Abstract
To address environmental concerns, researchers focus on reducing the energy use of industrial machinery. Position-controlled systems allow for energy savings by optimizing the position function between fixed user-defined start and endpoints, requiring only adjusted drive settings for easy and cost-effective implementation. State-of-the-art optimizers rely on complex, machine-specific models, deterring machine builders. Furthermore, mismatches between model and reality, and changes in machine behavior over time can render optimized motion profiles suboptimal in practice. This project proposes a novel solution by optimizing the motion profile online during machine operation. Moreover, a key issue is that existing algorithms often achieve only local optimum motion profiles, potentially missing up to 11.3% of energy savings compared to the global optimum. This project proposes two steps to achieve global optimum motion profiles. First, the use of a global optimization algorithm. Secondly, the motion profile's mathematical formulation, typically constrained to specific bases (e.g., polynomial, splines), can limit revealing the global optimum. This project will employ Gaussian Processes to describe motion profiles, allowing unconstrained optimization potential by not limiting the profile to a specific form. Only an online motion profile without pre-defined profile bases can result in a machine operation with an absolute minimal energy need.
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