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This work explores an extension of Machine Learning (ML)–optimized Piecewise Polynomial (PP) approximation by incorporating energy optimization as an additional objective. Traditional closed-form solutions enable continuity and approximation targets but lack flexibility in accommodating complex optimization goals. By leveraging modern gradient descent optimizers within TensorFlow, we introduce a framework that minimizes elastic strain energy in cam profiles, leading to smoother motion. Experimental results confirm the effectiveness of this approach, demonstrating its potential to Pareto-efficiently trade approximation quality against energy consumption.

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Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers

  • 会议论文

  • 发表日期：

    2026年

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  • Hannes Waclawek,
  • Stefan Huber

摘要

This work explores an extension of Machine Learning (ML)–optimized Piecewise Polynomial (PP) approximation by incorporating energy optimization as an additional objective. Traditional closed-form solutions enable continuity and approximation targets but lack flexibility in accommodating complex optimization goals. By leveraging modern gradient descent optimizers within TensorFlow, we introduce a framework that minimizes elastic strain energy in cam profiles, leading to smoother motion. Experimental results confirm the effectiveness of this approach, demonstrating its potential to Pareto-efficiently trade approximation quality against energy consumption.