教与学樽海鞘优化的松散回潮预测控制研究

Research on predictive control of loosening and conditioning process in the optimization of teaching and learning salp swarm

在具有非线性和时滞性的烟叶松散回潮系统中,为解决传统控制方法存在的预测精度低、控制稳定性差等问题,提出一种模型预测控制方法。将卷积神经网络与门控循环单元网络相结合,按照NARMAX模型建立回潮工序多输入多输出系统的预测模型,提高预测精度;提出教与学樽海鞘优化算法进行滚动优化,保证出口水分和回风温度均能够准确且平稳地跟随设定值。结果表明:模型能实现对回潮过程回风温度和出口水分的同步控制,与其他预测控制方法相比,具有较好的预测效果与控制性能,其中预测模型的均方根误差的平均值为0.027,控制器的超调量平均为0.118%,CPK值平均高达2.45。

In the tobacco loosening and conditioning system with non-linearity and time lag,a model predictive control method is proposed to address the problems of the traditional control methods with low prediction accuracy and low control stability.First,to improve the model prediction accuracy,the convolutional neural network is integrated with the gated recurrent unit network according to the NARMAX model to build a prediction model with multi-input and multi-output systems of the loosening and conditioning process.Then,a teaching and learning salp swarm optimization algorithm is proposed to perform rolling optimization,ensuring the recirculated air temperature and the outlet moisture consistently and accurately meet the set values.The results show the model achieves synchronized control of recirculated air temperature and outlet moisture,and performs better prediction and control than other models.The average root-mean-square error of the prediction model is 0.027,the average overshoot of the controller is 0.118%,and the average CPK value is as high as 2.45.