基于模型预测控制的松散回潮自适应控制系统OA北大核心CSTPCD
An adaptive control system of tobacco loosening and conditioning based on model predictive control
为解决松散回潮的传统控制自适应差、控制不稳定等问题.根据松散回潮机工作原理与烟叶吸湿性机理,筛选出物料入口含水率、加水量、回风温度 3 个关键变量,以物料出口含水率为目标值,建立预测模型,针对因模型偏差导致的控制误差,在模型预测控制的基础上,运用神经网络模型中反向传播算法,根据松散回潮生产特性优化损失函数,使预测模型在控制过程中自迭代、自适应,提高了模型的自适应速率.系统优化后,松散回潮出口水分平均标准偏差由原 0.29 下降至 0.20,同比下降 32%,平均cpk由 1.132 提升至1.479,同比提升 30%.有效提高了松散回潮过程控制能力.
To solve the problems of poor adaptive and unstable control in traditional control of moisture regain.Based on the working principle of the loose moisture regain machine and the mechanism of moisture absorption of tobacco leaves,three key variables:the moisture content of the material inlet,the amount of water added and the return air temperature,were selected.With the outlet moisture content of the material as the target value,a prediction model was established.To address control errors caused by model deviations,using model predictive control(MPC),the backpropagation algorithm in the neural network model was used to optimize the loss function tailored to the characteristics of the moisture regain process.By enabling the predictive model to self-iterate and adapt during the control process,the adaptability of the model is improved.After system optimization,the average standard deviation of loose regain outlet moisture decreased from 0.29 to 0.20,a year-on-year decrease of 32%,and the average CPK increased from 1.132 to 1.479,a year-on-year increase of 30%.This effectively improved the control ability of loose moisture regain process.
赵春元;王略韬;张晓峰;于红丽;周成林;邓红伟;李秀芳
河南中烟工业有限责任公司黄金叶生产制造中心,郑州市经开第三大街 9 号 450016首域科技(杭州)有限公司,杭州市临平区新丰路 199 号 311100
松散回潮模型预测控制自适应反向传播算法损失函数
tobacco loosening and conditioningmodel predictive controladaptive back propagation algorithmloss function
《中国烟草学报》 2024 (002)
27-37 / 11
基于广义预测控制的松散回潮控制系统推广应用研究(20184100033450054)
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