Objective Model Selection for Identifying the Human Feedforward Response in Manual Control
Objective Model Selection for Identifying the Human Feedforward Response in Manual Control
复制标题
用于识别手动控制中人类前馈响应的目标模型选择
DOI：
10.1109/tcyb.2016.2602322
复制
发表时间：
2018
影响因子：
11.8
通讯作者：
H. Bülthoff
中科院分区：
文献类型：
--
作者：
Frank M. Drop;D. Pool;M. V. van Paassen;M. Mulder;H. Bülthoff
实际的人工控制任务通常涉及可预测的目标信号和随机干扰。假设人类控制器（HC）除了使用反馈控制来抑制干扰外，还使用前馈控制策略来跟踪目标。人们对人类前馈控制知之甚少，部分原因是普通的系统识别方法难以识别HC是否以及（如果是的话）如何应用前馈策略。在本文中，提出了一种识别过程，旨在使用具有外生输入模型的线性时不变自回归来识别人类前馈响应的客观模型选择。提出了一种新的模型选择准则来决定模型的顺序（参数个数）和是否存在前馈和反馈。对于一系列典型的控制任务，通过蒙特卡罗计算机模拟表明，经典贝叶斯信息准则（BIC）导致从纯反馈模型生成的数据中选择包含前馈路径的模型：“假阳性”前馈检测。为了消除这些误报，修改后的BIC对模型复杂性进行了额外的惩罚。在进行跟踪实验之前，通过假设HC模型的计算机模拟找到适当的权重。在未来的工作中，将考虑实验中的人在环数据。通过适当的加权，该方法可以正确识别大范围控制任务中的HC动态，没有假阳性结果。
Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: “false-positive” feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.