Intelligent control systems
Learning, interpreting, verification
Q. Lin (TU Delft - Cyber Security)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.