A new method of direct data-driven predictive controller design

In this paper, we try to find a direct path between data and predictive controller. A straightforward data-driven predictive controller for the linear multivariable systems is proposed, without identifying any representation of the system in an intermediate step. The minimal image representation is used to describe the controlled linear multivariable system instead of model or dynamical description matrix. Data-based prediction can be estimated directly from an input/output trajectory of the system and thus the computation of dynamic optimization. For the unconstrained condition, control laws can be analytically determined directly from the data Hankel matrices without model or any intermediate step to meet the given performance specifications. The proposed predictive controller is demonstrated on a multivariable system.

[1]  Akira Kojima,et al.  Reduced order model predictive control method based on decomposition of discrete-time linear systems , 2007, 2007 46th IEEE Conference on Decision and Control.

[2]  D. Laurí,et al.  PLS-based model predictive control relevant identification: PLS-PH algorithm , 2010 .

[3]  Haralambos Sarimveis,et al.  Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models , 2008, Comput. Chem. Eng..

[4]  S. Van Huffel,et al.  Exact and Approximate Modeling of Linear Systems: A Behavioral Approach , 2006 .

[5]  Jan C. Willems,et al.  From time series to linear system - Part I. Finite dimensional linear time invariant systems , 1986, Autom..

[6]  Stefan Jakubek,et al.  Reduced order optimization for model predictive control using principal control moves , 2012 .

[7]  Biao Huang,et al.  Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach , 2008 .

[8]  Patrick Dewilde,et al.  Subspace model identification Part 1. The output-error state-space model identification class of algorithms , 1992 .

[9]  Biao Huang,et al.  Data-driven Subspace Approach to Predictive Control , 2008 .

[10]  Prashant Mhaskar,et al.  Latent variable model predictive control for trajectory tracking in batch processes: Alternative modeling approaches , 2011 .

[11]  Biao Huang,et al.  Data-driven predictive control for solid oxide fuel cells , 2007 .

[12]  Jian-Xin Xu,et al.  Notes on Data-driven System Approaches: Notes on Data-driven System Approaches , 2009 .

[13]  Shaoyuan Li,et al.  Order estimation of multivariable ill-conditioned processes based on PCA method , 2012 .

[14]  J. Willems.  Paradigms and puzzles in the theory of dynamical systems , 1991 .

[15]  Bart De Moor,et al.  Algorithms for deterministic balanced subspace identification , 2005, Autom..

[16]  Joao M. C. Sousa,et al.  Model predictive control: a data-driven approach using simple fuzzy tools , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[17]  Akira Kojima,et al.  Reduced order model predictive control for constrained discrete‐time linear systems , 2012 .

[18]  Bart De Moor,et al.  Subspace Identification for Linear Systems: Theory ― Implementation ― Applications , 2011 .

[19]  S. Shah,et al.  A control-relevant identification strategy for GPC , 1992 .

[20]  Paolo Rapisarda,et al.  Data-driven simulation and control , 2008, Int. J. Control.

[21]  Zhongsheng Hou,et al.  Notes on Data-driven System Approaches , 2009 .

[22]  J. A. Rossiter,et al.  Data-driven latent-variable model-based predictive control for continuous processes , 2010 .

[23]  Bart De Moor,et al.  A note on persistency of excitation , 2005, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[24]  J. A. Rossiter,et al.  Modelling and implicit modelling for predictive control , 2001 .

[25]  Biao Huang,et al.  A data driven subspace approach to predictive controller design , 2001 .