Learning control application to nonlinear process control

This paper presents the application of Reinforcement to nonlinear process control. Reinforcement Learning is a model-free technique based on online learning without supervision, with the objective of optimizing a cumulative future reward by resorting to experimentation with the system. The One-step-ahead Q-learning look-up table of reinforcement Learning Method is applied to a model of a pH neutralization process. Control actions are selected using the ε-greedy and softmax policies. The application shows the ability of the proposed method to control chemical processes with difficult, unknown or time-varying dynamics.