Reducing the energy consumption is a major concern in industrial production systems. One approach is recuperating the braking energy of robot axes. Ideally, their acceleration and deceleration phases should be synchronized so that the braking energy of one axis can be reused directly to accelerate another. This requires a detailed alignment of the axes' trajectories, but also a careful design of the overall discrete control. Finding an optimal control strategy manually, however, is difficult, as also many functional and safety requirements must be considered. We therefore propose an automated methodology that consists of three parts: (1) A scenario-based language to flexibly specify the discrete production system behavior, (2) an automated procedure to synthesize optimal control strategies from such specifications, including PLC code generation, and (3) a procedure for the detailed trajectory optimization. We describe the methodology, focusing on parts (1) and (2) in this paper, and present tool support and evaluation results.
In recent years, scenario-based modeling has been proposed to help mitigate some of the underlying difficulties in modeling complex reactive systems, by allowing modelers to specify system behavior in a way that is intuitive and directly executable. This modeling approach simplifies the specification of systems that include events occurring in distinct system components. However, when these system components are physically distributed, executing the scenario-based model requires inter-component coordination that may negatively affect system performance or robustness. We describe a technique that aims to reduce the amount of joint eventselection decisions that require coordination and synchronization among distributed system components. The technique calls for replicating the entire scenario-based executable specification in each of the components, and then transforming it in a component-specific manner that induces the required differences in execution while reducing synchronization requirements. In addition to advantages in streamlining design and improving performance, our approach captures the fact that in certain "smart" distributed systems it is often required that components know what rules govern the behavior of other components. Our evaluation of the technique shows promising results.
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