论文标题
Neuralexplorer：使用神经网络对封闭环控制系统的状态空间探索
NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks
论文作者
论文摘要
在本文中，我们提出了一个框架，用于执行封闭环控制系统的状态空间探索。我们的方法涉及近似敏感性，并通过神经网络提出了新引入的逆敏感性概念。我们展示了如何将灵敏度和逆敏感性的近似值用于计算可达集的估计值。然后，我们概述了通过生成到达社区的轨迹来执行状态空间探索的算法。我们通过将其应用于标准的线性和非线性动力学系统，还将其应用于非线性混合系统以及基于神经网络的反馈控制系统来证明我们的方法的有效性。
In this paper, we propose a framework for performing state space exploration of closed loop control systems. Our approach involves approximating sensitivity and a newly introduced notion of inverse sensitivity by a neural network. We show how the approximation of sensitivity and inverse sensitivity can be used for computing estimates of the reachable set. We then outline algorithms for performing state space exploration by generating trajectories that reach a neighborhood. We demonstrate the effectiveness of our approach by applying it not only to standard linear and nonlinear dynamical systems, but also to nonlinear hybrid systems and also neural network based feedback control systems.
