Safe chance constrained reinforcement learning for batch process control: A data-driven framework for learning control of uncertain batch process systems

Max Mowbray, Dongda Zhang, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona

Research output: Contribution to journalArticlepeer-review

Abstract

Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their ability to optimize uncertain systems independently of explicit assumption of process uncertainty. Recent focus on engineering applications has been directed towards the development of safe RL controllers. Previous works have proposed approaches to account for constraint satisfaction through constraint tightening from the domain of stochastic model predictive control. Here, we extend these approaches to account for plant-model mismatch. Specifically, we propose a data-driven approach that utilizes Gaussian processes for the offline simulation model and use the associated posterior uncertainty prediction to account for joint chance constraints and plant-model mismatch. The method is benchmarked against nonlinear model predictive control via case studies. The results demonstrate the ability of the methodology to account for process uncertainty, enabling satisfaction of joint chance constraints even in the presence of plant-model mismatch.
Original languageEnglish
Article number107630
JournalComputers & Chemical Engineering
Volume157
Early online date8 Dec 2021
DOIs
Publication statusPublished - 1 Jan 2022

Keywords

  • Bioprocess operation
  • Dynamic optimization
  • Machine learning
  • Optimal control
  • Safe reinforcement learning

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