HES machine: Harmonic equivalent state machine modeling for cyber-physical systems

Cyber-Physical Systems (CPS) are composed of computation, networking, and physical processes. Model-based design is a powerful technique to apply mathematical modeling in CPS design. A model of a physical system is the description of variations in some aspects and properties of the system such as motion, velocity, and pressure. The variations of physical quantities such as motion, velocity, and pressure as a function of time or space may be captured as a set of Ordinary Differential Equations (ODE). As such, system engineers model physical problems using mathematical equations, and then solve these equations to study the behavior of the target system. Therefore, fast executable models of physical systems are required especially for Model-based Predictive Control (MPC) algorithms or real-time Hardware-In-the-Loop (HIL) simulations. A complex physical model may comprise thousands of ODEs which pose scalability, performance and power consumption challenges. One approach to address these model complexity challenges are model-to-model transformation, and frameworks and tools that automate their implementation and development. In this paper, we present a framework to generate a Harmonic Equivalent State (HES) Machine model of the physical systems. One of the merits of the proposed state machine-based model is that the state machines can eliminate execution of compute-intensive and iterative tasks for describing the behavior of the physical systems. The model accommodates reconfigurable parameters that allow the user to have tradeoff between accuracy and execution time in CPS design. For validation purposes, we compare our model performance with state-of-the-art models in terms of execution time and accuracy. The simulation results indicate that our generated HES model executes 38% faster than ODE-based equivalent model with same level of model accuracy.

I. Introduction

Model-based design in Cyber-Physical Systems (CPS) provides abstraction and modeling techniques to integrate the dynamics of the physical processes with software and communication components. As opposed to desktop computing, CPS should be dynamically reconfigurable and adapt to changes in the environment. Application-specific disturbance models may be included to predict the effect of unknown physical disturbances that perturb system behavior and incorporate these effects on the input and state variables for control system design. Complex CPS applications such as in industrial machines, land vehicles, medical equipment, spacecraft, jet engines require new computer-aided methods for modeling, simulation and offline design. These methodologies are influ-enced by the need for lower time to market and higher quality, reliability and safety for the CPS design.