Intelligent control systems: Learning, interpreting, verification

Qin Lin

Research output: ThesisDissertation (TU Delft)

326 Downloads (Pure)

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.
Original languageEnglish
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Verwer, S.E., Advisor
  • van den Berg, J., Supervisor
Award date5 Sep 2019
DOIs
Publication statusPublished - 2019

Keywords

  • intelligent control
  • hybrid automata learning
  • safety verification

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