復(fù)雜軟件系統(tǒng)的開發(fā)中,為節(jié)省開發(fā)成本,開發(fā)者常常會復(fù)用一些已有的系統(tǒng)組件或者第三方提供的構(gòu)件。這些組件大都是黑盒系統(tǒng)。對于使用者來說,他們通常并不知道這些被復(fù)用組件的邏輯模型和源代碼。在軟件工程實踐中,很多系統(tǒng)需求描述均使用自然語言或半形式化語言。開發(fā)者想得到這些需求的正確形式化模型十分困難。本論文考慮系統(tǒng)的形式化模型未知或者系統(tǒng)需求的形式化模型未知或者這兩種模型均未知等情況,將離散事件系統(tǒng)的監(jiān)督控制理論和自動機(jī)學(xué)習(xí)算法結(jié)合起來,為系統(tǒng)設(shè)計控制器,并用它對實際系統(tǒng)實施在線監(jiān)督控制,一定程度上使被復(fù)用的組件在不更改源代碼的情況下滿足系統(tǒng)需求。本論文主要貢獻(xiàn)如下。當(dāng)系統(tǒng)需求的形式化模型未知但受控對象的形式化模型易于獲得時,本文通過對經(jīng)典的L^*學(xué)習(xí)算法進(jìn)行擴(kuò)展,結(jié)合離散事件系統(tǒng)的監(jiān)督控制理論,提出了一種可為受控系統(tǒng)生成非阻塞的且具有最大可允許行為的控制器的方法。該方法分為兩個步驟。首先,利用學(xué)習(xí)算法學(xué)習(xí)得到一個臨時正確的控制器。其次,若學(xué)習(xí)到的控制器是非阻塞的,那么該控制器就是我們想要得到的具有最大可允許行為的非阻塞控制器。否則,該方法將這個阻塞控制器看作一個新的受控系... 

【文章來源】：西安電子科技大學(xué)陜西省 211工程院校 教育部直屬院校

【文章頁數(shù)】：124 頁

【學(xué)位級別】：博士

【文章目錄】：
摘要
ABSTRACT
List of Symbols
List of Abbreviations
Chapter1 Background and Motivation
    1.1 Supervisory Control Theory
    1.2 Automaton Learning Algorithm
    1.3 Integration of SCT and Learning Algorithms
    1.4 Organization and Contributions
        1.4.1 Organization
        1.4.2 Contributions
Chapter2 Preliminaries
    2.1 Automaton.
    2.2 Supervisory Control Theory
    2.3 Learning-based Testing and LBTest
    2.4 L*Learning Algorithm
        2.4.1 Brief Description about the L*Learning Algorithm
        2.4.2 A Small Example.
Chapter3 Supervisory Control without Formal Models of Requirements
    3.1 Supervisor Synthesis Approach
        3.1.1 The Proposed Learning Approach.
        3.1.2 Computing Supervisors Using SCT
        3.1.3 Simplifications of Requirement Membership Queries
        3.1.4 Illustrative Examples
    3.2 Experimental Studies
        3.2.1 A Manufacturing System Using AGVs
        3.2.2 Small Factory
    3.3 Conclusions and Discussion
Chapter4 Supervisory Control without Formal Models of Systems
    4.1 The Integration of LBT and SCT
        4.1.1 Integration Framework
        4.1.2 Example:A Simplified Cruise Controller
    4.2 Experimental Studies
        4.2.1 Testing Performing
        4.2.2 Supervisory Control of the System
        4.2.3 Confirmation of the Experiment Results by Simulation for the BBW
        4.2.4 Scalability Study.
    4.3 Conclusions and Discussion
Chapter5 Supervisory Control without Formal Models of Systems and Requirements
    5.1 Learning and Control Approach
        5.1.1 Testing the System.
        5.1.2 System Abstraction
        5.1.3 Learning Moore Automaton
        5.1.4 Computing Supervisors
        5.1.5 Supervisory Control of the System
    5.2 Experimental Studies
        5.2.1 BBW System
        5.2.2 Adaptive Cruise Control in A Platooning Program
    5.3 Conclusions
Chapter6 Conclusions and Future Works
    6.1 Conclusions
    6.2 Future Works
Reference
Acknowledgement
Biography



本文編號：3323949