Intelligent control systems

Learning, interpreting, verification

Doctoral thesis (2019)

Authors

Q. Lin Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Copyright: © 2019 Q. Lin
DOI
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https://doi.org/10.4233/uuid:7b17a968-1414-4b84-bbf3-9a0c1197e1fd
Intelligent control Hybrid automata learning Safety verification
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Published Date

2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

Automatic control is a technique about designing control devices for controlling ma- chinery processes without human intervention. However, devising controllers using conventional control theory requires first principle design on the basis of the full under- standing of the environment and the plant, which is infeasible for complex control tasks such as driving in highly uncertain traffic environment. Intelligent control offers new op- portunities about deriving the control policy of human beings by mimicking our control behaviors from demonstrations. In this thesis, we focus on intelligent control techniques from two aspects: (1) how to learn control policy from supervisors with the available demonstration data; (2) how to verify the controller learned from data will safely control the process.