Package: mlpro-mpps
MLPro-MPPS - A Customizable Framework for Multi-Purpose Production Systems in PythonMLPro-MPPS provides functionalities to design and develop customizable multi-purpose production systems in Python. This framework is integrated with MLPro (https://mlpro.readthedocs.io) and inherits several basic functionalities from MLPro. The user can design a production system simulation from the lowest component level (sensors and actuators) until the combination of them in the form of MPPS. The other possibility is to use the ready-to-use components in the pool of objects. Moreover, since MLPro-MPPS is compatible with MLPro, it is possible to utilize MLPro-MPPS as an environment in MLPro-RL, as a game board in MLPro-GT, or as a state transition function in an MLPro-BF-Systems. Hence, MLPro-MPPS is reusable and powerful.Getting StartedTo get started with MLPro-MPPS, you can begin with the following tasks:Installation from PyPIpipinstallmlpro-mppsDependenciesPlease readrequirements.txtIntroduction VideoYou can see a video of the introduction of MLPro-MPPS at the 2nd IEEE Industrial Electronics Society Annual Online Conference by clickingthis link.First StepsAfter installing MLPro-MPPS and its dependencies, we suggest starting with the ready-to-run examples:HOWTO 001 - SETTING UP COMPONENTS AND MODULESHOWTO 002 - SETTING UP MPPSHOWTO 003 - MPPS IN REINFORCEMENT LEARNINGHOWTO 004 - MPPS IN GAME THEORYAdditionally, the class diagram of the basic function is available inthis directory. The ready-to-use components and MPPS samples can be found inthis directory.Key Features and Functionalitiesa) Providing base classes of components in a clean structureIncluding base classes for a sensor, an actuator, and a component state (required for simulation)b) Versatile and configurableMPPS is versatile, which means that it has a high degree of flexibility, where the users can set up a production system with as many sensors, actuators, components, and modules as possibleMoreover, MPPS is also not restricted to a modular production system, but also applicable in any form of technical systemsc) Simplification of measurements and designs of the MPPS-based systems' dynamicsWhen MPPS is simulated, the dynamics of the sensors and component states are affected by the actual status of the actuators. In the simulation mode, their dynamics are measured through a mathematical calculation that is defined by theTransferFunction class of MLProThe mathematical calculation can be attached to the lowest level of the components, such as sensors and component states, which make them reusable and reproducible.d) Well-integrated toMLProPossibility to convert MPPS into the reinforcement learning environment in MLPro-RL or game theory game board in MLPro-GTPossibility to reuse functionalities fromMLProDevelopmentConsequent object-oriented design and programming (OOD/OOP)Quality assurance by test-driven developmentHosted and managed on GitHubAgile CI/CD approach with automated test and deploymentClean code paradigmaCiting MLPro-MPPShttps://doi.org/10.1109/ONCON60463.2023.10431280https://doi.org/10.1016/j.simpa.2023.100509Project and TeamProject MLPro-MPPS was started in 2022 by theGroup for Automation Technology and Learning Systems at the South Westphalia University of Applied Sciences, Germany.MLPro is currently designed and developed bySteve Yuwono,Marlon Löppenberg, and furthercontributors.How to contributeIf you want to contribute, please readCONTRIBUTING.md