Compositional Construction of Finite State Abstractions for Stochastic Control Systems
It addresses the computational bottleneck of constructing finite-state abstractions for large stochastic control systems by enabling compositional construction, which is crucial for practical controller synthesis.
This paper extends compositional abstraction methods for controller synthesis to stochastic control systems, showing how to construct finite-state abstractions that are disturbance bisimilar to the original system under stability conditions, enabling scalable synthesis for networks.
Controller synthesis techniques for continuous systems with respect to temporal logic specifications typically use a finite-state symbolic abstraction of the system. Constructing this abstraction for the entire system is computationally expensive, and does not exploit natural decompositions of many systems into interacting components. We have recently introduced a new relation, called (approximate) disturbance bisimulation for compositional symbolic abstraction to help scale controller synthesis for temporal logic to larger systems. In this paper, we extend the results to stochastic control systems modeled by stochastic differential equations. Given any stochastic control system satisfying a stochastic version of the incremental input-to-state stability property and a positive error bound, we show how to construct a finite-state transition system (if there exists one) which is disturbance bisimilar to the given stochastic control system. Given a network of stochastic control systems, we give conditions on the simultaneous existence of disturbance bisimilar abstractions to every component allowing for compositional abstraction of the network system.