Multi-loop Internal Model Controller Design Based on a Dynamic PLS Framework

Abstract In this paper, a multi-loop internal model control (IMC) scheme in conjunction with feed-forward strategy based on the dynamic partial least squares (DyPLS) framework is proposed. Unlike the traditional methods to decouple multi-input multi-output (MIMO) systems, the DyPLS framework automatically decomposes the MIMO process into a multi-loop system in the PLS subspace in the modeling stage. The dynamic filters with identical structure are used to build the dynamic PLS model, which retains the orthogonality among the latent variables. To address the model mismatch problem, an off-line least squares method is applied to obtain a set of optimal filter parameters in each latent space. Without losing the merits of model-based control, a simple and easy-tuned IMC structure is readily carried over to the dynamic PLS control framework. In addition, by projecting the measurable disturbance into the latent subspace, a multi-loop feed-forward control is yielded to achieve better performance for disturbance rejection. Simulation results of a distillation column are used to further demonstrate this new strategy outperforms conventional control schemes in servo behavior and disturbance rejection.

[1]  Babatunde A. Ogunnaike,et al.  Multivariable controller design for linear systems having multiple time delays , 1979 .

[2]  Wen Tan,et al.  IMC design for unstable processes with time delays , 2003 .

[3]  Hyunbo Cho,et al.  Partial least square-based model predictive control for large-scale manufacturing processes , 2002 .

[4]  Thomas J. McAvoy,et al.  A DATA-BASED PROCESS MODELING APPROACH AND ITS APPLICATIONS , 1993 .

[5]  Christiane M. Jaeckle,et al.  Industrial applications of product design through the inversion of latent variable models , 2000 .

[6]  W. Harmon Ray,et al.  Dynamic PLS modelling for process control , 1993 .

[7]  Min-Sen Chiu,et al.  Decoupling internal model control for multivariable systems with multiple time delays , 2002 .

[8]  Daniel Sbarbaro,et al.  Neural Networks for Nonlinear Internal Model Control , 1991 .

[9]  Junghui Chen,et al.  Multiloop PID controller design using partial least squares decoupling structure , 2005 .

[10]  W. Harmon Ray,et al.  Chemometric methods for process monitoring and high‐performance controller design , 1992 .

[11]  N. L. Ricker The use of biased least-squares estimators for parameters in discrete-time pulse-response models , 1988 .

[12]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[13]  Junghui Chen,et al.  Applying Partial Least Squares Based Decomposition Structure to Multiloop Adaptive Proportional-Integral-Derivative Controllers in Nonlinear Processes , 2004 .

[14]  Dale E. Seborg,et al.  Nonlinear internal model control strategy for neural network models , 1992 .

[15]  John F. MacGregor,et al.  Latent variable MPC for trajectory tracking in batch processes , 2005 .

[16]  Sirish L. Shah,et al.  Constrained nonlinear MPC using hammerstein and wiener models: PLS framework , 1998 .

[17]  Babatunde A. Ogunnaike,et al.  Advanced multivariable control of a pilot‐plant distillation column , 1983 .

[18]  M. Morari,et al.  Internal model control: PID controller design , 1986 .

[19]  S. Qin Recursive PLS algorithms for adaptive data modeling , 1998 .

[20]  M. A. Henson,et al.  An internal model control strategy for nonlinear systems , 1991 .

[21]  T. McAvoy,et al.  Short cut operability analysis. 1. The relative disturbance gain , 1985 .

[22]  Carlos E. Garcia,et al.  Internal model control. A unifying review and some new results , 1982 .