基于数据建模和参数压缩的连续干燥过程温湿度预测控制

Data modeling and parameter compression based predictive control based on temperature and humidity in continuous drying process

为精确控制干燥物料的含水率和干燥温度,提出基于数据建模和参数压缩的连续干燥过程温湿度预测控制算法。首先通过子空间辨识法建立连续干燥过程的子空间模型组,然后采用控制增量状态空间预测模型和阶梯式策略,描述具有约束的模型预测控制(MPC)的优化控制问题。在此基础上,将原二次规划问题的优化变量进行合理排序,再采用原始障碍内点法在线优化计算控制增量值,提高预测控制器的在线计算效率。最后通过对比仿真实验验证本文算法的有效性和优越性。

In order to accurately control the moisture content and drying temperature of dried materials,a data modeling and parameter compression based predictive control algorithm of temperature and humidity in continuous drying processes is proposed.Firstly,a set of subspace models of the continuous drying process are established through the subspace identification method,then the control incremental state-space prediction model and the stepwise strategy are adopted to formulate the optimization control problem of constrained model predictive control(MPC).Moreover,the optimized variables of the original quadratic programming problem are sorted reasonably,and then the original obstacle interior point method is used to optimize the control increment value online to improve the online calculation efficiency of the predictive controller.Finally,the effectiveness and superiority of the proposed algorithm are verified through comparative simulation experiments.