Two-dimensional delay compensation based iterative learning control scheme for batch processes with both input and state delays

2019 ◽  
Vol 356 (15) ◽  
pp. 8118-8137 ◽  
Author(s):  
Shoulin Hao
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Batch Processes ◽  
Two Dimensional ◽  
Delay Compensation ◽  
State Delays ◽  
Control Scheme

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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