催化剂窑炉温度的启发式动态规划控制

Heuristic dynamical programming control for catalyst baking furnace temperature

针对催化剂生产过程中焙烧窑炉温度控制问题,提出了一种启发式动态规划(heuristic dynamical programming,HDP)控制方法。该方法通过策略评价及策略提升的重复进行逐渐逼近最优的控制策略。采用人工神经网络建立了被控系统和评价指标模型,基于梯度下降原理阐明了控制器各模块的在线学习方法。对某催化剂公司的窑炉温度控制实验表明,与常规PID控制相比,HDP控制方案具有较强的工况适应能力,其控制精度较常规控制提高约70%,加热电流均值减小约5%。

To solve the temperature control problem of the catalyst baking furnace, a heuristic dynamical programming （HDP） control method is proposed. The optimal control policy in HDP scheme is approximated gradually by implementing policy evaluation and policy improvement repeatedly. The system dynamics and the critic model are established by artificial neural networks. The learning algorithm of the HDP controller is clarified based on the gradient-decent principle. The proposed controller is tested on a baking furnace in a certain catalyst company. The experimental results indicate that the HDP controller has stronger ability to accommodate different work conditions than PID controller. In comparison with PID controller, the control precision in HDP controller increases about 70%, and the average electric current in HDP controller decreases about 5%