Fuzzy model based predictive control and comparison with other nonlinear MBPC algorithms

Authors:
Igor Škrjanc;Sašo Blažič
Affiliations:
Faculty of electrical engineering, University of Ljubljana, Ljubljana, Slovenia;Faculty of electrical engineering, University of Ljubljana, Ljubljana, Slovenia
Venue:
MIC'06 Proceedings of the 25th IASTED international conference on Modeling, indentification, and control
Year:
2006

Citing 6
Cited 0

Fuzzy Systems as Universal Approximators

IEEE Transactions on Computers
Necessary conditions for some typical fuzzy systems as universal approximators

Automatica (Journal of IFAC)
Fuzzy Predictive Functional Control in the State Space Domain

Journal of Intelligent and Robotic Systems
Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: quadratic stabilizability, H∞ control theory, and linear matrix inequalities

IEEE Transactions on Fuzzy Systems
An approach to fuzzy control of nonlinear systems: stability and design issues

IEEE Transactions on Fuzzy Systems
Predictive functional control based on fuzzy model for heat-exchanger pilot plant

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the paper a comparison of different nonlinear model-based predictive control algorithms is presented as a case study for a continuous stirred reactor. The focus is given to the fuzzy predictive control approach which is compared to Wiener based model predictive control and nonlinear model predictive control based on optimization. It has been shown that fuzzy predictive control law which is given in analytical form gives very promising results in comparison to other two approaches which are both based on optimization. All the proposed approaches are potentially interesting in the case of batch reactors, heat-exchangers, furnaces and all the processes with strong nonlinear dynamics.