Self‐triggered control for linear systems based on hierarchical reinforcement learning

Abstract In this article, we propose a model‐free self‐triggered control approach for linear systems using a hierarchical policy framework. This framework splits the self‐triggered control approach into top and bottom‐level policies. The top‐level policy generates a triggering time interval based on an initial control strategy, while the bottom‐level policy creates a control inputs guided by the top‐level sub‐goal. This division ensures that both strategies have their own independent tasks and optimization goals, facilitating a model‐free iterative design process for self‐triggered control. The present structure integrates with a dual‐Actor Critic algorithm, utilizing two interconnected neural networks to approximate control and trigger policies. It reflects the framework of hierarchical reinforcement learning, wherein top‐level policies guide bottom‐level decision‐making. It fosters the model‐free design of self‐triggered controller, thereby enhancing the efficiency of the learning process. To validate the effectiveness of our proposed method, we conduct a series of numerical simulations.