Abstract Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user’s needs and their environment, demanding the adaptation of the developed DT systems. DTs are developed for a specific purpose, which generally entails the monitoring, analysis, simulation or optimisation of a specific aspect of an actual system, referred to as the actual twin (AT). As such, when the twin system changes, that is either the AT itself changes, or the scope/purpose of a DT is modified, the DTs usually evolve in close synchronicity with the AT. As DTs are software systems, the best practices or methodologies for software evolution can be leveraged. This paper tackles the challenge of maintaining a (set of) DT(s) throughout the evolution of the user’s requirements and priorities and tries to understand how this evolution takes place. In doing so, we provide two contributions: (i) we develop , a visual notation form that enables reasoning on a twin system, its purposes, properties and implementation, and (ii) we introduce a set of architectural transformations that describe the evolution of DT systems. The development of these transformations is driven and illustrated by the evolution and transformations of a family home’s DT, whose purpose is expanded, changed and re-prioritised throughout its ongoing lifecycle. Additionally, we evaluate the transformations on a laboratory-scale gantry crane’s DT.

Model Predictive Control (MPC) is an advanced control technique that uses a model to predict the system’s future states to optimize its control actions. However, it can be computationally intensive and challenging to provide real-time guarantees. Controller surrogates, based on machine learning techniques, try to overcome this barrier. Training neural networks as surrogates for MPCs often involves intricate workflows, including data generation, model training, and iterative refinement, which can be challenging to manage efficiently. In this paper, we introduce SLWM, a Library that helps create complex workflows used when training a (deep) neural network to act as a surrogate of an MPC. Through a case study of an inverted pendulum, we demonstrate its effectiveness in implementing workflows like Dagger. The results of this implementation show that SLWM fulfils the requirements such as modularity, readability, simplified data management and reliability. Lastly, we list a few potential future applications of SLWM and the extensions of SLWM required for some of them.

Real-time safety-critical systems using nonlinear model predictive control (NMPC) require guaranteed worst-case execution time (WCET) bounds. Measuring the WCET of NMPC is challenging. We compare three model-informed ways of generating WCET measurement tests. The first two involve uniform partitioning of the state space and simulation; the third uses the concept of complexity certification for active-set solvers. We validate our approach on two benchmarks: an inverted cart pendulum and motion planning of a bicycle. Our results demonstrate a trade-off between the number of tests and the degree of WCET underestimation.

Digital twins (DTs) are prevalent throughout industrial domains as evidenced by the rapid pace of experience reports in the literature.However, there remains disagreement about the precise definition of a DT and the essential characteristics in the DT paradigm, such as the scope of the system-under-study and the time-scale of its communication with the DT.These experience reports could therefore be hampering further classification and research insights by not reporting all of these relevant details about the DT solutions.We address these concerns by providing a conceptual structure for DTs as a common understanding and checklist for researchers and practitioners to precisely describe the characteristics and capabilities of their DT solutions.We express five experience reports using our structure to demonstrate its applicability and role as a guideline to improve the reporting of characteristics and increase the clarity of future experience reports.

In Domain-Specific Modelling (DSM) the general goal is to provide Domain-Specific Modelling Languages (DSMLs) for domain users to model systems using concepts and notations they are familiar with, in their problem domain. Verifying whether a model satisfies a set of requirements is considered to be an important challenge in DSM, but is nevertheless mostly neglected. We present a solution in the form of ProMoBox, a framework that integrates the definition and verification of temporal properties in discrete-time behavioural DSMLs, whose semantics can be described as a schedule of graph rewrite rules. Thanks to the expressiveness of graph rewriting, this covers a very large class of problems. With ProMoBox, the domain user models not only the system with a DSML, but also its properties, input model, run-time state and output trace. A DSML is thus comprised of five sublanguages, which share domain-specific syntax, and are generated from a single metamodel. Generic transformations to and from a verification backbone ensure that both the language engineer and the domain user are shielded from underlying notations and techniques. We explicitly model the ProMoBox framework's process in the paper. Furthermore, we evaluate ProMoBox to assert that it supports the specification and verification of properties in a highly flexible and automated way.

Cyber-physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. In these situations, a traditional (sequential) design process becomes inefficient in finding good designs options. Instead, an integrated approach is needed where parameters in both the control and embedded domain can be chosen, evaluated and optimized to have a good solution in both domains. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem. In this paper, we show how domain knowledge can be used to guide the design-space exploration process for an advanced control system and its deployment on embedded hardware. We use domain knowledge, captured in an ontology, to reason about the relationships between parameters in the different domains. This leads to a stepwise design space-exploration process where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. In this process, we make use of cross-domain evaluation to find feasible design options with good system-level performance.

Simulation-based analyses of Cyber-Physical Systems are fundamental in industrial design and testing approaches. The utility of analyses relies on the correct configuration of the simulation tools, which can be highly complicated. System engineers can normally judge the results, and either evaluate multiple simulation algorithms, or change the models. However, this is not possible in a co-simulation approach. Co-simulation is a technique to perform full-system simulation, by combining multiple black-box simulators, each responsible for a part of the system. In this paper, we demonstrate the difficulty of correctly configuring a co-simulation scenario using an industrial case study. We propose an approach to tackle this challenge by allowing multiple engineers, specialized in different domains, to encode some of their experience in the form of hints. These hints, together with state-of-the-art best practices, are then used to semi-automatically guide the configuration process of the co-simulation. We report the application of this approach to a use case proposed by our industrial partners, and discuss some of the lessons learned.

The embedded software in some classes of contemporary cyber-physical systems (CPS) is required to run models of its physical environment in real time and with high accuracy and precision. The question to which extent the embedded software can achieve these requirements largely depends on the properties of the embedded platform (i.e. embedded hardware and middleware). In this paper, we focus on the modelling of some of the properties of an embedded platform and on the co-simulation of these properties with the physics-based models. This will result in an assessment of the influence of the embedded properties on the performance of the physics-based models in early stages of the development of embedded software for CPS.

Embedded systems, with their tight technology integration, and multiple requirements and stakeholders, are characterized by tightly interrelated processes, information and tools. Embedded systems will as a consequence be described by multiple, heterogeneous and interrelated descriptions such as for example requirements documents, design and analysis models, software and hardware descriptions. We refer to a system designed this way as a multi-view (MV) system. The main contribution of this paper is a characterization of model-based approaches to MV systems. The characterization takes three main perspectives for the relations between viewpoints: semantic relations (content), relations over time (process), and manipulation of views (operations). We complement these perspectives by investigating MV system challenges and by a survey of related approaches. The characterization aims to provide a basis for a better understanding, design and implementation of MV systems, and thereby to overcome the current fragmented points of view on integrated multi-view modeling (MVM).

Domain-specific modeling (DSM) is one major building block of model-driven engineering. By moving from the solution space to the problem space, systems are designed by domain experts. The benefits of DSM are not unique to the design of systems, the specification and verification of desired properties of the designed systems by the help of DSM seems the next logical step. However, this latter aspect is often neglected by DSM approaches or only supported by translating design models to formal representations on which temporal properties are defined and evaluated. Obviously, this transition to the solution space is in contradiction with DSM.