文献类型：期刊文献

英文题名：An adaptive human-machine control system based on multiple fuzzy predictive models of operator functional state

作者：Yang, Shaozeng[1];Zhang, Jianhua[1]

机构：[1]E China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China

年份：2013

卷号：8

期号：3

起止页码：302

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20131516186523);WOS:【SCI-EXPANDED(收录号:WOS:000317942900010)】；

基金：The work reported in this paper was supported by the National Natural Science Foundation of China under Grant No. 61075070 and Key Grant No. 11232005. The authors would like to thank the anonymous reviewers for their comments and suggestions. They also would like to thank the developers of the AUTO-CAMS software which was used in our data collection experiments. This paper was completed when the first author (S. Yang) was a visiting Ph.D student, funded by the China Scholarship Council (CSC), at the Control Systems Group (headed by Prof. Dr. Ing. J. Raisch), Technical University of Berlin, whose support is sincerely appreciated.

语种：英文

外文关键词：Adaptive automation; Operator functional state; Predictive model; Wang-Mendel fuzzy model

摘要：Under the framework of adaptive Human-Machine (HM) systems, it has been proposed that human operators' task level should be dynamically adjusted according to his/her functional state. The construction of models that can reliably predict the operator functional state (OFS) becomes critical to accomplish such adjustments. However, most of the existing models that evaluate the current OFS by using operators' current physiological data are static and are of no real predictive capability. Thus, when they are used in adaptive HM systems, the resultant task allocation between operators and machines would be time-delayed. To overcome this problem, a one-step-ahead predictive model concept for OFS computation is proposed. Meanwhile, multiple fuzzy models are developed by using the Wang-Mendel method. These models are able to increase the accuracy of the OFS breakdown prediction, as well as to reduce the model training time. In addition, an adaptive task allocation strategy is designed to validate the proposed models. The results demonstrate that, compared to the conventional HM systems, a 6.7% OFS increment and a 57.1% OFS breakdown decrement can be obtained in the multiple models based adaptive HM systems. The multiple predictive models and the adaptive task allocation strategy would pave the way for future implementations of real-time adaptive HM systems. (c) 2012 Elsevier Ltd. All rights reserved.