發(fā)布時間：2018-04-21 01:38

  本文選題：操作員功能狀態(tài) + 適應(yīng)性自動化��； 參考：《華東理工大學(xué)》2014年博士論文

【摘要】：從技術(shù)可行性、經(jīng)濟(jì)性和安全性的角度出發(fā),人類已經(jīng)意識到,以徹底取代人類操作員為目的的完全自動化的實現(xiàn)正變得越來越困難,人類操作員仍將繼續(xù)長期存在于各種系統(tǒng)中。因此,對人機(jī)交互系統(tǒng)的研究成為了自動化技術(shù)發(fā)展的另一個分支。在高安全性要求的人機(jī)交互系統(tǒng)中,微小的事故往往可能造成巨大的損失。其中,由操作員功能狀態(tài)(Operator Functional State, OFS)失效造成的其所承擔(dān)的任務(wù)無法有效完成是導(dǎo)致各種事故發(fā)生的重要原因。為此,一些學(xué)者提出了適應(yīng)性自動化(Adaptive Automation, AA)的概念。在AA系統(tǒng)中,通過對OFS進(jìn)行估計和預(yù)測,一旦發(fā)現(xiàn)操作員出現(xiàn)高風(fēng)險狀態(tài),立刻對其任務(wù)負(fù)荷進(jìn)行調(diào)整或提醒操作員采取一定措施,以使操作員所承擔(dān)的任務(wù)要求與其當(dāng)前的狀態(tài)兩者相匹配。在AA系統(tǒng)的構(gòu)建中,建立可以對OFS進(jìn)行精確估計和預(yù)測的模型是一個關(guān)鍵問題。本文通過對操作員電生理數(shù)據(jù)進(jìn)行采集和分析,使用模糊建模方法建立了基于操作員電生理數(shù)據(jù)的OFS估計和預(yù)測模型,完成了如下的研究工作： (1)使用aCAMS (automation-enhanced Cabin Air Management System)軟件對5名操作員被試進(jìn)行了多任務(wù)負(fù)荷仿真實驗,并采集了被試在不同任務(wù)負(fù)荷下的電生理數(shù)據(jù)及其任務(wù)性能數(shù)據(jù)。對電生理數(shù)據(jù)進(jìn)行濾波、功率譜分析、數(shù)據(jù)平滑等預(yù)處理,通過相關(guān)性分析,得到了3個EEG特征作為OFS模型的輸入,使用被試的任務(wù)性能數(shù)據(jù)作為OFS的量化指標(biāo)和模型的輸出,為后續(xù)的模糊建模工作提供了數(shù)據(jù)集； (2)使用粒子群優(yōu)化(Particle Swarm Optimization, PSO)算法對OFS模糊模型的參數(shù)進(jìn)行估計。在該過程中,比較了PSO算法與增量型PID控制器的內(nèi)在聯(lián)系,將二者結(jié)合提出了一種新的搜索方式,開發(fā)了一種增量型PID控制的PSO算法(IPID-PSO)。為了檢驗該算法的有效性,首先在7個基準(zhǔn)函數(shù)的優(yōu)化問題中進(jìn)行了測試,發(fā)現(xiàn)對于多峰函數(shù),IPID-PSO算法在優(yōu)化效果上有優(yōu)于其它3種PSO算法的表現(xiàn)。接著,將IPID-PSO算法應(yīng)用于OFS模糊模型的參數(shù)估計中,所得的模糊模型實現(xiàn)了對OFS的良好估計； (3)使用Wang-Mendel (WM)方法進(jìn)行OFS模糊建模。在基于WM方法進(jìn)行模糊模型設(shè)計時,分析了高斯隸屬函數(shù)的寬度參數(shù)σ對模糊模型抗噪聲能力的影響。為了確定最優(yōu)的σ值,在使用聚類法進(jìn)行論域劃分時,設(shè)計了一種混合高斯隸屬函數(shù),將對σ的確定轉(zhuǎn)化為對相鄰隸屬函數(shù)重疊度δ值的確定。為了得到最優(yōu)的δ值,首先比較了使用不同δ值的模糊模型在4個數(shù)據(jù)集預(yù)測中的性能,得到了適用于不同含噪水平數(shù)據(jù)的最優(yōu)δ值,說明了進(jìn)行最優(yōu)δ值選取的普遍意義。接著,將同樣的比較應(yīng)用于OFS模糊建模中,取得了類似的結(jié)論,并實現(xiàn)了對OFS的良好估計。同時,比較結(jié)果顯示,使用聚類方法加混合高斯隸屬函數(shù)的論域劃分形式在OFS模糊建模中表現(xiàn)出了優(yōu)于傳統(tǒng)均勻論域劃分形式的性能； (4)為了實現(xiàn)AA系統(tǒng)的功能,即對操作員高風(fēng)險狀態(tài)的預(yù)防,使用了OFS預(yù)測的概念,據(jù)此建立了OFS動態(tài)預(yù)測模型,并進(jìn)行了仿真驗證。對OFS預(yù)測模型的結(jié)構(gòu)進(jìn)行了估計,結(jié)果顯示,采用WM方法的一階模糊模型可以獲得最優(yōu)性能。為了提高對高風(fēng)險OFS的有效預(yù)測率,用多模型策略代替了單模型策略,并建立了多個WM模型用于OFS預(yù)測。為了驗證該預(yù)測模型的有效性,設(shè)計了一種自適應(yīng)任務(wù)分配策略,對基于該自適應(yīng)任務(wù)分配策略的人機(jī)交互控制系統(tǒng)進(jìn)行了仿真。仿真結(jié)果顯示,在該人機(jī)交互系統(tǒng)中,OFS得到了有效的調(diào)節(jié),操作員的任務(wù)性能水平得到了顯著改善,同時,操作員高風(fēng)險狀態(tài)出現(xiàn)的次數(shù)大大減少,從而大幅度提高了人機(jī)系統(tǒng)的安全性。
[Abstract]:From the perspective of technical feasibility, economy and security, human beings have realized that the realization of complete automation for the purpose of completely replacing human operators is becoming more and more difficult, and human operators will continue to exist in various systems for a long time. Therefore, the research of human-computer interaction system has become the development of automation technology. Another branch. In high security man-machine interaction systems, small accidents can often cause huge losses. Among them, the failure of the operator's functional state (Operator Functional State, OFS) failure to be effectively completed is an important cause of the occurrence of various accidents. The concept of Adaptive Automation (AA). In the AA system, by estimating and predicting the OFS, once the operator finds a high risk state, it immediately adjusts its task load or reminds the operator to take certain measures to match the operator's task requirements with the current state. In AA In the construction of the system, it is a key problem to establish a model that can accurately estimate and predict the OFS. By collecting and analyzing the operator's electrophysiological data, a OFS estimation and prediction model based on the operator's electrophysiological data is established by using the fuzzy modeling method, and the following research work is completed.
(1) using aCAMS (automation-enhanced Cabin Air Management System) software to carry out a multi task load simulation experiment on 5 operators, and collect the electrophysiological data and the task performance data of the subjects under different task loads. The electrophysiological data are filtered, power spectrum analysis, data smoothing and other preprocessing, through correlation. In sex analysis, 3 EEG features are obtained as the input of the OFS model. Using the task performance data of the subjects as the quantization index of OFS and the output of the model, the data set is provided for the following fuzzy modeling work.
(2) the Particle Swarm Optimization (PSO) algorithm is used to estimate the parameters of the OFS fuzzy model. In this process, the internal relation between the PSO algorithm and the incremental PID controller is compared. A new search method is put forward by combining the two parties, and a PSO algorithm (IPID-PSO) for incremental PID control is developed. The effectiveness of the algorithm is tested first in the optimization of 7 benchmark functions. It is found that for multi peak function, the IPID-PSO algorithm is superior to the other 3 PSO algorithms in the optimization effect. Then, the IPID-PSO algorithm is applied to the parameter estimation of the OFS fuzzy model, and the fuzzy model has achieved a good estimation of the OFS.
(3) using the Wang-Mendel (WM) method to make OFS fuzzy modeling. In the design of the fuzzy model based on the WM method, the influence of the width parameter sigma of the Gauss membership function on the anti noise ability of the fuzzy model is analyzed. In order to determine the optimal value of the sigma, a hybrid Gauss membership function is designed when the clustering method is used to divide the domain. In order to obtain the optimal delta value, the performance of the fuzzy model using different delta values in the prediction of 4 data sets is compared. The optimal delta value suitable for different noise level data is obtained, and the universal significance for the optimum selection of the delta value is explained. Then, the same ratio will be compared. Compared with the OFS fuzzy modeling, a similar conclusion is obtained and a good estimation of OFS is achieved. At the same time, the comparison results show that the clustering method and the domain division of the mixed Gauss membership function are better than the traditional uniform domain classification in the OFS fuzzy modeling.
(4) in order to realize the function of the AA system, that is to prevent the high risk state of the operator and use the concept of OFS prediction, the OFS dynamic prediction model is established, and the simulation verification is carried out. The structure of the OFS prediction model is estimated. The result shows that the first order fuzzy model of the WM method can obtain the optimal performance. The effective prediction rate of risk OFS is replaced by a multi model strategy and multiple WM models are used for OFS prediction. In order to verify the effectiveness of the prediction model, an adaptive task allocation strategy is designed, and the simulation of the man-machine cross control system based on the adaptive task allocation strategy is simulated. The simulation results show that In the human-computer interaction system, the OFS has been effectively adjusted. The performance level of the operator has been greatly improved. At the same time, the number of high risk states of the operator is greatly reduced, which greatly improves the security of the man-machine system.

【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：X912.9;TP18

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