Control loop performance assessment with closed-loop subspace identification

This thesis is concerned with subspace identification and its applications for controller performance assessment and process modeling from closed-loop data.;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.;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.