Tracker-Design for the Unknown System with an Input-Output Feed-Through Term and Constraints: An Adaptive Mechanism for Tuning Weighting Matrices

  • 張 睿彬

Student thesis: Master's Thesis

Abstract

An observer/Kalman filter identification (OKID) method-based adaptive mechanism for tuning weighting matrices of multi-objective cost function is newly proposed in this thesis An efficient algorithm is newly derived for the new tracker-design for the unknown system with an input-output feed-through term and input/state/output constraints First the unknown linear/nonlinear system containing an input-output feed-through term is identified by the observer/Kalman filter identification (OKID) method to have the equivalent mathematical model then the controller is analyzed and designed by the equivalent mathematical model The linear analogue quadratic performance index is modified to contain the term of input state and output constraints The linear analogue quadratic performance index with input state and output constraints can be directly discretized into an equivalent discrete function so that the obtained quadratic sub-optimal digital tracker can preserve the performance of the linear analogue quadratic performance index In order to make the exceeding input state and output update quickly and accurately an OKID-based adaptive mechanism for tuning the weighting matrices is constructed Finally an OKID-based adaptive mechanism for tuning weighting matrices of the new tracker-design for the unknown system with an input-output feed-through term and input state and output constraints is proposed Examples show the usefulness of the proposed design
Date of Award2014 Aug 6
Original languageEnglish
SupervisorJason Sheng-Hon Tsai (Supervisor)

Cite this

Tracker-Design for the Unknown System with an Input-Output Feed-Through Term and Constraints: An Adaptive Mechanism for Tuning Weighting Matrices
睿彬, 張. (Author). 2014 Aug 6

Student thesis: Master's Thesis