Application of A Neural Network Predictive Control for the Supercritical Main

摘要：

The traditional PID control is difficult in the non-linear, delay, time-varying conditions and have a disturbance characteristics in supercritical main steam temperature control system to achieve satisfactory control effect. This paper presents a neural network predictive control method using multi-step prediction, rolling optimization and feedback correction control strategy, achieved good control results.Taking the supercritical main steam temperature as the research object, MATLAB simulation results show that, in various of the main steam temperature condition neural network dynamic model, both are well predict the dynamic characteristic, and achieved better performances than traditional PIDs.

关键词： Rolling optimal prediction function predictive control supercritical fluid main steam temperature

作者: Yun-Juan Li Yan-jun Fang Qi Li

作者单位: Kunming University Kunming 650118 ，China Wuhan University Wuhan 430072 ，China Naval University of engineering Wuhan 430033 ，China

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）