Prediction And Regulation Of Operator Functional State Based On Multiple Physiological Data And Fuzzy Modeling Methods

From the technical feasibility, economy and safety point of view, mankind has realized that the implementation of fully automation with the purpose of completely replacing the human operator is becoming increasingly difficult, thus human operators will continue to persist in various kinds of system. Therefore, the study of human-machine (HM) interaction system becomes another branch of automation technology development. In safety-critical HM systems, trivial accidents often may cause huge losses. Among them, operator’s assigned task failure caused by operator functional state (OFS) breakdown is found one of the main reasons of such accidents. To prevent accidents, the adaptive automation (AA) concept was proposed. In AA systems, the OFS are evaluated and predicted. Once a high-risk OFS is detected, either the operator’s task will be reallocated or the operator will be required to do some adjustments to make the two match each other. In the realization of AA systems, building the exact model used for OFS evaluation and prediction is a key problem. After collecting and analyzing operators’physiological data, in this paper, the fuzzy modeling method based on operators’ physiological data was employed as the main paradigm to derive the OFS evaluation and prediction model. The main contributions are as follows:(1) The aCAMS software was used to simulate the multi-task environment and5subjects participated in the experiments. Physiological data and the task performance data were collected while operators were working with different tasks. The raw physiological data were preprocessed with filtering, power spectrum analysis, data smoothing and so on. After that, by using the correlation analysis technique,3EEG features were selected as the inputs of the OFS model. The operator task performance data were used to quantify the OFS and were treated as the output of the OFS model. This work prepared the dataset used in the following OFS fuzzy modeling.(2) The particle swarm optimization (PSO) algorithm was employed to estimate the OFS fuzzy model’s parameters. During this process, the PSO algorithm and the incremental PID controller were compared and their intrinsic links were analyzed. The two were combined and a new search strategy was proposed and an incremental-PID-controlled PSO (IPID-PSO) algorithm was developed. To verify the usefulness of the algorithm, the IPID-PSO was firstly applied in the optimization of7benchmark functions. The results showed for multi-modal functions, IPID-PSO performed better than other3PSO variants on final results. Then, the IPID-PSO was applied to the estimation of the OFS fuzzy model parameters. The derived fuzzy model can well evaluate the OFS.(3) The Wang-Mendel (WM) method was employed for OFS fuzzy modeling. In the WM-based fuzzy model designing, the relationship between the Gaussian membership function’s a parameter and the fuzzy model anti-noise ability was analyzed. The clustering method was employed for the domain partition. To determine the best a value, by using an hybrid Gaussian membership function, the determination of a was transferred to the determination of adjacent membership functions’intersection point membership grade8which was called the overlap value. To derive the best δ value, firstly, different8values were adopted in fuzzy models which were used for the prediction of4datasets and the prediction performances were compared. Thus the best8value was derived for each dataset with different noise level and the generalization of8selectioin was demonstrated. Then, the same comparison was executed in the OFS fuzzy modeling by using WM method with different8values and the similar conclusion was derived. Meanwhile, comparison demonstrated that, domain partition based on clustering method and the hybrid Gaussian membership performed better than the traditional even domain partition in the OFS fuzzy modeling.(4) To realize the function of the AA system which means the prevention of high-risk OFS, the "OFS Prediction" concept was used. According to this concept, the OFS dynamic predictive model was constructed and verified. Firstly, the predictive model structure was identified. The results demonstrated the WM-based1st-order input-output model can get the best performance for OFS prediction. To improve the effective prediction rate of high-risk OFSs, the multi-model strategy was used instead of the single model, and multiple WM models were built for OFS prediction. To verify the usefulness of the predictive model, an adaptive task allocation strategy was designed and based on which an adaptive HM system was simulated. Simulation results showed the OFS can be effectively regulated, the operator task performance can be significantly improved and the number of high-risk OFSs can be greatly reduced in the adaptive HM system. Thus, the safety of HM system was substantially enhanced.