Compensator-Based Self-Learning: Optimal Operational Control for Two-Time-Scale Systems With Input Constraints

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
$\infty$
control are employed for designing the composite controller. Finally, the efficacy is verified by an industrial mixed separation thickening process and a numerical example.