神经网络控制系统的无模型验证
Model-Free Verification for Neural Network Controlled Systems
AI总结：
针对未知动态的神经网络控制系统，提出数据驱动的半定规划方法，无需识别动态即可端到端形式化验证其稳定性与安全性。
摘要：
神经网络控制器在反馈控制系统中展现出实现卓越性能的潜力。尽管可以使用深度学习和强化学习方法高效训练神经网络，但为闭环特性提供形式化保证具有挑战性。主要困难源于非线性激活函数。一种流行的方法是对激活函数使用扇区界，从而进行鲁棒分析。这些方法在系统动态完全已知的假设下效果良好，然而这在实践中是不可能的。在本文中，我们提出数据驱动的半定规划，以形式化验证具有未知动态的神经网络控制线性系统的稳定性和安全性。所提出的方法直接进行端到端验证，无需识别动态。通过一个数值示例，我们验证了我们的方法在使用通过模仿学习训练的控制器的线性系统上的有效性。
英文摘要：
Neural network controllers have shown potential in achieving superior performance in feedback control systems. Although a neural network can be trained efficiently using deep and reinforcement learning methods, providing formal guarantees for the closed-loop properties is challenging. The main difficulty comes from the nonlinear activation functions. One popular method is to use sector bounds on the activation functions resulting in a robust analysis. These methods work well under the assumption that the system dynamics are perfectly known, which is, however, impossible in practice. In this paper, we propose data-driven semi-definite programs to formally verify stability and safety for a neural network controlled linear system with unknown dynamics. The proposed method performs verification directly from end-to-end without identifying the dynamics. Through a numerical example, we validate the efficacy of our method on linear systems with controller trained by imitation learning.
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。
