Optimal operational self-learning control for multi-time scale industrial with

Since the multi-time scales and strong nonlinearity are objectively existed in the practical industrial processes, it is extremely challenging to design control policy for optimizing the industrial operation. Therefore, this paper focuses on the multi-time scale optimal operational control (OOC) problem with unmodeled dynamics. Under the framework of reinforcement learning (RL) technology, a self-learning model-free method by integrating zero-sum game and singular perturbation (SP) theories is proposed in this paper, such that composite controllers with signal compensation can be found under which the suboptimal operation of the whole process can be achieved using only measured data. The remarkable highlight in this paper is the novel design method of the composite controllers with signal compensations for unknown nonlinear systems with multi-rate operations. Finally, the effectiveness of the proposed method is verified by a practical example and a numerical example.