Hybrid fuzzy predictive control of a batch reactor using a branch and bound and a genetic algorithm

Javier Jesús Causa Morales, Gorazd Karer, Alfredo Nuñez, Doris Saez, Igor Skrjanc, Borut Zupancic

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

The paper deals with model predictive control (MPC) of nonlinear hybrid systems with discrete inputs. It is often required to take into account the hybrid and/or nonlinear nature of real systems, therefore, a hybrid fuzzy model is used for MPC in the paper. Two approaches that are suitable for MPC of nonlinear hybrid systems with discrete inputs are compared on a batch reactor example: a branch & bound and a genetic algorithm. We have established that both algorithms are suitable for controlling such systems. The main advantages of the genetic algorithm are boundedness of computational time in one step and whole computation-e±ciency, whereas the main drawbacks are its inherent sub-optimality and the need for suitably tuned parameters. On the other hand, the branch & bound approach does not require parameter tuning and using a suitable cost function provides optimal results in considerably less time than an explicit enumeration method.

Original languageEnglish
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Volume17
Edition1 PART 1
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 6 Jul 200811 Jul 2008

Conference

Conference17th World Congress, International Federation of Automatic Control, IFAC
CountryKorea, Republic of
CitySeoul
Period6/07/0811/07/08

Keywords

  • Batch and semi-batch process control
  • Model predictive and optimization-based control
  • Nonlinear model reduction

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