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.
Parameter optimised iterative learning control algorithm for multi-batch reactor