ET-BAS算法在炉温预测控制中的应用

Application of ET-BAS algorithm in furnace temperature predictive control

针对蓄热式氧化炉炉温非线性、大滞后的特点,以及传统预测控制算法难以建立精确的数学模型、控制精度不高的现状,提出一种基于极端随机树(ET)的非线性系统预测控制算法。算法利用历史输入输出数据训练学习,建立炉温预测模型,从而实现对炉温的精确预测,同时以天牛须搜索(BAS)算法为基础设计滚动优化控制器。仿真结果表明:基于天牛须搜索为基础的预测控制法相较于传统预测控制方法,其预测精度达到了±1.5℃,控制精度达到了±0.5℃,且具有更短的上升时间、更小的超调量和更小的稳态误差。

Aiming at characteristics of non-linear temperature and large time delay of regenerative oxidation furnace,as well as the current situation that the traditional predictive control algorithm is difficult to establish an accurate mathematical model and the control precision is not high,a predictive control algorithm for non-linear system based on extra trees(ET)is proposed.The algorithm trains and studies the historical input and output data,establishes furnace temperature prediction model,and realizes the accurate prediction of furnace temperature.At the same time,rolling optimization controller is designed based on the algorithm of beetle antenna search(BAS).The simulation results indicate that compared with the traditional predictive control method,the predictive control method based on BAS has a predictive precision of±1.5℃and a control precision of±0.5℃.The proposed method has shorter rise time,smaller overshoot and smaller steady-state errors.