Approximate abstractions of discrete-time controlled stochastic hybrid systems

ate Abstractions of Discrete-Time Controlled Stochastic Hybrid Systems Alessandro D’Innocenzo, Alessandro Abate, and Maria D. Di Benedetto — This work proposes a procedure to construct e abstraction of a controlled discrete-time stochastic hybrid system. The state space and the control space of the original system are partitioned by finite lattices according to some refinement parameters. The approximation errors can be explicitly computed, over time, given proper continuity assumptions on the model. We show that the errors can be arbitrarily chosen by increasing the partition accuracy. Similar bounds can be provided if a particular feedback control policy ted and quantized. The obtained abstraction can be interpreted as a Bounded-parameters Markov Decision Process, or a controlled Markov set-Chain, and can be used both for verification and control design purposes. We finally test the ate abstraction technique on a model from systems biology.

• Type: Conference
• Year: 2008
• Where: CDC
• Authors: Alessandro D'Innocenzo, Alessandro Abate, Maria Domenica Di Benedetto
• Keywords: Ate Abstraction, CDC 2008, Control Systems, Controlled Stochastic Hybrid, Stochastic Hybrid System