用机器学习优化凸轮轮廓，让运动更平滑且省电。
Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers
- 用梯度下降在TensorFlow中联合优化多项式分段拟合与能量
- 实验显示可同时降低能耗并保持高精度逼近
- 适合做机械系统设计的工程师或研究运动优化的人
本工作将机器学习优化的分段多项式逼近扩展至包含能量优化的目标。传统解析解虽能保证连续性与逼近精度，但难以灵活处理复杂优化目标。通过在TensorFlow中引入现代梯度下降优化器，我们构建了一个框架，最小化凸轮轮廓的弹性应变能，从而实现更平滑的运动。实验结果验证了该方法的有效性，表明其可在逼近质量与能量消耗之间实现帕累托最优权衡。
原文摘要 · Abstract (English)
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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