论文标题
自动化的MIMO运动前馈控制：通过数据驱动的梯度通过伴随实验和随机近似的有效学习
Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation
论文作者
论文摘要
参数化的前馈控制是许多成功的控制应用，并具有不同的参考。本文的目的是开发一种有效的数据驱动方法来学习MIMO系统的前馈参数。为此，使用随机梯度下降算法将成本标准最小化，其中搜索方向和步长通过系统实验确定。特别是，搜索方向是从单个实验中获得的梯度的无偏估计，而不论MIMO系统的大小如何。使用模拟示例来说明该方法，在该示例中，就收敛速度和实验成本而言，它被证明比确定性方法优越。
Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both the search direction and step size are determined through system experiments. In particular, the search direction is chosen as an unbiased estimate of the gradient which is obtained from a single experiment, regardless of the size of the MIMO system. The approach is illustrated using a simulation example, in which it is shown to be superior to a deterministic method in terms of convergence speed and thus experimental cost.
