Robust self-tuning predictive control for industrial applications

The recently introduced self-tuning Generalized Predictive Control (GPC) algorithm based on long range prediction, has been successfully tested in a wide range of industrial control applications. The complicated nature of the GPC algorithm, however, makes it very difficult to apply to it the standard analytical robustness techniques. A novel approach, Minimax Predictive Control (MPC), is developed which is shown by simulation studies to have robustness properties superior to those of the standard GPC. The difference between MPC and GPC algorithm is that the peak of the future predicted tracking error and the incremental control spectra are penalized rather than their integral on the unit circle. Both one degree and two degree of freedom MPC algorithms are developed. The two degree of freedom design leads to mixed $H\sb2/H\sb{\infty}$ (minimax) predictive control law design. Extensions of the method to the multivariable case are also considered. The Minimax Prediction Error (MPE) identifier is introduced as the parameter estimator in the adaptive (self-tuning) version of the algorithm. The resulting self-tuning algorithm is shown to have better robustness properties than the GPC self-tuner. The constrained MPE estimation algorithm is developed to provide the long term integrity for adaptive control.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Science and Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Tse, Johnson Y.

Contributors dc:contributor

• Miller, Norman R.

• Engineering, Industrial
• Engineering, Mechanical

Statement dc:rights

• Copyright 1994 Tse, Johnson Y.

Language dc:language

eng

Identifier

AAI9512577
(UMI)AAI9512577

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/19976