自治系统具有安全保证的启发式间歇控制
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems
摘要：
自治系统的控制方案通常以预期任何情况下的最坏情况的方式设计。然而，在运行时，可能存在利用特定环境和操作上下文的特征以进行更有效控制的机会。在这项工作中，我们开发了一个在线自动控制框架，该框架将形式验证与基于模型的优化和深度强化学习相结合，以机会性地跳过某些控制计算和驱动，从而节省驱动能量和计算资源，而不会影响系统安全。自适应巡航控制系统的实验表明，我们的方法可以实现显着的能源和计算节省。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
