A New Input Constrained Tracker for an Unknown Sampled-Data System: Modified Observer-Based Model Predictive Control Approach

• Main Authors: Wei-XiangJian, 簡偉翔
• Other Authors: Jason S. H. Tsai
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/3m6fby

碩士 === 國立成功大學 === 電機工程學系 === 103 === This thesis proposes a modified observer-based model predictive control tracker for linear unknown system with a direct transmission term and input constraint. First, the observer/Kalman filter identification(OKID) is used to identify the unknown and linear system with a transmission term into the equivalent mathematical model containing a transmission term. This identified model is used for the design of the controller and observer. Besides, the prediction-based digital redesign method is utilized to obtain a relatively low-gain and implementable observer and digital tracker from the theoretically well-designed high-gain analogue observer and tracker. The proposed modified observer-based model predictive control not only reduces the control input to fit the requirement of the input constraint, but also possesses the high-gain property of controlled system.