Abstract
In comparison with constant torque brakes, constant deceleration brakes are more advantageous for the safety of mining hoists, but complete set of such products manufactured by big companies are not what ordinary mining enterprises can afford. As an alternative solution, this article develops a constant deceleration compensation device, which adds the function of constant deceleration brake onto the original brakes. Control strategy based on Fuzzy Neural Network PID is investigated and simulated with the combination of AMEsim and Simulink. An actual device is built and tested in real industrial field. The application illustrates the feasibility of this constant deceleration compensation device, which can achieve constant decelerations within a very short time. This device will prevent dangerous decelerations from happening to hoists at a much lower cost, and greatly improve the safety and reliability of mining hoists.
Highlights
A mine hoist is the key transportation equipment connecting the ground and underground facilities during mine production
Gui et al.[4] built a mathematical model of a constant deceleration brake system, in which a fuzzy controller was utilized, but the fuzzy rules were based on priori knowledge and impacted by subjective factors to some extent
With the trained Fuzzy Neural Network, the developed controller calculates the change amount of proportional coefficient DKp, integral coefficient DKi, and differential coefficient DKd according to the deceleration error and the change rate of deceleration error
Summary
A mine hoist is the key transportation equipment connecting the ground and underground facilities during mine production. Based on the sample set that is collected through the simulation of the predesigned fuzzy PID control system under various conditions of hoist brakes, the parameters above are obtained through training.[9,10,11] Figures 4 and 5 show the trained membership function of deceleration error and the change rate of deceleration error, as well as the change amount of proportional coefficient DKp, integral coefficient DKi, and differential coefficient DKd. In the Fuzzy Neural Network, the central values cij and the width values sij need training, as well as the weight coefficients vji. During this test, with the trained Fuzzy Neural Network, the developed controller calculates the change amount of proportional coefficient DKp, integral coefficient DKi, and differential coefficient DKd according to the deceleration error and the change rate of deceleration error. The red line shows the change of the deceleration, which is kept stable after the preset value is achieved
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
