Paper Info
Title
Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.
Abstract
A new fuzzy inference Petri net (FIPN) method is proposed for modeling and control of hybrid human-machine systems.A multi-model approach is developed for operator functional state (OFS) prediction using EEG data.Multiple fuzzy models are represented in a unified framework of FIPN.The simulation results verified the feasibility and effectiveness of the proposed FIPN method. Background and objectiveIn human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. MethodsPetri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. ResultsExperiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). ConclusionThe simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller.
Year
DOI
Venue
2017
10.1016/j.cmpb.2017.03.016
Computer Methods and Programs in Biomedicine
Keywords
Field
DocType
Adaptive functional allocation,Electroencephalography,Fuzzy inference petri net,Human performance,Man-machine system,Operator functional state
Computer vision,Control theory,Petri net,Evolutionary algorithm,Computer science,Fuzzy logic,Algorithm,Artificial intelligence,Operator (computer programming),Control system,Hybrid system,Membership function
Journal
Volume
Issue
ISSN
144
C
0169-2607
Citations 
PageRank 
References 
11
0.51
28
Authors
5
Name
Order
Citations
PageRank
Jianhua Zhang1704.44
Jia-Jun Xia2110.51
J. M. Garibaldi31425146.38
P. P. Groumpos424614.83
Rubin Wang514125.54