Intelligent motion control is integral to modern cyber-physical systems. However, smart integration of intelligent motion control with commercial and industrial systems requires domain expertise, industrial ‘know-how’ of the production processes, and resilient adaptation for the various engineering phases. The challenge is amplified with the adoption of advanced digital twin approaches, big data and artificial intelligence in the various industrial domains. This paper proposes the IMOCO4.E reference framework for the smart integration of intelligent motion control with commercial platforms (e.g. from SMEs) and industrial systems. The IMOCO4.E reference framework brings together the architecture, data management, artificial intelligence and digital twin viewpoints from the industrial users of the large-scale ‘Intelligent Motion Control under Industry4.E’ (IMOCO4.E) consortium. The framework envisions a generic platform for designing, developing, and implementing novice and complex motion-controlled industrial systems. Refinements and instantiations of the framework for the IMOCO4.E industrial cases validate the framework’s applicability for various industrial domains throughout the engineering phases and under different constraints imposed on the industrial cases.

Recently, smart system integration was identified as a key competence for optimizing machines and robots. However, when one wants to ’tune’ the entire production process a step further is necessary. We should evaluate performance indicators (e.g. energy and material consumption) over the whole machine life cycle in order to align the production with circular economy principles. To reach that target MBSE (model-based system engineering) should be covered by advanced digital twin approaches which allow continuous monitoring of machine performance, predict the failures and maintenance. Moreover, artificial intelligence and machine learning must be used to process big data sets gathered from the production lines. This paper identifies a common set of technologies and building blocks suitable to solve above mentioned problems for a large variety of industrial domains (semiconductor production, health-care robotics, CNC1machining, high-speed packaging and others). It presents the first results of the large-scale IMOCO4.E2project and shows the pathways for application of the technology on specific machines (so-called pilots). The authors believe the ideas presented could be inspiring also in other domains.

This paper describes the design of a robust PI controller by considering several design criteria simultaneously (multi-objective optimization). Firstly, all controllers satisfying multiple frequency domain criteria (e.g. sensitivity function limits) are found in PI parameter plane using robustness regions method. Then, all suitable controllers are evaluated by the selected time-domain criterion, for example: Integral Error (IE), Integral of Time-weighted Absolute Error (ITAE) etc. Further, application of H2and H∞norms is shown in that context. The whole design toolchain is supported by advanced Matlab GUI which was used in the illustrative example. The described approach can be extended for PID controllers as well, for development of advanced autotuners and for teaching and training purposes.

Emerging mechatronic applications aim to work at limit performance and reliability while their size and operational space is getting restricted more and more. To reach those targets, often fast integration of customized components is necessary, either electronic systems, SW modules, sensors or actuators. Such diverse set of components needs special tool-chains and methods for fast customization and optimization respecting MBSE (model based system engineering) principles. Large-scale I-MECH project is a natural, fully industry driven initiative trying to follow those demands. The purpose of this paper is to describe its core scientific content, report initial milestones and show a variety of application where I-MECH components, so called building blocks, are being applied.

This paper deals with recent optimization trends of model-based software development in automotive industry. Over the past years, there has been a growing interest in software development optimization due to increasing amount and complexity of code, higher safety requirements, and increasing efficiency demands. Key elements of the process are identified and possible improvement is drawn based on experience during software development in ZF. One important direction of optimization is code generation directly from a model which is created in Matlab Simulink. A model transformation suitable for automatically generated code is described and possible advantages are shown in a real example.

UNLABELLED: : We present Galaxy Portal app, an open source interface to the Galaxy system through smart phones and tablets. The Galaxy Portal provides convenient and efficient monitoring of job completion, as well as opportunities for inspection of results and execution history. In addition to being useful to the Galaxy community, we believe that the app also exemplifies a useful way of exploiting mobile interfaces for research/high-performance computing resources in general. AVAILABILITY AND IMPLEMENTATION: The source is freely available under a GPL license on GitHub, along with user documentation and pre-compiled binaries and instructions for several platforms: https://github.com/Tarostar/QMLGalaxyPortal It is available for iOS version 7 (and newer) through the Apple App Store, and for Android through Google Play for version 4.1 (API 16) or newer. CONTACT: [email protected].