Optimal operational control for industrial processes based on Q-learning method

It is difficult to accurately model productive processes and describe relationship between operational indices and controlled variables for complex modern industrial processes. How to design the optimal setpoints by using only data generated by operational processes, without requiring the knowledge of model parameters of operational processes, poses a challenge on designing optimal setpoints. This paper presents a state-observer based Q-learning algorithm to learn the optimal setpoints by utilizing only data, such that the real operational indices can track the desired values in an approximately optimal manner A simulation experiment in flotation process is implemented to show the effectiveness of the proposed method.