Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computati...Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.展开更多
A set of methods designed to improve （i.e.extend） the medium-term forecasting of persistent severe rainfall （PSR） events in China using the regional Weather Research and Forecasting model are summarized.Simulation...A set of methods designed to improve （i.e.extend） the medium-term forecasting of persistent severe rainfall （PSR） events in China using the regional Weather Research and Forecasting model are summarized.Simulations show that achieving a more efficient use of large-scale atmospheric variations of the global model and retaining small-scale features in the regional model are critical for better forecasting PSR events.For precipitation,the larger the magnitude and longer the lead time,the more significant the improvement-especially for the methods of spectral nudging and updated initial conditions.In terms of large-scale circulation,the anomaly correlation coefficient can be distinctly improved for 1-5-day lead times by adopting the spectral nudging technique,whereas lateral boundary filtering results in marked improvement for 7-11-day lead times.展开更多
The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to deve...The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.展开更多
基金Supported by the National Natural Science Foundation of China(21136003,21176089)the National Science&Technology Support Plan(2012BAK13B02)+2 种基金the National Major Basic Research Program(2014CB744306)the Natural Science Foundation Team Project of Guangdong Province(S2011030001366)the Fundamental Research Funds for Central Universities(2013ZP0010)
文摘Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.
基金supported by the National Natural Sci ence Foundation of China[grant number 41775097],[grant number 91437221]the National Key Basic Research Program of China[grant number 2012CB417204]the China Specia Fund for Meteorological Research in the Public Interest[grant number GYHY201506002]
文摘A set of methods designed to improve （i.e.extend） the medium-term forecasting of persistent severe rainfall （PSR） events in China using the regional Weather Research and Forecasting model are summarized.Simulations show that achieving a more efficient use of large-scale atmospheric variations of the global model and retaining small-scale features in the regional model are critical for better forecasting PSR events.For precipitation,the larger the magnitude and longer the lead time,the more significant the improvement-especially for the methods of spectral nudging and updated initial conditions.In terms of large-scale circulation,the anomaly correlation coefficient can be distinctly improved for 1-5-day lead times by adopting the spectral nudging technique,whereas lateral boundary filtering results in marked improvement for 7-11-day lead times.
基金Supported by the National Basic Research Program of China(2012CB720500)Postdoctoral Science Foundation of China(2013M541964)Fundamental Research Funds for the Central Universities(13CX05021A)
文摘The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.
