论文标题
通过神经近似器在机械系统中发现有效的周期性行为
Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators
论文作者
论文摘要
众所周知，保守的机械系统由于其弹性和引力电位而表现出局部振荡行为，这将这些周期性动作与系统的惯性特性完全表征。这些周期性行为及其几何表征的分类是在正在进行的世俗辩论中，最近导致了所谓的特征曼植物理论。本特曼福尔德将非线性振荡表征为线性特征空间的概括。凭借有效执行周期性任务的动机，我们使用该理论的工具来构建一个优化问题，旨在通过州反馈法来诱导所需的闭环振荡。我们通过涉及神经网络的梯度散发方法解决了构建的优化问题。广泛的模拟显示了该方法的有效性。
It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.
