Abstract
Various development and validation methods for cyber-physical systems such as Controller-Hardware-in-the-Loop (C-HIL) testing strongly benefit from a seamless integration of (hardware) prototypes and simulation models. It has been often demonstrated that linking discrete event-based control systems and hybrid plant models can advance the quality of control implementations. Nevertheless, high manual coupling efforts and sometimes spurious simulation artifacts such as glitches and deviations are observed frequently. This work specifically addresses these two issues by presenting a generic, standard-based infrastructure referred to as virtual component, which enables the efficient coupling of simulation models and automation systems. A novel soft real-time coupling algorithm featuring event-accurate synchronization by extrapolating future model states is outlined. Based on considered standards for model exchange (FMI) and controls (IEC 61499), important properties such as real-time capabilities are derived and experimentally validated. Evaluation demonstrates that virtual components support engineers in efficiently creating C-HIL setups and that the novel algorithm can feature accurate synchronization when conventional approaches fail.
Highlights
The complexity of instances of cyber-physical systems, like smart grids, calls for mature testing and validation methods in order to ensure correct operation in safety-critical situations [1].In this context, model-based techniques allow representing parts of the overall system as virtual components and to simulate their dynamic behavior using numerical models, e.g., for evaluating control algorithms based on a virtual model of the physical system [2]
Evaluation demonstrates that virtual components support engineers in efficiently creating C-HIL setups and that the novel algorithm can feature accurate synchronization when conventional approaches fail
Model-based techniques allow representing parts of the overall system as virtual components and to simulate their dynamic behavior using numerical models, e.g., for evaluating control algorithms based on a virtual model of the physical system [2]
Summary
The complexity of instances of cyber-physical systems, like smart grids, calls for mature testing and validation methods in order to ensure correct operation in safety-critical situations [1] In this context, model-based techniques allow representing parts of the overall system as virtual components and to simulate their dynamic behavior using numerical models, e.g., for evaluating control algorithms based on a virtual model of the physical system [2]. Model-based techniques allow representing parts of the overall system as virtual components and to simulate their dynamic behavior using numerical models, e.g., for evaluating control algorithms based on a virtual model of the physical system [2] Such applications require the coupling of (time-continuous) simulation models with automation infrastructure, forming hybrid discrete/continuous systems.
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