Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is pr...Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning(JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.展开更多
A new method was developed for batch process monitoring in this paper. In the devdopad method, just-in-time learning （ JITL ） and independent component analysis （ICA） were integrated to build JITL-ICA monitoring s...A new method was developed for batch process monitoring in this paper. In the devdopad method, just-in-time learning （ JITL ） and independent component analysis （ICA） were integrated to build JITL-ICA monitoring scheme. JITL was employed to tackle with the characteristics of batch process such as inherent time- varying dynamics, multiple operating phases, and especially the case of uneven length stage. According to new coming test data, the most correlated segmentation was obtained from batch-wise unfolded training data by JITL. Then, ICA served as the principal components extraction approach. Therefore, the non.Gaussian distributed data can also be addressed under this modeling framework. The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation.展开更多
发酵过程具有时变性、动态性和多阶段性的特点,对其进行故障监测主要采用离线建模方式,但这种方法并不能很好地反映当前生产过程的数据特征。近年来有学者使用即时学习（Just in Time Learning,JITL）在线建模策略来建立精确的在线模型...发酵过程具有时变性、动态性和多阶段性的特点,对其进行故障监测主要采用离线建模方式,但这种方法并不能很好地反映当前生产过程的数据特征。近年来有学者使用即时学习（Just in Time Learning,JITL）在线建模策略来建立精确的在线模型并进行故障监测,但是即时学习在线建模策略存在着模型更新频繁、计算量大的问题？本文提出一种带有模型更新机制的即时学习多向偏最小二乘（JITL-MPLS）的故障监测方法：依据马氏距离相似度,选择相似历史样本建立多向偏最小二乘监测模型;而后通过对比上一时刻的质量测量值和当前时刻的质量预测值的差值是否超限来判断模型是否需要更新,当其差值没有超限,即上一时刻监测模型能够表征当前时刻的数据特征,不更新模型,而是继续沿用,否则更新模型。最后将此方法应用于青霉素发酵仿真系统的在线监测,验证了该方法的有效性。展开更多
Mechanical performance prediction is the key to the transformation and upgrading of steel enterprises to intelligent manufacturing.Due to time-varying manufacturing data,the traditional prediction model of mechanical ...Mechanical performance prediction is the key to the transformation and upgrading of steel enterprises to intelligent manufacturing.Due to time-varying manufacturing data,the traditional prediction model of mechanical properties of hotrolled strip may cause performance degradation or even failure in its use.An MDA-JITL model was thus proposed to handle the modeling problem of complex time-varying data.Relevant parameters were first chosen and normalized.Then,a distance measurement method combining the importance of data attributes and time characteristics was designed to select the most suitable samples for on-line local modeling.After that,using the chosen dataset,a linear local model was created to predict target sample.Finally,an uncertainty evaluation method was designed to evaluate the uncertainty of prediction results.Furthermore,the appropriate dataset partition and off-line simulation experiment scheme were created based on the peculiarities of hot-rolling production.The suggested model performs much better than the classic global model when applied to actual production data from a steel plant.The stability of its prediction accuracy is demonstrated in a simulation prediction for up to five months.Moreover,there is a high link between the uncertainty evaluation metrics and the prediction error of the model,reducing the field sampling rate by 30%in industrial applications in the latest year.展开更多
基金Supported by the National Natural Science Foundation of China(61374044)Shanghai Science Technology Commission(15510722100,16111106300)Shanghai Municipal Education Commission(14ZZ088)
文摘Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning(JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.
基金National Natural Science Foundations of China(Nos.61403256,61374132)Special Scientific Research of Selection and Cultivation of Excellent Young Teachers in Shanghai Universities,China(No.YYY11076)
文摘A new method was developed for batch process monitoring in this paper. In the devdopad method, just-in-time learning （ JITL ） and independent component analysis （ICA） were integrated to build JITL-ICA monitoring scheme. JITL was employed to tackle with the characteristics of batch process such as inherent time- varying dynamics, multiple operating phases, and especially the case of uneven length stage. According to new coming test data, the most correlated segmentation was obtained from batch-wise unfolded training data by JITL. Then, ICA served as the principal components extraction approach. Therefore, the non.Gaussian distributed data can also be addressed under this modeling framework. The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation.
文摘发酵过程具有时变性、动态性和多阶段性的特点,对其进行故障监测主要采用离线建模方式,但这种方法并不能很好地反映当前生产过程的数据特征。近年来有学者使用即时学习（Just in Time Learning,JITL）在线建模策略来建立精确的在线模型并进行故障监测,但是即时学习在线建模策略存在着模型更新频繁、计算量大的问题？本文提出一种带有模型更新机制的即时学习多向偏最小二乘（JITL-MPLS）的故障监测方法：依据马氏距离相似度,选择相似历史样本建立多向偏最小二乘监测模型;而后通过对比上一时刻的质量测量值和当前时刻的质量预测值的差值是否超限来判断模型是否需要更新,当其差值没有超限,即上一时刻监测模型能够表征当前时刻的数据特征,不更新模型,而是继续沿用,否则更新模型。最后将此方法应用于青霉素发酵仿真系统的在线监测,验证了该方法的有效性。
基金This work was supported by the National Natural Science Foundation of China(No.52004029)the Fundamental Research Funds for the Central Universities(FRF-TT-20-06).
文摘Mechanical performance prediction is the key to the transformation and upgrading of steel enterprises to intelligent manufacturing.Due to time-varying manufacturing data,the traditional prediction model of mechanical properties of hotrolled strip may cause performance degradation or even failure in its use.An MDA-JITL model was thus proposed to handle the modeling problem of complex time-varying data.Relevant parameters were first chosen and normalized.Then,a distance measurement method combining the importance of data attributes and time characteristics was designed to select the most suitable samples for on-line local modeling.After that,using the chosen dataset,a linear local model was created to predict target sample.Finally,an uncertainty evaluation method was designed to evaluate the uncertainty of prediction results.Furthermore,the appropriate dataset partition and off-line simulation experiment scheme were created based on the peculiarities of hot-rolling production.The suggested model performs much better than the classic global model when applied to actual production data from a steel plant.The stability of its prediction accuracy is demonstrated in a simulation prediction for up to five months.Moreover,there is a high link between the uncertainty evaluation metrics and the prediction error of the model,reducing the field sampling rate by 30%in industrial applications in the latest year.
