论文标题
Lyapunov稳定的自适应方法，可近似于基于传感器的控制的感觉运动模型
A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control
论文作者
论文摘要
在本文中，我们提出了一个新方案，该方案仅使用反馈信号来近似机器人的未知感觉运动模型。首先提出了未校准的基于传感器的调节问题的表述，然后，我们开发了一种计算方法，该方法将模型估计问题分布在多个专门从事局部感觉运动图的自适应单元之间。与传统的估计算法不同，所提出的方法需要很少的数据来训练和限制它（可以在分析上确定所需的数据点的数量），并且具有严格的稳定性属性（满足Lyapunov稳定性的条件）。给出了数值模拟和实验结果，以验证所提出的方法。
In this article, we present a new scheme that approximates unknown sensorimotor models of robots by using feedback signals only. The formulation of the uncalibrated sensor-based regulation problem is first formulated, then, we develop a computational method that distributes the model estimation problem amongst multiple adaptive units that specialise in a local sensorimotor map. Different from traditional estimation algorithms, the proposed method requires little data to train and constrain it (the number of required data points can be analytically determined) and has rigorous stability properties (the conditions to satisfy Lyapunov stability are derived). Numerical simulations and experimental results are presented to validate the proposed method.
