scholarly journals Stability Monitoring of Batch Processes with Iterative Learning Control

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yan Wang ◽  
Junwei Sun ◽  
Taishan Lou ◽  
Lexiang Wang
Keyword(s):  
Iterative Learning Control ◽  
Moving Average ◽  
Arma Model ◽  
Learning Control ◽  
Batch Process ◽  
Iterative Learning ◽  
Batch Processes ◽  
Statistical Process ◽  
Stability Monitoring ◽  
Stability Indices

In recent years, the iterative learning control (ILC) is widely used in batch processes to improve the quality of the products. Stability is a preoccupation of batch processes when the ILC is applied. Focusing on the stability monitoring of batch processes with ILC, a method based on innerwise matrix with considering the uncertainty of the model and disturbance was proposed. First, the batch process with ILC was derived as a two-dimensional autoregressive and moving average (2D-ARMA) model. Then two kinds of stability indices are constructed based on the innerwise matrix through the identification of the 2D-ARMA. Finally, the statistical process control (SPC) chart was adopted to monitor those stability indices. Numerical results are presented to demonstrate the effectiveness of the proposed method.

scholarly journals Iterative Learning Control Guided Reinforcement Learning Control Scheme for Batch Processes

2020 ◽  
Author(s):  
Xinghai Xu ◽  
Huimin Xie ◽  
Kechao Wen ◽  
Runze He ◽  
Wenjing Hong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Iterative Learning Control ◽  
Batch Reactor ◽  
Learning Control ◽  
Batch Process ◽  
Iterative Learning ◽  
Batch Processes ◽  
The Neural Network ◽  
Control Scheme ◽  
Data Efficiency

Iterative learning control (ILC) offers an effective learning control scheme to solve the control problems of the batch processes. Although the control performances of ILC systems can be improved batch-by-batch, the convergence still strongly depends on the repeatability of the process and thus lack of robustness. Meanwhile, the data-driven-based deep reinforcement learning (DRL) algorithms have good robustness due to the generalization of the neural network, but it has lower data efficiency in training. In this paper, we propose a complementary control scheme for the batch processes by employing a DRL guided by a classical ILC, termed as the IL-RLC scheme. This scheme has higher data efficiency than the DRL without guidance and better robustness than the ILC, which are demonstrated by the numerical simulations on a linear batch process and a nonlinear batch reactor. This work provides a way for the application of DRL algorithm in the batch process control.

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