Modeling and simulation is increasingly used in the design process for a wide span of applications. Rising demands and the complexity of modern products also increase the need for models and tools capable to cover areas such as virtual testing, design-space exploration or digital twins, and to provide measures of the quality of the models and the achieved results. The latter is also called credible simulation process. In an article at the International Modelica Conference 2021, we summarized the state of the art and best practice from the viewpoint of a Modelica language user, based on the experience gained in projects in which Modelica models were utilized in the design process. Furthermore, missing features and gaps in the used processes were identified. In this article, new proposals are presented to improve the quality of Modelica models, in particular by adding traceability, uncertainty, and calibration information of the parameters in a standardized way to Modelica models. Furthermore, the new open-source Modelica library Credibility is discussed together with examples to support the implementation of credible Modelica models.

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This work describes a novel hybrid reinforcement learningenhanced trajectory planning algorithm for an active debrisremoval scenario for combined control of a satellite with a7-axis robotic arm. A reinforcement learning algorithm iscombined with a correction algorithm and classicaltrajectory planning to handle the collision free approachof a chaser satellite to a target, and placing the gripperat the robots near the grasping point for use with acombined controller, which commands the satellite and itsrobotic arm simultaneously.The algorithm is verified using a complex simulationscenario study implemented in Modelica/FMI.