We introduce optimal energy shaping as an enhancement of classical passivity-based control methods. A promising feature of passivity theory, alongside stability, has traditionally been claimed to be intuitive performance tuning along the execution of a given task. However, a systematic approach to adjust performance within a passive control framework has yet to be developed, as each method relies on few and problem-specific practical insights. Here, we cast the classic energy-shaping control design process in an optimal control framework; once a task-dependent performance metric is defined, an optimal solution is systematically obtained through an iterative procedure relying on neural networks and gradient-based optimization. The proposed method is validated on state-regulation tasks.

中文翻译：

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通过神经近似器优化能量整形

我们引入了最佳能量整形，以增强基于经典被动性的控制方法。传统上，被动性理论的一个有希望的特征是稳定性，以及稳定性，传统上是在执行给定任务时进行直观的性能调整。但是，由于每种方法都依赖于很少且针对特定问题的实际见识，因此尚未开发出一种在被动控制框架内调整性能的系统方法。在这里，我们将经典的能量整形控制设计过程转换为最佳控制框架。一旦定义了与任务相关的性能指标，就可以通过依赖于神经网络和基于梯度的优化的迭代过程来系统地获得最佳解决方案。该方法在状态调节任务上得到了验证。