论文标题
机会性的间歇性控制，并为自主系统提供安全保证
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems
论文作者
论文摘要
自治系统的控制方案通常以预期在任何情况下最坏的情况进行设计。但是，在运行时，可能会有机会利用特定环境和操作环境的特征以进行更有效的控制。在这项工作中，我们开发了一个在线间歇控制的框架，该框架将正式验证与基于模型的优化和深度强化学习结合在一起，以机会性地跳过某些控制计算和驱动，以节省驱动能源和计算资源，而无需损害系统安全。自适应巡航控制系统上的实验表明，我们的方法可以实现大量的能量和计算节省。
Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverage the characteristics of specific environment and operation context for more efficient control. In this work, we develop an online intermittent-control framework that combines formal verification with model-based optimization and deep reinforcement learning to opportunistically skip certain control computation and actuation to save actuation energy and computational resources without compromising system safety. Experiments on an adaptive cruise control system demonstrate that our approach can achieve significant energy and computation savings.
