Learning-based near-optimal tracking control for industrial processes with slow and fast modes

This paper devotes to solving the optimal tracking control (OTC) problem of singular perturbation systems in industrial processes under the framework of reinforcement learning (RL) technology. The encountered challenges include the different time scales in system operations and an unknown slow process. The immeasurability of slow process states especially increases the difficulty of finding the optimal tracking controller. To overcome these challenges, a novel off-policy ridge RL method is developed after decomposing the singular perturbed systems using the singular perturbation (SP) theory and replacing unmeasured states using important mathematical manipulations. Theoretical analysis of approximate equivalence of the sum of solutions of subproblems to the solution of the OTC problem is presented. Finally, a mixed separation thickening process (MSTP) and a numerical example are used to verify the effectiveness. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper addresses the optimal tracking control problem of singular perturbation systems in industrial processes using off-policy ridge reinforcement learning method and singular perturbation theory. The challenges of different time scales and unknown slow process are overcome by mathematical manipulations and method improvement. Experimental results demonstrate the effectiveness of the proposed method.