Paper Title:
Chance Constrained Model Predictive Control Based on Set-Valued Optimal Algorithm
  Abstract

This paper adopts one kind of classical predictive control, namely model algorithmic control, to control the distillation process. Due to finite cognition degree for the distillation system and existence noise in the chemical industry, we present a chance constrained model predictive control algorithm to eliminate the effect of parameter pertubation and noise. In view of the linear impulse response model, we introduce set-valued optimal algorithm and orthogonal standardization method to transform the chance constrained model predictive control to a general model predictive control problem. For the determinate MPC, efficient quadratic programming exsits to solve such problem. Applying such controller to a distillation system, output constraint condition will be satisfied with a predefined probability.

  Info
Periodical
Chapter
Chapter 2: Manufacturing Technology and Machinery Automation
Edited by
Quanjie Gao
Pages
148-155
DOI
10.4028/www.scientific.net/AMM.127.148
Citation
Y. Cao, J. Wu, "Chance Constrained Model Predictive Control Based on Set-Valued Optimal Algorithm", Applied Mechanics and Materials, Vol. 127, pp. 148-155, 2012
Online since
October 2011
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