Control loop performance assessment with closed-loop subspace identification

  • Author / Creator
    Danesh Pour, Nima
  • This thesis is concerned with subspace identification and its applications for controller performance assessment and process modeling from closed-loop data.
    A joint input-output closed-loop subspace identification method is developed which provides consistent estimation of the subspace matrices and the noise covariance matrix required for the LQG benchmark curve estimation.
    Subspace LQG benchmark is also used for performance assessment of the cascade supervisory-regulatory control systems.
    Three possible scenarios for LQG control design and performance improvement are discussed for this structure. A closed-loop subspace identification method is also provided for estimation of the subspace matrices necessary for performance assessment.
    A method of direct step model estimation from closed-loop data is provided using subspace identification. The variance calculation required for this purpose can be performed using the proposed method. The variances are used for weighted averaging on the estimated Markov parameters to attenuate the noise influence on the final step response estimation.

  • Subjects / Keywords
  • Graduation date
    Fall 2009
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.