Joachim Denil associate professor

Research Joachim Denil

Research team

FlexIA – Flexible Industrial Automation. 01/05/2024 - 30/04/2025

Abstract

In the past, industrial automation predominantly relied on Programmable Logic Controllers (PLCs), offering an unparalleled reliability in controlling a wide range of manufacturing processes. The demand for increased efficiency, adaptability, and intelligent decision-making has propelled the integration of advanced algorithms in industrial automation projects. This shift towards such promising software-intensive automation poses some challenges. First, the integration of advanced algorithms, such as artificial intelligence (AI) models, with legacy installed automation infrastructures hits its limits when implemented in closed software-hardware ecosystems. Technology suppliers constrain their customers within the boundaries of their proprietary tools and hardware, thereby limiting hardware-software flexibility. Second, vendor-specific solutions do not support code reuse across different software-hardware suppliers, also called vendor lock-in. This results in costly redesigns when transitioning to different hardware platforms. The primary goal of the FlexIA Proof-of-Concept (PoC) project is to provide true hardware-software flexibility for the development of software-intensive automation systems by introducing the develop once, deploy anywhere strategy. This will be facilitated by developing and commercializing the FlexIA platform, a vendor-agnostic toolkit for developing and deploying software-intensive automation systems. The platform enables the creation and management of complex automation applications regardless of whether the hardware is realized on PLC or microcontroller. This contributes to the secondary goal of the project, namely reducing the vendor lock-in. Primary valorisation goal of this FlexIA PoC project is to conduct a thorough market study to determine the current and future needs of the relevant industrial stakeholders. This market study will help to determine an optimal go-to-market strategy to successfully commercialize the FlexIA platform. At the same time, it aligns the FlexIA technology features with the needs of the different stakeholders.

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  • Research Project

Nexor - Cyber-Physical Systems for the Industry 4.0 era 01/01/2021 - 31/12/2026

Abstract

The fourth industrial revolution (Industry 4.0 as it is commonly referred to) is driven by extreme digitalization, enabled by tremendous computing capacity, smart collaborating machines and wireless computer networks. In the last six years, Nexor — a multi-disciplinary research consortium blending expertise from four Antwerp research labs — has built up a solid track record therein. We are currently strengthening the consortium in order to establish our position in the European eco-system. This project proposal specifies our 2021 - 2026 roadmap, with the explicit aim to empower industrial partners to tackle their industry 4.0 challenges. We follow a demand driven approach, convincing industrial partners to pick up our innovative research ideas, either by means of joint research projects (TRL 5—7) or via technology licenses.

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  • Research Project

Dotation for the structural collaboration with Flanders Make. 01/01/2021 - 31/12/2024

Abstract

Flanders Make's mission is to strengthen the international competitiveness of the Flemish manufacturing industry on the long term through industry-driven, precompetitive, excellent research in the field of mechatronics, product development methods and advanced production technologies and by maximizing valorisation in these areas.

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  • Research Project

Deterministic and inexpensive realizations of advanced control (DIRAC-SBO). 01/09/2020 - 28/02/2025

Abstract

The mechatronic machine building and manufacturing industry is currently facing various control challenges that simple PID controllers and alike fail to address: systems are increasingly complex, need to comply to constraints, need to account for economic objectives and effectively cope with valuable preview information. Model Predictive Control (MPC) is the only advanced control approach able to address all these challenges, and this thanks to its model-based and optimization-based nature. Yet MPC's optimization-based nature currently impedes wide adoption in industrial mechatronic systems: current MPC implementations are expensive in terms of computational and memory resources, computation time is non-deterministic and hence MPC algorithms cannot be certified to operate at a given sampling rate, MPC development and deployment is not straightforward and comes with a high engineering cost because proper tools are missing. The project "Deterministic and Inexpensive Realizations of Advanced Control" (DIRAC) aims for a breakthrough of MPC in the mechatronic/machine building/manufacturing industry by resolving all impeding elements through accomplishments that revolve around the three keywords in its title:  - Deterministic: Novel MPC algorithms and approaches will be developed that can run reliably at a given sampling rate as well as methods to verify their worst-case computation times and control performance.  - Inexpensive: Implementations will be created that approximate "full-blown" (=online nonlinear optimization with high fidelity models) MPC and hence can run on inexpensive computational hardware with a quantifiable impact on control performance that is computed upfront.  A modular MPC toolbox will be developed facilitating the development, tuning and validation of advanced control at manageable engineering cost. - Realizations: We will demonstrate the MPC toolbox and potential of MPC on industrially relevant demonstrators and validation cases in order to break the status-quo in control practices, foster take-up and inspire Flemish industry.  The overarching tangible reusable generic result of this project is a toolbox that simplifies design of nonlinear MPC controllers and brings methodological advances in solvers, approximations and validation techniques to the fingertips of control practitioners.

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Meaningful and scaleable reuse and composition of models, with frames. 01/01/2019 - 31/12/2024

Abstract

The engineered systems , such as autonomous self-driving vehicles, that we (want to) design and build, are characterized by an ever increasing complexity , offering ever more advanced functionality and comfort. At the same time, the demands on energy efficiency and cost, but also on safety and reliability of those systems, become more stringent, in a quest for some form of optimal, fit-for-purpose designs. Furthermore, in a circular economy, we wish to take into account not only the product, but an ecosystem, spanning entire families of related products, over their entire life-cycle, including production, maintenance, and recycling. The fact that such advanced systems can be built today is largely thanks to the ubiquitous use of models . Models, encoding (for reuse) our knowledge about various aspects of a system or system component, can namely be used for "virtual experimentation" : to perform computer simulations to answer "what if" questions. Such questions allow us to explore different design alternatives. It is this capability that is fueling the 4 th industrial revolution. Models in complex engineered systems vary widely in nature and purpose. They may describe structure and behaviour of systems in different domains such as mechanical, electrical, software, and networks, or different views on the systems such as the stability/control view, the safety view, and the cost/efficiency view, at different levels of abstraction/detail/fidelity. The may also be used to describe and even prescribe (for automation purposes) the complex, concurrent development processes. Process models can be used for "what if" analysis of the engineering processes themselves, leading not only to optimal products, but also to optimal time-to-market. When "what if" analysis is automated , exploring billions of alternatives efficiently in a computer, reaching optimal products/production designs can be accelerated , taking a matter of days or weeks on a cloud computing infrastructure as opposed to the decades required for organic convergence over generations of human engineering improvements. Engineering is however hitting a wall, keeping us from a truly exponential leap in complex systems development . Though advanced computer support exists in the form of modelling languages, model management tools, simulators, etc. for "what if" analysis, managing the meaningful and correct (re)use of models is still a mostly human enterprise, for which no rigorous foundations nor advanced tooling exist. Being constrained by human capabilities, it is costly, slow, and error prone. In some important, yet restricted, areas such as Electronic Design Automation, such foundations and tooling do exist (and fuel a thriving billion $ market). For truly complex, multi-domain systems, knowledge is scattered, often either in experts' minds, or in the best case in text documents and spreadsheets. In this project, we propose to develop a foundational framework as well as prototype tooling for the computer-assisted/automated meaningful (re)use of models . The key to our approach is that we will "eat our own dog food" : we will now apply advanced modelling language engineering, model transformation, property specification, modelling and simulation techniques we have helped develop over the last decades, to explicitly model and reason about the context in which models can be meaningfully (re)used. We call such models "frames" after the original, but incomplete "experimental frames" idea proposed by Bernard Zeigler in the 1980s. Concretely, we will start by using our experience with the modelling language Modelica (for physical systems) and DEVS (for discrete-event modelling of software and networks) to develop the theoretical foundations and application of frames, initially on a representative autonomous vehicle case .

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Digital Twins for Continuous Deployment in Model-Based Systems Engineering of Cyber-Physical Systems. 01/11/2020 - 31/10/2024

Abstract

Cyber-Physical Systems (CPS) are required to operate over a longer lifetime. As such, the initial requirements can be changed, requiring the system to be updated continuously. These updates to the system must be rolled out continuously (Continuous Deployment) throughout the system's lifetime. The DevOps methodology provides a structured, quality assuring way to do so, as it integrates Development and Operations of a system in a continuous cycle. DevOps is generally applied in software development, however in the design of CPS, which follows a Model-Based Systems Engineering (MBSE) approach, it is not. This is because many challenges remain in the application of DevOps in MBSE. My focus is to create the foundations for continuous deployment of safety-critical CPS using digital twins of the CPS.

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Collaborative Design Facility (CDF-Infra). 01/06/2020 - 30/09/2023

Abstract

With the evolution towards smart, interconnected products and production systems, the design of physical systems becomes more complex. Traditionally, the design process is rather sequential: engineers from different domains work on their own specific challenges and results are passed to the next group in the development process. This often leads to lengthy iterations. To solve this, companies are shifting to concurrent and multidisciplinary collaboration where engineers from different disciplines work in parallel on the same design. The organization and management of this concurrent process, without suitable HW and SW infrastructure support, requires time and resources which are drawn away from the core engineering tasks. The complexity increases further when the engineers are distributed across multiple locations and/or when different companies (OEMs, Tier1, …) are involved in the collaboration. The facility developed in this project will support collaborative model-based design.

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Foundations for Self-Adaptive Abstraction and Approximation in Real-time Cyber-Physical Systems (of Systems). 01/05/2020 - 30/04/2024

Abstract

Cyber-physical systems (CPS) are engineered systems that have a tight integration between the cyber part (computation and networking) and its physical components. Examples include but are not limited to industry 4.0, automotive and aerospace. To allow decisions to be made in a CPS (strategic control, tactical control and, low-level control), decision models are used. These models use input from sensors, but also from other supporting processes, e.g. predictions over the state of its contexts, to come to a control decision. The decision processes are implemented in software that runs on embedded hardware and is commonly real-time constrained, meaning that the time at which the decision is taken, is as import as the decision itself. In literature several techniques are available to reduce the computational cost of executing models by using abstraction and approximation (e.g. surrogate modelling). This reduced cost would make the process to come to a decision easier (scheduling) and would require less computational resources. However, we still need to be sure that the decision process is robust against approximations and uncertainties in these models. Furthermore, an approximated and/or abstracted model is most probable not valid in all the different contexts the system will be in. To enable this, the system should be able to switch at run-time between different abstractions and approximations. Therefore, this project will create the foundations to reason about dynamically adapting the decision models and prediction models with different abstractions and approximations depending on the context of the system. The project will result in a framework with supporting modelling languages, methods and proof-of-concept tools to reason on the trade-off between uncertainty (from the approximation) and the real-time behavior of the system.

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Framework for systematic design of digital twins (DTDesign). 01/04/2020 - 30/09/2023

Abstract

This project aims at developing a framework, comprising a methodology and supporting tools, for the systematic and efficient design of Digital Twins providing answers to two question types: (i) production parameters - product performance correlation and (ii) faults detection and diagnosis. The purpose of the framework is to support the user in choosing which data sets and models to combine and how to deploy them (Digital Twin implementation) to get an answer to the posed questions based on application specific requirements and criteria. The final goal is to use the developed framework to efficiently design Digital Twins and implement them for seven industrial use cases.

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Simulation based testing of large scale internet of things applications. 01/01/2019 - 31/12/2021

Abstract

The goal of this project is to introduce a simulation based methodology which will be used to cope with the scalability constraints of modern IoT software testing, and more specifically the testing of ultra large scale systems with emergent behavior. With IoT becoming more mainstream and with the rise in the amount of devices getting interconnected, the complexity and scale of the IoT landscape will largely increase. This interoperability between IoT devices and actuators of all sorts will prove to be vital for future IoT applications. As a result of the increased scale and diversity and because of modern decentralized IoT architectures such as Edge computing, we see that a whole new type of IoT application will gain importance. A type of application where local decentralized interaction between devices and actors will lead to a global emergent behavior. The concept of emergence can be compared to a flock of birds, where local interactions between individual birds lead to a global optimized behavior. This idea is also very relevant in IoT, imagine for example a smart traffic light application where local interactions between traffic lights could lead to a global optimized traffic flow. This type of IoT application will however lead to major difficulties with regards to application validation, testing and calibration. That is because in order for realistic emergent behavior to arise, the IoT application will need to be executed in a large-scale and diverse environment. An environment that resembles the eventual operational environment. Deploying such applications to a real-life isolated IoT testbed would be impractical as the cost of setting up such an environment at a realistic scale is too high and requires too much effort in the early stages of development. Instead of relying on expensive test beds, we propose a large scale simulation based approach. Such a simulation -based system needs to incorporate hundreds of thousands of virtual sensors interacting among each other and with the environment. The behavior of these systems will need to be modeled carefully. However, this leads to additional technical challenges. Also all virtual sensors in the system should be continuously active to interact in a real-time fashion with other systems. That is because an important part of the behavior of conventional IoT systems and EBI systems is controlled by an IoT middle-ware, the simulated entities should be able to interact with the middleware as if they were real-life IoT entities. We refer to this as software-in-the-loop (SIL) simulation. Because of this real-time requirement, a great amount of simulation entities should run in parallel which highly increases the computational complexity. Solely relying on state-of-the-art large-scale simulation techniques is insufficient. The contribution of this project is focused on the creation of a methodology for running real-time, large-scale simulations for testing and analyzing both conventional IoT systems and emergent behavior based IoT systems. We will focus on two major tracks, in the first we will reduce the computational complexity by dynamically increasing abstraction levels of simulation models and in the second track we aim at reducing network communication overhead of distributed simulations by optimizing the partitioning of simulation entities over multiple simulation servers.

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  • Research Project

Product-Assemby Co-Design (PACo). 01/09/2018 - 31/08/2023

Abstract

The Product-Assembly Co-Design (PACo) project is a project within the scope of the cluster Design & Optimisation of Flanders Make. The project aims at bridging the gap between product design and assembly system design by incorporating assembly knowledge into the early stages of the product development. Today, most companies consider assembly aspects later in the design process, often in a manual way, solely relying on the experience of assembly engineers. This leads to numerous design changes later on, causing significant extra costs. The current industrial context requires companies to aim at a first-time-right, down to lot size 1 at the cost of volume production strategy. Hence, considering assembly aspects too late or in a trial-and-error way is no longer an option. All companies involved in the user group of this project indicate a clear need to support their engineers with methods and software tools enabling assessment of assembly complexity in an early design stage, allowing co-optimization of product performance with ease-of-assembly in a quantitative way, and allowing trade-off analysis of various solutions. As these software tools are beyond the state-of-the-art, the research partners (FM-CodesignS, FM-ProductionS, AnSyMo/CoSys-lab, DMMS, and EEDT) will join forces to shift the state-of-the-art in product-assembly co-design, aiming at the following innovation goals: (1) a software environment for the formalization of assembly knowledge (e.g. Design-for-Assembly rules, assembly complexity metrics), (2) tools and algorithms for automated multi-objective optimization of the early-stage design of a product, taking into account the product performance and its assembly complexity, (3) tools and algorithms to automatically find the optimal assembly process (order of steps) and assembly system (resources allocation), for a given product design and a framework for the co-design of both product and its assembly system by combining both 1) and 2) in a semi-automated workflow.

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Automated and simulation based functional safety engineering methodology (aSET_ICON). 01/03/2018 - 31/08/2020

Abstract

Due to the trend towards more complex safety-related products combining mechanics, electric components, electronics and software, their design and development become more complex, leading to longer development times and higher costs as well as higher risks on errors with highly manual safety engineering processes. The goal of the aSET-project to develop methodologies to automate the functional safety engineering process to make the process less error prone and to reduce the required design time and cost compared to the current manual state-of-the-practice. More specifically, the objectives of the project are: (i) the development of a Functional Safety Formal model implemented in a persistent way enabling the intrinsic coupling between all Functional Safety artefacts requested by ISO26262; (ii) the development of a method and demonstrator tooling for the translation of textual requirements into mathematical equations (that can serve as a design contract for the actual hardware design) that describe functionality of E/E/PE enabling the automation of HARA with the help of a functional E/E/PE model and plant model; (iii) the validation of these methods in a generic use case as well as in different industrial use cases demonstrating their functionality and the targeted design time and cost gains.

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  • Research Project

Dotation for the structural collaboration with Flanders Make. 01/01/2018 - 31/12/2020

Abstract

Flanders Make's mission is to strengthen the international competitiveness of the Flemish manufacturing industry on the long term through industry-driven, precompetitive, excellent research in the field of mechatronics, product development methods and advanced production technologies and by maximizing valorisation in these areas.

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  • Research Project

EMPHYSIS - Embedded systems with physical model in the production code software. 01/10/2017 - 31/01/2021

Abstract

The major goal of the project is to enhance production code of embedded control systems in automotive vehicles in order to improve the performance of the underlying system: faster and safer operation, reduced energy consumption, reduced emission and reduced maintenance costs. Additionally, cost and time for the software development of these embedded systems shall be reduced. This is achieved by providing physics-based models from modelling and simulation tools in an automated and standardized way on electronic control units (ECU). By this approach physical models predicting the behaviour of the whole operating region of the target system are used in observers/virtual sensors, model-based diagnosis, or in advanced control algorithms (e.g., inverse models, non-linear dynamic inversion, model-predictive control) on ECUs to achieve significantly better vehicle performance.

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INES - Innovation in the development of Electronic Systems for Aeronautics . 01/09/2017 - 31/08/2019

Abstract

The INES project, a project consortium with its Flemish component Siemens Industry Software (with its subcontractor Siemens CT) and University of Antwerp, and its Spanish component Boeing Research & Technology Europe (with its subcontractors GMV and Skylife), coordinated through the Eureka program, aims to develop a realistic, innovative and implementable MBSE process as well as identify a series of software tool innovations that cover the complete development and life cycle of avionics systems (understood as the electronic systems of the aircraft including its avionics controller algorithm, software and hardware), which would, within two years, offer an paradigm shift for the development of aircraft electronic systems (avionics), whose objective would be to achieve much higher levels of quality at a reduced development cost with respect to current technology.

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Timing Analysis for Real-Time Embedded Multicore Software. 01/10/2015 - 30/09/2019

Abstract

Multicore processors are increasingly used in mechatronic applications and need to endorse the realtime requirements of the related embedded software. In spite of their huge processing power, certain operational conditions may arise in which they show longer software execution times than reasonably expected. In this project, we will elaborate software timing analysis techniques which will lead to better configurations of multicore platforms with respect to the software execution time and more specifically to the unexpected outliers mentioned above. To this purpose, we will propose a modelling language that will allow for a formal description of the timing properties of real-time embedded multicore software. This modelling language will enable formal methods for schedulability analysis and design space exploration methods, such that timing outliers can be eliminated by suggesting alternative configurations for the multicore platform.

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Study of the technical impact of the AUTOSAR-standards on automotive software. 01/10/2008 - 28/02/2013

Abstract

In the area of automotive electronics, software is becoming increasingly more prominent. The AUTOSAR consortium aims to consolidate this, but the technical impact of their standards is not sufficiently known. Therefore, we will investigate the technical footprint of these standards. This will lead to a more efficient use of performance and memory in automotive embedded systems.

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