Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers

Hannes Waclawek; Stefan Huber

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arXiv · 2503.09329

Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers

Hannes Waclawek·Stefan Huber

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Abstract

This work explores an extension of machine learning-optimized piecewise polynomial 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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Hannes Waclawek, Stefan Huber. 2025-03-12. Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers. https://arxiv.org/abs/2503.09329

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