Multi-parameter deep-perception and many-objective autonomous-control of rolling schedule on high speed cold tandem mill

Abstract

High-speed cold tandem rolling process control system consists of complex mechanical and electrical equipments. The coupling association of these equipments makes multi-objective rolling process complicated to be predicted and controlled. In order to achieve higher prediction precision, a multi-parameter depth perception model is established based on a deep belief network. To get higher control precision in real time, a multi-objective rolling optimization method is introduced, which is supported by many-objective evolutionary algorithm. Five objectives are selected as rolling schedule optimization objective: equal relative power margin, slippage prevent, good flatness, total energy consumption and energy consumption per ton. Simulation results show that many-objective evolutionary algorithm based on decomposition and Gaussian mixture model achieves a set of balance solutions on these objectives. The proposed method could not only predict rolling force and rolling power in real time, but also give the solutions for many-objective reduction schedule.