??-250T rotary drill with different hard nesses and geological structures . The proposed solution uses an artificial neural network (neural network) tool to replace sensors to measure the vibrations to detect the amplitude and frequency of vibration on a rotating drill. The vibration amplitude, frequency of vibration and set point of the speed serve as input variables for the logic fuzzy. The logic fuzzy has the function of deducing and deciding the appropriate compensation parameter ?? with the goal of reducing vibration for the drill, but the speed control range of the system needs to ensure the allowable working efficiency of machine. The evaluation results are verified through modeling with the Simulink_matlab tool to be applied to the existing control system and improve the existing control quality in order to reduce vibration for the rotary drill. Keywords: fuzzy compensation control; drilling machine CB?-250T; neural network; fuzzy logic.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL