摘要
针对抽油机系统的特点和采油过程控制方面存在的问题,提出了基于RBF神经网络的自适应逆控制系统结构及算法.应用结果表明,该方法与常规抽油机相比最大净转矩峰值下降52.10%,转矩指数大于70%,节电率为17.8%.
It is difficult to control underground tapping oil due to its complexity and uncertainty of model.Aiming at the characteristics of nonlinear system and problem existing in controlling,the structure and arithmetic of adaptive inverse control based on RBF neural network are proposed.Test results applied to this system indicate that the system has qualities of good self-adaptive and high control precision,it can greatly improve efficiency of oil tapping and ratio of power saving.
出处
《大庆石油学院学报》
CAS
北大核心
2005年第5期104-105,111,共3页
Journal of Daqing Petroleum Institute
基金
国家教育部重点研究项目(地方02047)
