A Petri Net Neural Network Robust Control for New Paste Backfill Process Model

Abstract

In mining industries, the backfill becomes more and more important due to the environment protection concern. But most backfill investigations focus on the underground model and backfill materials. In this research, the paste backfill process based on the process control is investigated and a new paste backfill process model is proposed according to the Torricelli's law and Bernoulli principle. In order to deal with the unknown nonlinear function of the proposed backfill model, a Petri net(PN) structure is presented and a new Petri net neural network(PNNN) is introduced to approximate the unknown nonlinear function. Then, a robust controller is designed for the backfill process with PNNN and the closed loop stability is guaranteed by Lyapunov function candidate. The effectiveness of the proposed model with new PNNN and the robust controller are verified by simulation results.