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

Abstract

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

Date of Award	2015 Jul 23
Original language	English
Supervisor	Jason Sheng-Hon Tsai (Supervisor)