Application Of Generalized Predictive Control In Stell Strip Rolling-Geting Control System

This paper studys the generalized predictive control algorithm, control object is Taiyuan Iron and Steel group CO.'s steel strip rolling-getting system on cold roll silicon steel production line, and researchs application of Generalized Predictive control algorithm in hydraulic servo position closed-loop system.Generalized Predictive control algorithm, based on predictive model and using fast digital computer, is a new type algorithm in computer process control since 70's, and it also is one of representative Advanced Process Control (APC) algorithm. First, in this paper ,a brief history is introduced about Predictive Control(PC) about its technology development and current features presented. Including Model Algorithmic Control(MAC), Dynamic Matrix Control(DMC) and three main principle Predictive Control (GPC).Then, the paper detail deduce algorithmic principles of GPC, and introduce algorithmic characteristic of GPC.The basic GPC algorithm have to solve Diophantinee quation and the inversion of big matrix online. As a result, real time control is infeasible. Therefore, a soften the control increaseing quantity strategy is proposed in this paper to induce extraordinarily computation. At the same time, it needn't computing the inverse matrix and thus reduces large computation. Then, for rank-variety or time-delay of system model, the improvming GPC algorithm has simulated .The simulation results show that improvming GPC has good control performance.On the base of theory study and simulation , in this paper, improving GPC algorithm is applied in stell strip rolling-getting control system(EPC) in order to overcome the disturbing in stell strip rolling-getting process. Then, for EPC system, it's mathematics model is builded and control performance is simulated. The simulation results show that, as disturbing appearing, improvming GPC algorithm have good control performance for hydraulic servo position closed -loop system.Finally, the paper concludes with a summary and perspectives of future research of GPC.