沈正华,黄金梭.基于改进NSGA-II的无纺布制袋机纠偏系统预测控制[J].包装工程,2018,39(13):176-180.
SHEN Zheng-hua,HUANG Jin-suo.Improved NSGA-II Based MPC on Strip Deviation Control System of Non-woven Bag Making Machine[J].Packaging Engineering,2018,39(13):176-180. |
| 基于改进NSGA-II的无纺布制袋机纠偏系统预测控制 |
| Improved NSGA-II Based MPC on Strip Deviation Control System of Non-woven Bag Making Machine |
| 投稿时间：2017-09-04 修订日期：2018-07-10 |
| DOI：10.19554/j.cnki.1001-3563.2018.13.029 |
| 中文关键词: 改进NSGA-II 预测控制 纠偏控制 |
| 英文关键词: Improved NSGA-II MPC Deviation control |
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| 中文摘要: |
| 目的 解决无纺布制袋机在制袋过程中出现的带材跑偏问题，实现四路带材的纠偏控制，减少废品率，以提高控制精度和生产效率。方法 针对某厂无纺布制袋机运行的特点，采用一种基于改进NSGA-II的预测控制算法，用于该机器的纠偏控制。由于系统为非线性，采用支持向量机SVM模型作为被控对象来预测模型。同时，针对模型非线性及基本遗传算法收敛速度慢、精度低等缺点，利用一种改进的NSGA-II算法，作为预测控制的滚动优化策略。最后将设计的预测控制器应用于实际系统中。结果 该控制算法能满足系统实时稳定运行的要求，制袋机在快速运行时，纠偏误差基本控制在±0.3 mm以内，废品率由原来的0.80%降为0.40%。结论 基于改进NSGA-II的无纺布制袋机纠偏预测控制系统具有纠偏误差小、废品率低、可靠性强等特点，提高了生产效率。 |
| 英文摘要: |
| To solve strip deviation in non-woven making process，realize deviation Control of 4 strips，reduce rejection rate，improve the control precision and the productivity. According to the operation characteristics of a non-woven bag making machine in a factory，an improved NSGA-II based model predictive Control（MPC） algorithm was used in strip deviation controlling. The support vector machine (SVM) model was used as the predictive model of controlled object aiming to the nonlinear characteristics of this system. Meanwhile，an improved NSGA-II method was used as rolling optimization strategy of MPC, because of the model's nonlinear characteristics and basic genetic algorithm's slow convergence rate and low precision. Finally, the designed predictive controller was applied in the actual system. It could meet the requirement of real-time and stable operation of the system, the deviation error was basically controlled within ±0.3mm while fast running, and the rate of rejects was reduced from 0.80% to 0.40%. The improved NSGA-II based MPC method used on strip deviation control system of non-woven bag making machine was characteristic of little deviation error, low reject rate, strong reliability，and increased productivity. |
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