Symbolic models for nonlinear control systems using approximate bisimulation

Abstract

Control systems are usually modeled by differential equations describing how physical phenomena can be influenced by certain control parameters or inputs. Although these models are very powerful when dealing with physical phenomena, they are less suitable to describe software and hardware interfacing the physical world. This has spurred a recent interest in describing control systems through symbolic models that are abstract descriptions of the continuous dynamics, where each symbol corresponds to an aggregate of continuous states in the continuous model. Since these symbolic models are of the same nature of the models used in computer science to describe software and hardware, they provided a unified language to study problems of control in which software and hardware interact with the physical world. In this paper we show that every incrementally globally asymptotically stable nonlinear control system is approximately equivalent (bisimilar) to symbolic model with a precision that can be chosen a-priori. We also show that for digital controlled systems, in which inputs are piecewise-constant, and under the stronger assumption of incremental input-to-state stability, the symbolic models can be obtained, based on a suitable quantization of the inputs.