Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers

• URL: http://arxiv.org/abs/2503.09329v2
• Date: Mon, 14 Apr 2025 10:53:19 GMT
• Title: Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers
• Authors: Hannes Waclawek, Stefan Huber,
• Abstract summary: We introduce a framework that minimizes elastic strain energy in cam profiles, leading to smoother motion.<n> Experimental results confirm the effectiveness of this approach, demonstrating its potential to approximation quality against energy consumption.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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