摘要
针对超临界直流锅炉过热汽温对外界扰动响应快,易发生超温或低温现象,提出一种将动态矩阵预测控制与修正的递推最小二乘算法相结合的折息递推最小二乘(DRPLS)自适应动态矩阵控制策略。通过在动态矩阵预测控制中加入DRPLS算法,降低老数据的影响,增强新数据的作用,提高预测性能。通过调节折息因子,可使模型具有更高的灵活性和适应性。将该方法应用于600MW超临界直流锅炉高温过热系统进行仿真研究,结果表明,该控制策略能较好地适应对象的动态特性变化,且控制系统的性能明显优于常规PID控制器。
To avoid superheater steam temperature of supercritical boiler effect such as presumably higher or lower by disturbance, this paper proposed an adaptive dynamic matrix control（DMC） algorithm based on the theory which combining DMC with discount recursive partial least squares （DRPLS）. This method can reduce the effect of the old data, and tone up new data. That improves the predictive capability of model. Based on discounted-measurement the model has the better flexibility and adaptability. A simulation for the superheated steam temperature control system of 600MW supercritical once-through boiler using presented method is carried out, and results in that the control system performance is better than conventional PID cascade control.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2007年第8期70-75,共6页
Proceedings of the CSEE
基金
国家自然科学基金项目(50576022)
北京市自然科学基金项目(4062030)。~~
关键词
动态矩阵控制
折息递推最小二乘
自适应控制
超临界炉
dynamic matrix control
discount recursive partial least square
adaptive control
supercritical boiler
