Modelling And Decoupling Control For Electrode Regulating System Of Electronic Arc Furnace

Electric arc furnace steelmaking has nearly a hundred years of history,the development in recent decades is particularly rapid.In modern steelmaking,electric arc furnace has a prominent position.It heats the charge by generating an arc of heat between the electrode and the material.Therefore,the performance of the electrode regulator for the entire EAF metallurgical process is very important,it directly affects the consumption of electricity,the electrode loss,the loss of mechanical equipment,power grid quality and product quality.For EAF,it is a time-lag nonlinear system with large hysteresis and strong coupling.The existing control methods for industrial electrodes are separately controlled for single-phase electrodes,which does not consider the unique coupling of EAF Characteristics,resulting in energy waste.Aiming at the nonlinear and time-varying characteristics of electrode control system,we first hope to use the variable-domain fuzzy control method with some decoupling ability.Different from fuzzy control,which can only divide the quantitative fuzzy universe,due to its own scaling factor,it can adjust the domain size adaptively and error rate if the initial domain division is set.This is a great error in a very fast time,but also in small errors with good accuracy.Its disadvantage is that it does not have strong decoupling characteristics,thus proposed diagonal decoupling control method.Diagonal decoupling control method can reduce the coupling phenomenon,but each time you need to re-calculate the work point.And its idea is to linearize the nonlinear model,the accuracy is not high,and then for the time-varying system,the diagonal decoupling method is slightly cumbersome.In view of this disadvantage,a neural network decoupling controller is proposed.In the end,the on-line training method combining decoupling and control is adopted to avoid the disadvantage of large computational load of diagonal decoupling and to make up for the time-varying influence of system parameters.Finally,simulations of all the above are given.Through the analysis we can see that the variable universe fuzzy control has its own decoupling characteristics,can play a certain inhibitory effect.The neural network decoupling controller goes a step further and eliminates the effects of coupling.