Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints
Jinna Li, Mingwei Yang, Frank L. Lewis, Meng Zheng
Abstract
The practical industrial operation systems are not ideally immune to the effect of unmodeled dynamics and the industrial processes generally are operated at multitime-scales, which cause troubles for optimizing the industrial operation. The novelty of this article is that a self-learning composite compensation control method is developed for two-time-scale optimal operation systems, with well dealing with unmodeled dynamics, unknown operation process and input constraints. First, the two-time scales system is decomposed into fast and slow subsystems based on singular perturbation theory. Then, the critic-only reinforcement learning technique and H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> control are employed for designing the composite controller. Finally, the efficacy is verified by an industrial mixed separation thickening process and a numerical example.