A novel sim2real reinforcement learning algorithm for process control

doi:10.1016/j.ress.2024.110639

A novel sim2real reinforcement learning algorithm for process control

2025-02-01

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DOI：10.1016/j.ress.2024.110639

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While reinforcement learning (RL) has potential in advanced process control and optimization, its direct interaction with real industrial processes can pose safety concerns. Model-based pre-training of RL may alleviate such risks. However, the intricate nature of industrial processes complicates the establishment of entirely accurate simulation models. Consequently, RL-based controllers relying on simulation models can easily suffer from model-plant mismatch. On the one hand, utilizing offline data for pre-training of RL can also mitigate safety risks. However, it requires well-represented historical datasets. This is demanding because industrial processes mostly run under a regulatory mode with basic controllers. To handle these issues, this paper proposes a novel sim2real reinforcement learning algorithm. First, a state adaptor (SA) is proposed to align simulated states with real states to mitigate the model-plant mismatch. Then, a fix-horizon return is designed to replace traditional infinite-step return to provide genuine labels for the critic network, enhancing learning efficiency and stability. Finally, applying proximal policy optimization (PPO), the SA-PPO method is introduced to implement the proposed sim2real algorithm. Experimental results show that SA-PPO improves performance in MSE by 1.96% and in R by 21.64% on average for roasting process simulation. This verifies the effectiveness of the proposed method.

Keywords:

Reinforcement learning

Process control

Model-plant mismatch

Fix-horizon return

Industrial roasting process

A novel sim2real reinforcement learning algorithm for process control

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