Cloud Neural Fuzzy PID Hybrid Integrated Algorithm of Flatness Control  

作　　者：Chun-yu JIA Tao BAI Xiu-ying SHAN Fa-jun CUI Sheng-jie XU 

机构地区：[1]College of Mechanical Engineering,Yanshan University [2]Department of Mechanical Engineering,Chengde Petroleum College

出　　处：《Journal of Iron and Steel Research International》2014年第6期559-564,共6页

基　　金：Sponsored by National High-tech Research and Development Project of China(2009AA04Z143);Natural Science Foundation of Hebei Province of China(E2006001038);Science and Technology Project of Hebei Province of China(10212101D)

摘　　要：In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.

关 键 词：flatness control cloud model neural network fuzzy inference PID 

分 类 号：TG334.9[金属学及工艺—金属压力加工]