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

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.