Optimal Iterative-Learning Control with Disturbances

論文翻譯標題: 具有干擾項之最佳迭代學習演算控制
  • 魏 宏宇

學生論文: Master's Thesis


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
獎項日期2014 7月 31
監督員Jason Sheng-Hon Tsai (Supervisor)