NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks

In this paper, we propose a framework for performing state space exploration of closed loop control systems. For closed loop control systems, we introduce the notion of inverse sensitivity function and present a mechanism for approximating inverse sensitivity by a neural network. This neural network can be used for generating trajectories that reach a destination (or a neighborhood around it). We demonstrate the effectiveness of our approach by applying it to standard nonlinear dynamical systems, nonlinear hybrid systems, and also neural network based feedback control systems.