Model based predictive control: an extended state space approach

An extended state space (ESS) model, familiar in subspace identification theory, is used for the development of a model based predictive control algorithm for linear model structures. In the ESS model, the state vector consists of system outputs, which eliminates the need for a state estimator. A framework for model based predictive control is presented. Both general linear state space model structures and finite impulse response models fit into this framework.

[1]  W. Larimore System Identification, Reduced-Order Filtering and Modeling via Canonical Variate Analysis , 1983, 1983 American Control Conference.

[2]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

[3]  Riccardo Scattolini,et al.  Constrained receding-horizon predictive control , 1991 .

[4]  David Di Ruscio Methods for The Identification of State Space Models from Input and Output Measurements , 1994 .

[5]  Eduardo Fernandez-Camacho,et al.  Model Predictive Control in the Process Industry , 1995 .

[6]  Romeo Ortega,et al.  On generalized predictive control: Two alternative formulations , 1989, Autom..

[7]  Edoardo Mosca,et al.  Stable redesign of predictive control , 1992, Autom..

[8]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[9]  David Di Ruscio A method for the identification of state space models from input and output measurements , 1995 .

[10]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[11]  Mats Viberg,et al.  Subspace-based methods for the identification of linear time-invariant systems , 1995, Autom..

[12]  Bart De Moor,et al.  A unifying theorem for three subspace system identification algorithms , 1995, Autom..

[13]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..