Optimal Iterative-Learning Control with Disturbances

  • 魏 宏宇

Student thesis: Master's Thesis

Abstract

In this thesis optimal error compensation iterative learning control (OECILC) has been implemented to deal with steady state error from a known or an unknown system with disturbances First consider a known system with disturbances Sometimes it is not easy to obtain the states of system so we design a prediction-based digital observer to estimate the states from the system and then apply OECILC to deal with this problem Furthermore consider there are lots of unknown systems in real world and the information is hard to obtain then Observer/Kalman filter identification (OKID) is applied to deal with this problem Next we use digital redesign to design the controller and use high ratio of Q to R to promote the performance To be worth mentioning after OKID we use lower degree system model from OKID to control higher degree real system with OECILC In addition error prediction concept is applied to enhance OECILC and make the performance better Final the fault tolerance of OECILC is discussed
Date of Award2014 Jul 31
Original languageEnglish
SupervisorJason Sheng-Hon Tsai (Supervisor)

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