A Trajectory-Based Stochastic Approach to Symbolic Control

This paper presents two innovative approaches to design symbolic controllers for dynamical systems. The first novelty involves a new trajectory-based strategy for defining the states of a symbolic model, which provides a more accurate representation of the system's dynamics than the traditional grid-based technique. The second novelty concerns using a Bounded-parameter Markov Decision Process rather than a Finite Transition System to model the behavior of a symbolic model. This procedure allows for handling the system's stochastic behavior and considers uncertainties. The effectiveness of the novel approaches presented is demonstrated through numerical results.