We present SimpleMG, a new, provably correct design methodology for runtime assurance of microgrids (MGs) with neural controllers. Our approach is centered around the Neural Simplex Architecture, which in turn is based on Sha et al.'s Simplex Control Architecture. Reinforcement Learning is used to synthesize high-performance neural controllers for MGs. Barrier Certificates are used to establish SimpleMG's runtime-assurance guarantees. We present a novel method to derive the condition for switching from the unverified neural controller to the verified-safe baseline controller, and we prove that the method is correct. We conduct an extensive experimental evaluation of SimpleMG using RTDS, a high-fidelity, real-time simulation environment for power systems, on a realistic model of a microgrid comprising three distributed energy resources (battery, photovoltaic, and diesel generator). Our experiments confirm that SimpleMG can be used to develop high-performance neural controllers for complex microgrids while assuring runtime safety, even in the presence of adversarial input attacks on the neural controller. Our experiments also demonstrate the benefits of online retraining of the neural controller while the baseline controller is in control

提出基于屏障证明的Simplex（BC-Simplex）算法，旨在同时保证连续动态系统的安全和性能；算法采用屏障证明来证明基线控制器确保安全，基于屏障证明的泰勒展开式推导出了控制器切换的条件，并针对一个包含神经网络控制器的微电网进行了实验验证，结果表明该算法能够自动导出控制器切换条件，且还可以在神经控制器被攻击的情况下仍然保证系统安全。

基于障碍证明的单纯形架构及其在微电网中的应用