Event-Triggered Model Predictive Control for Continuous T-S fuzzy Systems with Input Quantization

양자화 입력을 고려한 연속시간 T-S 퍼지 시스템을 위한 이벤트 트리거 모델예측제어

  • Kwon, Wookyong (Graduate Institute of Ferrous Technology (GIFT), POSTECH) ;
  • Lee, Sangmoon (Dept. of Electronic Engineering, Kyungpook National University)
  • Received : 2017.07.17
  • Accepted : 2017.07.31
  • Published : 2017.09.01


In this paper, a problem of event-triggered model predictive control is investigated for continuous-time Takagi-Sugeno (T-S) fuzzy systems with input quantization. To efficiently utilize network resources, event-trigger is employed, which transmits limited signals satisfying the condition that the measurement of errors is over the ratio of a certain level. Considering sampling and quantization, continuous Takagi-Sugeno (T-S) fuzzy systems are regarded as a sector bounded continuous-time T-S fuzzy systems with input delay. Then, a model predictive controller (MPC) based on parallel distributed compensation (PDC) is designed to optimally stabilize the closed loop systems. The proposed MPC optimize the objective function over infinite horizon, which can be easily calculated and implemented solving linear matrix inequalities (LMIs) for every event-triggered time. The validity and effectiveness are shown that the event triggered MPC can stabilize well the systems with even smaller average sampling rate and limited actuator signal guaranteeing optimal performances through the numerical example.


Supported by : National Research Foundation of Korea (NRF)


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