Efficient MPC algorithms based on fuzzy wiener models and advanced methods of prediction generation

Authors:
Piotr M. Marusak
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warszawa, Poland
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

Citing 7
Cited 0

Model Predictive Control in the Process Industry

Model Predictive Control in the Process Industry
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)

Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors

Applied Soft Computing
Effective Dual-Mode Fuzzy DMC Algorithms with On-Line Quadratic Optimization and Guaranteed Stability

International Journal of Applied Mathematics and Computer Science
Efficient model predictive control algorithm with fuzzy approximations of nonlinear models

ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Application of fuzzy Wiener models in efficient MPC algorithms

RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Fuzzy Modeling and Control

Fuzzy Modeling and Control

Quantified Score

Hi-index	0.00

Visualization

Abstract

Efficient Model Predictive Control (MPC) algorithms based on fuzzy Wiener models with advanced methods of prediction are proposed in the paper. The methods of prediction use values of future control changes which were derived by the MPC algorithm in the last iteration. Such an approach results in excellent control performance offered by the proposed algorithms. Moreover, they are formulated as numerically efficient quadratic optimization problems. Advantages of the proposed fuzzy MPC algorithms are demonstrated in the control systems of a nonlinear plant.