摘要：AbstractIn recent years, the trade-off between communication cost and control performance has become increasingly important. Among various control architectures, self-triggered controllers decide the next communication (state observation and action determination) timing online in a state-dependent manner. However, it should be emphasized that most of the existing methods do not explicitly evaluate the resulting long-run communication cost. In this paper, we formulate an optimal continuous-time self-triggered control problem that takes the communication cost into an explicit account and proposes a design method based on deep reinforcement learning.
关键词：KeywordsSelf-triggered controldata-driven control system designmachine learning
