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Intelligent control systems : Learning, interpreting, verification. / Lin, Qin.

2019. 194 p.

Research output: ThesisDissertation (TU Delft)Scientific

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@phdthesis{7b17a96814144b84bbf39a0c1197e1fd,
title = "Intelligent control systems: Learning, interpreting, verification",
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.",
keywords = "intelligent control, hybrid automata learning, safety verification",
author = "Qin Lin",
year = "2019",
doi = "10.4233/uuid:7b17a968-1414-4b84-bbf3-9a0c1197e1fd",
language = "English",
school = "Delft University of Technology",

}

RIS

TY - THES

T1 - Intelligent control systems

T2 - Learning, interpreting, verification

AU - Lin, Qin

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - intelligent control

KW - hybrid automata learning

KW - safety verification

U2 - 10.4233/uuid:7b17a968-1414-4b84-bbf3-9a0c1197e1fd

DO - 10.4233/uuid:7b17a968-1414-4b84-bbf3-9a0c1197e1fd

M3 - Dissertation (TU Delft)

ER -

ID: 56192328